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ai-content-collaboration

A complete, opinionated library of Claude Skills covering the full lifecycle of building, launch...

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项目 README

来源文件:README.md

抓取于 2026年8月23日
Complete Claude Skills for the full web lifecycle. Build, ship, audit, optimize.

Brand Build Skills for Claude

A complete, opinionated library of Claude Skills covering the full lifecycle of building, launching, running, and growing a brand and a website.

License: MIT PRs Welcome Skills Made for Claude

Website LinkedIn X Facebook

103 stack-agnostic skills covering brand, design, content, SEO, dev, ops, growth, and research. Includes an Ahrefs MCP-powered SEO audit suite. Use them on Next.js, WordPress, Shopify, Webflow, plain HTML, or anything else.

Featured in awesome-claude-skills under Business & Marketing.


Install in Claude Code

Add the marketplace, then install the plugin you want:

/plugin marketplace add rampstackco/claude-skills

# full catalog (103 skills)
/plugin install rampstack-skills@rampstack

# focused subsets
/plugin install rampstack-starter@rampstack
/plugin install rampstack-seo@rampstack
/plugin install rampstack-pm@rampstack

Prefer a lighter marketplace that lists only the curated subsets (no full catalog)? Add rampstackco/plugins instead and install the same three plugins from there:

/plugin marketplace add rampstackco/plugins
/plugin install rampstack-starter@rampstack

Skills load on demand: each contributes roughly its name and description until Claude needs it.


Table of contents


What are Claude Skills?

Claude Skills are reusable capability packages that teach Claude how to handle a specific kind of task with a consistent framework, vocabulary, and output format. Each skill is a folder containing a SKILL.md (instructions plus YAML metadata) and optional reference files (templates, checklists, worked examples). Claude loads a skill automatically when a user request matches the skill's description.

Skills work across Claude.ai, Claude Code, and the Anthropic API. Once you write a skill, it is portable across all three.

For the official deep dive, see Anthropic's Agent Skills documentation.


What is in this library

This is not a curated list of other people's skills. It is a single, opinionated library where every skill follows the same structure and conventions, so the skills compose cleanly across a real project lifecycle.

What you get:

  • 103 skills across 16 categories, every one with a complete SKILL.md and at least one reference file
  • 490 reference files (templates, checklists, decision matrices, worked examples)
  • Stack-agnostic. Works on any web stack. The only named-tool exception is the SEO audit suite, which assumes the Ahrefs MCP.
  • Future-proof. Principles over tools. Stable concepts over trending techniques. References to durable specs (W3C, WHATWG, Schema.org, MDN, NN/g, WCAG) over content that ages with each algorithm update.
  • Uniform structure. Every skill uses the same section order, the same tone, and the same authoring conventions. Predictable in, predictable out.
  • Composable. Skills reference each other. creative-brief points to brand-voice. incident-response points to monitoring-and-alerting. Each skill's "When NOT to use" tells you which sibling fits your adjacent work.

Highlight categories: brand strategy and identity, design systems, content production with full Tier 1 and Tier 2 coverage, full SEO suite (foundation plus Ahrefs MCP-powered audit suite), product management with experimentation and gap-closing tracks, growth tooling for interactive web tools, paid media discipline, frontend dev and accessibility, performance and QA, launch and incident ops, UX research, plus a meta-skill that teaches you to write your own.


Featured skills

Six entry-point skills, one per audience track. Run any of these standalone, or compose them with the rest of the catalog.

SkillWhat it does
creative-direction (Brand and creative)Four-axis brief (tone, aesthetic, audience, sensory ambition) that gives every downstream skill a coherent direction
experiment-design (PM, experimentation)From hypothesis to decision: sample size, duration, segment analysis, and the failure modes that produce wrong shipping calls
feature-launch-playbook (PM, gap-closing)The discipline of launching a feature well: positioning, internal alignment, customer comms, enablement, rollout, monitoring
pillar-content-architecture (Content)Hub-and-cluster topical authority: pillar selection, cluster planning, internal linking, refresh discipline
landing-page-copy (Marketing)Landing pages, sales pages, hero-to-CTA flow with copy that converts
funnel-flow-architecture (Growth tooling)Cross-tool conversion flows architected to match the audience and the funnel stage

See it in action

The creative-direction skill rendered as a live showcase →

Forty-two fictional brands generated from briefs that all use the same skill. Each is a fully styled brand site, not a mockup. The showcase demonstrates what the four-axis framework produces in practice and lets you filter by axis position to see how each combination renders.

Creative Direction skill highlight diagram. Navy header card reads 'Impactful Creative Direction' with the subtitle 'Direction for art, taste, and style'. Four quadrants below show: Framework Axes 4 (Tone, Aesthetic, Relationship, Sensory), Framework Positions 16 (each axis combines into 16 distinct positions), Example Treatments 42 (Pulse, Bloom, Forge, Observatory, and 38 others), and Possible Compositions infinity (Motion: Static, Light, Medium, High). Caption reads 'No templates, only guided outputs.'

Showcase grid of brand archetypes including Pulse, Volt, Anode, Drift, and others, with type and motion intensity filter pills above the cards.

Filter by any axis position

The skill defines four axes: tone, aesthetic, relationship, sensory. The showcase lets you filter by any combination and see which examples match. Pre-filtered URLs deep-link from the SKILL.md and axes-explained reference, so you can read about a position and click straight through to the rendered examples.

Showcase grid filtered by Tone equals Provocative and Sensory equals Resonant, showing eight matching brand cards with the axis disclosure auto-expanded.

The empty state is the lesson

The framework is generative. The showcase is illustrative. Most rare-but-powerful combinations are valid creative choices that simply have not been built yet. Set Provocative + Editorial Restrained + Coach + Resonant and the grid is empty.

Showcase grid with all four axis filters set to Provocative, Editorial Restrained, Coach, and Resonant, showing zero matching examples and the empty state copy: No example yet. The framework allows this combination, it just hasn't been built as one of the thirty worked examples.

The framework's range

Same skill, same brief format. Four completely different visual systems. Notice that Pulse and Bloom share identical axis positions yet read as opposite visual languages. The reference brands and aesthetic interpretation do the rest.

Pulse music streaming brand. Saturated gradient hero with the headline 'Sound that moves with you' and pink-to-cyan equalizer bars below.Forge boutique fitness studio. Dark industrial hero with intense typography and motivational copy.
Pulse · music streaming
Sound that moves with you.
Playful / Expressive Maximalist / Companion / Resonant
See Pulse demo example →
Forge · boutique fitness
Show up. Get hammered.
Provocative / Expressive Maximalist / Coach / Resonant
See Forge demo example →
Bloom adaptogenic soda brand. Peachy gradient hero with tri-color headline 'Soda that loves you back' and a strawberries-around-soda-can product photo.Observatory Editorial. Cream paper hero with restrained serif headline 'An observability tool for the engineers who already know what they are doing'.
Bloom · adaptogenic soda
Soda that loves you back.
Playful / Expressive Maximalist / Companion / Resonant
See Bloom demo example →
Observatory Editorial · observability tool
An open-source tool that respects engineer time.
Conversational / Editorial Restrained / Peer / Considered
See Observatory demo example →

See all the brands in the showcase →

Run this on your own brand

The creative-direction skill lives at skills/creative-direction/. Install it (see below), give Claude a project name and a few inspiration references, and the skill walks you through producing a brief that downstream skills can consume. The brand sites in the showcase were built from briefs of exactly that shape.


Logo design in action

The logo-design skill is rendered on rampstack.co as two parallel surfaces. The variant explorer goes deep on one brand at a time: a primary mark, variants across architectures, applied contexts. The taxonomy gallery goes wide across the architecture space: ten fictional marks demonstrating eight mark architectures (wordmark, lockup, monogram, letterform-as-symbol, abstract, pictorial, combination, emblem). Same skill, two different lenses.

Logo Design skill highlight diagram. Navy header card reads 'Bespoke Logo Design' with the subtitle 'Bringing brands to life.' Below the header, a simulated construction guide shows a stylised letterform B rendered against gridlines, with a Golden Ratio overlay, Primary Curve and Secondary Shape callouts, a Kerning marker, and a six-swatch color palette. Three columns at the bottom show: Verticals (Tech, Finance, Healthcare, Retail), Brand Voice (Trustworthy, Innovative, Premium, Approachable), and Architectures (Monogram, Wordmark, Emblem, Abstract).

Per-brand depth

The variant explorer →

Each brand has a primary mark plus variants across architectures and applied contexts. The logo-design skill walks through the discipline of choosing one architecture and rendering it consistently across the system the brand will actually use.

Logo design variant explorer showing six fictional brand cards in a three-by-two grid: Whitfield Carter (legal counsel lockup), Wren and Bough (consumer goods lockup), Highline (hospitality wordmark), Sentinel (tech and AI symbol-only), Lacuna (fashion wordmark), and Roost (restaurant lockup). Each card pairs a primary mark with three classification chips for architecture, typographic register, and category, plus a four-variants and five-application-contexts subtext.

The brands are filterable by architecture, typographic register, and category. The intent is reference work, not consumable templates.

Architectural taxonomy

The marks gallery →

Ten fictional marks across eight mark architectures: wordmark, lockup, monogram, letterform-as-symbol, abstract, pictorial, combination, emblem. The taxonomy makes the architectural distinctions concrete by showing all eight side-by-side, with three wordmarks at three typographic registers so the architectural label does less work than the execution.

Marks gallery showing six fictional brand cards in a three-by-two grid: knurl (lowercase serif wordmark with knurled texture), TARSUS (uppercase sans lockup with stacked-bar mark), PLINTH (classical serif inside a double-lined emblem frame), Caval (italic horse silhouette plus italic wordmark combination), Ostend (flowing OS monogram resolving to a single connected glyph), and GLINT (high-contrast Didone wordmark with hairline I crossbar). Each card carries the brand name, descriptor, and three classification chips for mark architecture, vertical, and brand voice.

Filter by architecture, vertical, or brand voice; click any mark card to read its design rationale.


Reference build in action

The full catalog rendered as a 4-phase reference build: blank brief through deployed audited launch site. Threshold is a fictional PLG onboarding analytics product, but the research, brand foundations, build, and audit findings are all real. The reference build is the catalog's single strongest demonstration of how the skills compose end-to-end.

Reference build hero card. Navy header card reads 'Reference Build' with subtitle 'A fictional B2B SaaS launch, end-to-end.' Below, four white phase cards arranged in a 2x2 grid: Phase 01 Strategy and Research with caption 'Real Ahrefs research applied to a fictional brief'; Phase 02 Brand and Design with caption 'Working brand system with live tokens and components'; Phase 03 Build and Ship with caption 'Deployed launch microsite at /demo/threshold'; Phase 04 Audit and Optimize with caption 'Real audit findings with applied fixes.' Footer caption reads 'Threshold is fictional. The methodology is not.'

The four phases

Phase 1: Strategy and research → Real Ahrefs keyword research, competitor analysis, content gap audit, and backlink opportunity mapping applied to a fictional B2B SaaS brief. Live data tables sourced from the Ahrefs API.

Phase 2: Brand and design → Logo system, color and typography tokens, working brand component primitives. The brand system renders live on the walkthrough page in real fonts and tokens, not just described.

Phase 3: Build and ship → The actual launch microsite built with Next.js using Phase 2's brand foundations. Live at rampstack.co/demo/threshold. Persistent demonstration banner; noindex; local-only waitlist form.

Phase 4: Audit and optimize → Real audit on the deployed site using the catalog's audit suite (axe-core, Lighthouse, manual checks). Real findings with severity, real fixes applied, real before/after metrics. The closing chapter where the catalog audits its own output.

The deployed result

The four phases compose into a working microsite at rampstack.co/demo/threshold. Real Next.js code, real brand foundations from Phase 2 referenced via cross-route imports, real working multi-step waitlist form (no data stored), persistent demonstration banner, and the inline data visualizations that came out of the post-audit polish pass.

Full-page screenshot of the deployed Threshold demo at rampstack.co/demo/threshold. Persistent navy demonstration banner at top reads 'Demonstration · Threshold is a fictional product built to illustrate how the catalog composes from blank brief to deployed launch microsite.' Below, the hero section shows a serif headline 'Know how new users actually get to value' next to a stylized product dashboard mockup with KPI tiles, an activation funnel chart, and recent cohorts comparison. Further down the page: a fictional cohort trust strip, a 'The gap' problem section, a wedge section with inline funnel and time-to-first-value charts, a comparison table against Mixpanel/Amplitude/Heap and Pendo/Userpilot, a 'How it works' section with three connected cards, a multi-step waitlist form, and a FAQ section.

Why a fictional product

Real client work cannot be open-sourced; portfolio claims trigger conflict-of-interest concerns in interviews and consulting conversations. A fictional product with a documented brief plus real research, real brand foundations, real working code, and real audit findings produces a teaching artifact that demonstrates methodology without claiming relationships. Threshold is a measurement tool that does not exist; the methodology that built it is the catalog working end-to-end.


Getting started

Skills install in three different places depending on where you use Claude. Pick the platform that matches your workflow.

Option 1: Claude.ai (web and desktop)

If your Claude.ai plan supports custom Skills:

  1. Go to Settings → Capabilities → Skills.
  2. Upload the skill folder you want as a .zip (one zip per skill folder containing SKILL.md and the references/ subfolder).
  3. Enable the skill in the chat interface.

Claude will load the skill automatically when your request matches its description.

For current plan availability and the exact upload UI, see Anthropic's Skills user guide.

Option 2: Claude Code (recommended)

Skills are first-class citizens in Claude Code. Drop them into your skills directory and Claude Code picks them up automatically.

User-level skills (available in every project):

# macOS / Linux
mkdir -p ~/.claude/skills
cp -r skills/* ~/.claude/skills/

# Windows (PowerShell)
New-Item -ItemType Directory -Force -Path "$HOME\.claude\skills"
Copy-Item -Recurse skills\* "$HOME\.claude\skills\"

Project-level skills (available only in a specific project):

mkdir -p .claude/skills
cp -r path/to/this-repo/skills/* .claude/skills/

Start (or restart) Claude Code. Skills load automatically.

For exact current paths and config flags, see the Claude Code documentation.

Option 3: Anthropic API

Use Skills programmatically by referencing them in your API calls. Skills must first be uploaded to your workspace (via the Console or API), then referenced by ID when creating messages.

For the current API surface, request format, and limits, see the Agent Skills API documentation.

Want only a few skills?

You do not have to install all 103. Pick the categories that match your work. The library is modular: each skill stands on its own.


Quick example

Once installed, skills trigger automatically based on your request. You do not have to name the skill or change how you talk to Claude.

You ask:

"Our organic traffic dropped 30% last week. Help me figure out why."

What happens:

Claude recognizes the request matches seo-traffic-diagnosis, loads the skill, and walks through its 5-layer root cause framework: confirm the change is real → localize the change → page-level analysis → technical analysis → external analysis. By the end, you have a hypothesis statement, evidence, and an action plan, structured the same way every time.

Other natural triggers:

  • "Help me write a creative brief" → creative-brief
  • "Audit my homepage for SEO" → seo-onpage
  • "We need a backlink audit" → seo-backlink-audit
  • "Plan our content roadmap for Q3" → seo-content-gap-audit plus content-strategy
  • "Postmortem template for last night's incident" → after-action-report
  • "How do I write my own skill?" → skill-creation-walkthrough

You can also call a skill explicitly: "Use the seo-audit-orchestration skill to run a full audit on example.com."


How they compose

The skills compose into a full project flow:

brand-discovery → brand-ideation → brand-identity → brand-style-guide → brand-voice
                                                                        ↓
creative-brief → information-architecture → content-strategy → design-system
                                                              ↓
seo-keyword → seo-content-audit → content-and-copy → landing-page-copy
                                                    ↓
seo-onpage → seo-technical → seo-aeo-geo → seo-offpage → seo-competitor
                                          ↓
frontend-component-build → accessibility-audit → performance-optimization
                                                ↓
code-review-web → qa-testing → security-baseline → launch-runbook
                                                  ↓
domain-strategy → monitoring-and-alerting → backup-and-disaster-recovery
                                          ↓
incident-response → after-action-report
                  ↓
analytics-strategy → cro-optimization → ux-research → usability-testing → journey-mapping

The SEO audit suite (Ahrefs MCP-powered) wraps around the SEO foundation skills:

seo-audit-orchestration
  ├── seo-site-health-audit
  ├── seo-backlink-audit
  ├── seo-keyword-gap-audit
  ├── seo-content-gap-audit
  ├── seo-traffic-diagnosis  (also runs standalone for incident-style work)
  └── seo-rank-tracking      (ongoing, feeds the others)

The catalog also includes four audience tracks that compose alongside the foundational lifecycle. Each track has its own internal flow:

Paid media (Marketing track):

paid-media-strategy → ads-creative-development → ads-performance-analytics

Pairs with the paid media platforms in the integrations catalog at rampstack.co (Google Ads, Meta, LinkedIn, TikTok, plus Synter as the multi-platform aggregator).

Growth tooling (interactive web tools):

funnel-flow-architecture (orchestrator)
  ├── lead-magnet-design          (capture)
  ├── calculator-design           (capture / activate)
  ├── quiz-and-assessment-design  (capture / activate)
  ├── multi-step-form-design      (activate)
  ├── chatbot-flow-design         (activate)
  ├── onboarding-wizard-design    (activate)
  ├── interactive-product-tour    (activate / convert)
  ├── upgrade-flow-design         (convert)
  ├── scheduler-and-booking-design (convert)
  ├── comparison-tool-design      (convert)
  └── product-configurator-design (convert)

funnel-flow-architecture is the orchestrator: it sequences which interactive tool fits each audience and funnel stage, distinguishing matched-funnels from kitchen-sink-funnels.

Tier 2 content lifecycle:

content-strategy → pillar-content-architecture → content-brief-authoring
                                                ↓
              content-and-copy / long-form-content-frameworks / email-sequences
                                                ↓
                       editorial-qa → content-distribution → programmatic-seo
                                                ↓
              content-refresh-system → content-repurposing → content-migration

ai-content-collaboration is a workflow layer that runs across every phase rather than a single step. documentation-strategy operates continuously alongside the rest.

Tier 2 product management (two parallel tracks):

Experimentation track:
experiment-design → feature-flagging → experimentation-platform-orchestrator
                                     ↓
                         experimentation-analytics → data-warehouse-experimentation

Gap-closing track:
pm-spec-writing → roadmap-planning → feature-launch-playbook
                                   ↓
       beta-program-management → product-analytics-setup → integration-orchestrator

The experimentation track ships changes with statistical discipline; the gap-closing track ships features with operational discipline. Both compose with the foundational lifecycle above.

Operations, cross-cutting, and team skills (stakeholder-communication, documentation-strategy, vendor-evaluation, team-onboarding-playbook, dependency-management, cost-optimization, etc.) cut across every track.

You can also pull individual skills for one-off work. Need just a backlink audit? Use seo-backlink-audit. Need to write a creative brief? Use creative-brief. Each skill stands on its own.


How the catalog connects

The skills compose with the tools your team already uses. 103 skills at the center; 35 integrations across 6 integration categories radiating out via MCPs.

RampStack architecture diagram. A central navy hub card shows the RampStack mark with the subtitle 'Stack-agnostic methodology'. Six category cards radiate out: Workflow with 6 integrations (Jira, Linear, Notion, Figma, GitHub), Experimentation with 11 integrations (Statsig, PostHog, Optimizely, Amplitude), SEO Intelligence with 3 integrations (Ahrefs, Semrush, Similarweb), Paid Media with 5 integrations (Google Ads, Meta Ads, LinkedIn, TikTok, Synter), Content and SEO with 5 integrations (Webflow, Contentful, Frase, Profound, AirOps), and Data and Analytics with 5 integrations (BigQuery, Snowflake, Mixpanel, dbt, Hex).


Surfaces

This catalog is the open-source methodology layer. Commercial surfaces at rampstack.co extend it:

  • Skills directory. Every skill on a curated landing surface with audience tracks, search, and category navigation.
  • Walkthroughs. Multi-skill recipes that orchestrate skill clusters end-to-end. Use these when one skill is not enough and a packaged sequence is.
  • Integrations directory. Curated MCPs, APIs, and tooling that the skills hook into.
  • Showcase. Real brand sites built from these skills, with the brief that produced each one.

The skills in this repository remain free, open-source, and stack-agnostic. The surfaces above are how the same methodology is delivered as a product.


Design principles

claude-skills follows the Agent Skills Specification, the open standard for portable AI agent skills originally developed by Anthropic and adopted across the AI tooling ecosystem (Claude Code, OpenAI Codex, Gemini CLI, GitHub Copilot, Cursor, VS Code, Goose, Spring AI, and 30+ other platforms as of early 2026).

Beyond the format itself, the catalog is designed around three principles aligned with the guidance Anthropic publishes in Building effective agents:

Simplicity. Each skill covers one focused capability rather than trying to be a multi-purpose document. A roadmap-planning skill plans roadmaps. A keyword-research skill researches keywords. Composing them together produces complex workflows; mixing them inside one skill produces unreliable ones.

Transparency. Every skill declares its scope, dependencies, and expected behavior in machine-readable YAML frontmatter. The catalog is inspectable by tooling, not just by humans reading prose.

Quality contracts via tooling. Structural and content quality is enforced through automated checks (run python .github/scripts/lint_skills.py) rather than convention alone. Every skill is validated against a schema. Every catalog change is validated in CI.

Skills in this catalog are designed to compose into the common agentic workflow patterns Anthropic documents: prompt chaining (sequential steps), routing (classify and direct), parallelization (sectioning or voting), orchestrator-workers (dynamic delegation), and evaluator-optimizer (iterative refinement).

Because the catalog conforms to the open Agent Skills standard, skills work across any platform supporting the specification without modification.

Family repos

claude-skills is the parent catalog. Curated subsets and companion repos focus on specific specialties:

RepoFocusSkills
claude-skillsFull catalog (you are here)103
claude-skills-starterGeneral-purpose lite14
claude-skills-seoSEO consulting12
claude-skills-pmProduct management12
claude-skills-widgetsUI patterns + components65 + 32
awesome-claude-skillsCurated discovery listn/a

Each family repo is MIT-licensed, conforms to the Agent Skills Specification, and is stack-agnostic. Use the full catalog for breadth; use a specialty subset when working in one domain.

How this org fits together

The table above covers the skill catalogs. They are one part of a larger set, and the rest of it is below. All of it is public.

Skills. This repo is the canonical home for all skill content. Alongside it sits the workflows tier: fifteen multi-skill runbooks with their connectors, a getting-started guide, and published run records for the ones that have been executed as written.

Subsets. The five curated repos in the table above copy from this catalog with attribution and track it upstream.

Design direction themes. Thirteen sibling repos, each shipping annotated design tokens with their measured contrast ratios, a component layer, two Tailwind adapters, and a demo that opens from a file with nothing installed. They come in three artifact classes: seven surface registers, one layout archetype, and five shells, which ship a structure a site lives inside (a window manager or a board, a taskbar or a dock, an enhancement contract and a focus model) with the register they wear left swappable. VivaOcean, the animated-scene shell, is the showcase flagship. What the shell class settled, and why, is public in its class decision log, which every new shell reads first and continues. All thirteen are linked from the gallery at rampstack.co/themes.

Creative direction. The themes are not thirteen moods. Each one states its coordinates in the creative direction framework, which sets brand direction on four axes, and the showcase renders archetypes at each position on it.

Engines. Krine, Tholo, and Basano run on one runtime: Krine decides, Tholo builds, Basano proves. The engines page covers what the three share.

Research. The SERP event registry is a dated, sourced, confidence-tagged record of AI model releases, search feature changes, and confirmed algorithm updates, rendered on the site from the repository that holds it.

What shipped, and when, is recorded at rampstack.co/updates.


The 103-skill catalog

All 103 skills are shipped. Each has a complete SKILL.md plus at least one reference file (template, checklist, or playbook).

Strategy and discovery (5)

SkillWhat it does
brand-discoveryAudience research, competitive scan, positioning territory exploration
creative-briefProject briefs that align stakeholders before work starts
creative-directionFour-axis aesthetic brief (tone, aesthetic, audience, sensory ambition) for cross-skill coherence
information-architectureSitemap, navigation, URL structure, content types, taxonomy
content-strategyEditorial strategy, content calendar, topical authority planning

Brand (7)

SkillWhat it does
brand-ideationNaming, positioning territories, mood directions, narrative angles
brand-identityLogo system, color, typography, imagery, iconography, motion
brand-style-guideThe canonical reference document for the full brand system
brand-voiceVoice attributes, tone shifts, vocabulary, paired-example library
brand-archetype-system12 archetype defaults across 18 verticals: color, type, voice, imagery starters
logo-designLogo variants across architectures (wordmark, lockup, monogram, letterform-as-symbol), with rationale and application specs
creative-brief-selectorLive-reference-grounded creative briefs with divergence check against prior builds

Design (4)

SkillWhat it does
design-systemComponent library, design tokens, design system documentation
design-standardsProduction-grade page and component design standards
art-directionPhotography, illustration, and visual direction for campaigns
vertical-site-conventionsVertical page and site composition built to the experience bar

Content (13)

SkillWhat it does
pillar-content-architectureHub-level content architecture: pillar topic selection, cluster planning, internal linking, URL structure, pillar and cluster page anatomy, topical authority signals, refresh discipline
content-brief-authoringPer-piece editorial brief: target keyword, intent, audience, outline, entity coverage, internal linking, success criteria, and the discipline that distinguishes useful briefs from bloat
content-and-copyWebsite copy, blog content, content production frameworks
landing-page-copyLanding pages, sales pages, hero-to-CTA flow
email-sequencesOnboarding flows, lifecycle campaigns, transactional copy
programmatic-seoDesigning pSEO programs that work: data sources, template design, quality control at scale, internal linking, crawl budget, AEO/GEO patterns, refresh discipline, and when pSEO is and is not the right answer
editorial-qaPre-publish QA framework: brief adherence, voice consistency, fact accuracy, AI-content audit, AEO/SEO compliance, sampling at scale, and the workflow that distinguishes catch-problems QA from process theater
ai-content-collaborationHow humans and AI compose in content workflows: participation boundaries, hybrid patterns, voice ownership, the AI slop problem, disclosure and transparency, team calibration, and the ethics of honest AI-assisted production
long-form-content-frameworksStructural patterns for individual long-form pieces (case studies, whitepapers, research reports, definitive guides, manifestos, ebooks, long-form tutorials) that distinguish publication-quality work from bloggy-long padding or academic bloat
content-refresh-systemSystematic content refresh: quarterly audits, refresh prioritization, refresh-vs-merge-vs-delete decisions, the lifecycle discipline that distinguishes intentional programs from set-and-forget decay
content-repurposingCross-format content adaptation: one piece becomes many (blog series, email, social, webinar, podcast, video) with per-format adaptation rather than mass-blast that ignores medium constraints
content-distributionContent distribution discipline: owned, earned, and paid channels matched to audience and content type. Channel-fit decisions, distribution cadence, the strategic alternative to spam-everywhere or hope-and-pray
evidence-based-reviewsEvidence tiers, methodology disclosure, honest review claims

SEO foundation (7)

Tool-agnostic SEO skills. These define the conceptual frameworks. The SEO audit suite below adds the Ahrefs MCP-powered execution layer.

SkillWhat it does
seo-onpageSingle-page audits and optimization across 8 dimensions
seo-technicalCrawlability, indexability, rendering, schema, page experience
seo-keywordDiscovery, intent classification, clustering, prioritization
seo-competitorSERP overlap, content gaps, backlink gaps, technical comparison
seo-offpageLink building, digital PR, citations, linkable assets
seo-content-auditKeep/update/merge/redirect/delete decisions across a site
seo-aeo-geoAI search optimization, llms.txt, extraction-friendly content

SEO audit suite (Ahrefs MCP-powered) (7)

End-to-end SEO audit workflows that pull data from the Ahrefs MCP and produce concrete deliverables. These skills assume the Ahrefs MCP is connected.

SkillWhat it does
seo-audit-orchestrationMaster orchestrator: sequences the suite, produces a rollup report
seo-backlink-auditProfile health, anchor mix, toxic links, reclamation, gap analysis
seo-keyword-gap-auditCompetitor keyword gaps with opportunity scoring and clustering
seo-content-gap-auditMissing topics, thin coverage, outdated content, decay diagnosis
seo-traffic-diagnosisDiagnose drops, stalls, or wins via 5-layer root cause analysis
seo-site-health-auditTriage Ahrefs Site Audit findings by SEO impact, not severity
seo-rank-trackingSetup, baseline, segmentation, alerting, dashboarding

Product (13)

SkillWhat it does
pm-spec-writingPRDs, user stories, acceptance criteria, dev briefs
roadmap-planningQuarterly planning, prioritization, dependency mapping
integration-orchestratorSequence creative-direction work across phases, gates, handoffs, and QA verification
experiment-designHypothesis to decision: sample size, duration, segment analysis, interpretation, and the failure modes that produce wrong shipping calls
feature-flaggingFlags as production infrastructure: types, naming, lifecycle, targeting, rollout, stale flag cleanup, governance
experimentation-analyticsRead result panels without fooling yourself: confidence intervals, p-values, multiple testing, sequential testing, CUPED, ratio metrics, network effects, dashboard reconciliation
experimentation-platform-orchestratorPick the right experimentation platform, migrate when wrong, coordinate when multi-platform: a decision framework for Statsig, PostHog, GrowthBook, Optimizely, Amplitude, Eppo, Kameleoon
product-analytics-setupInstrument product analytics correctly: event taxonomy, properties, naming conventions, schema versioning, funnels, retention cohorts, North Star selection, and the instrumentation debt that compounds without discipline
data-warehouse-experimentationRun experiments out of the warehouse: SQL assignment, exposure logs, dbt metric definitions, statistical analysis, variance reduction with CUPED, sequential testing, and the operational tradeoffs vs platforms
feature-launch-playbookThe operational discipline of launching a feature well: positioning, internal alignment, customer comms, sales enablement, support readiness, rollout strategy, monitoring, and post-launch measurement
jtbd-framingJobs-to-be-Done framework. Job statements, struggling moments, hire/fire criteria, the difference between feature-thinking and job-thinking. Honest about where JTBD earns its keep and where it becomes performative
okr-designOKR design discipline. Outcome statements, key results, scoring, mid-quarter recalibration. Distinguishes sandbagged OKRs (always hit, useless) from aspirational fantasy (impossible, demoralizing) from stretch OKRs (genuine ambition with quarterly accountability)
beta-program-managementRunning betas that produce real signal. Participant selection, structured feedback, beta-to-GA decisions. Distinguishes soft-launch (no structure) from kitchen-sink (everyone in) from structured-beta (calibrated cohort with intentional feedback loops)

Development (4)

SkillWhat it does
code-review-webPR review, build error diagnosis, security and quality checks
frontend-component-buildComponent architecture, props design, accessibility from the start
accessibility-auditWCAG compliance audit with remediation plan
performance-optimizationCore Web Vitals, asset optimization, render performance

Quality assurance (1)

SkillWhat it does
qa-testingPre-launch QA, regression testing, cross-browser checks

Operations (9)

SkillWhat it does
launch-runbookGo-live runbook, DNS cutover, deploy day procedures
incident-responseIncident triage, comms, mitigation, escalation
after-action-reportPost-mortems, retros, learnings documentation
domain-strategyDNS architecture, redirects, registrars, multi-domain portfolios
monitoring-and-alertingSLO design, uptime checks, alert routing, on-call rotations
backup-and-disaster-recoveryRPO/RTO targets, backup strategy, restoration drills
security-baselineHTTPS, security headers, CSP, secrets management, vulnerability scans
email-deliverabilityDMARC, SPF, DKIM, sender reputation, deliverability monitoring
media-asset-managementImage pipelines, video hosting, asset libraries, format selection

Growth (2)

SkillWhat it does
analytics-strategyMeasurement frameworks, dashboard design, event taxonomy
cro-optimizationHypothesis-driven testing, conversion optimization

Growth tooling (12)

Interactive web tools that turn visitors into leads. Lead magnets, calculators, quizzes, multi-step forms, chatbots, and the cross-tool funnel architecture that orchestrates them.

SkillWhat it does
lead-magnet-designDesigning gated content that earns the email. Distinguishes thin-bait (overpromises, underdelivers) from kitchen-sink-resource (everything, helps with nothing) from earned-value-magnet (delivers standalone value while qualifying the lead)
calculator-designDesigning interactive calculators that deliver decision-support value while qualifying leads. Distinguishes vanity-calculator (no real value) from lead-trap (hides answer behind email) from transparent-decision-tool (gives genuine value, captures leads honestly)
quiz-and-assessment-designDesigning quizzes and assessments that produce actionable segmentation. Distinguishes clickbait-quiz (engagement only) from vanity-result (entertaining, not useful) from actionable-segmentation (genuine categorization that drives next-step recommendations)
multi-step-form-designDesigning multi-step forms that respect cognitive load while maintaining completion intent. Distinguishes kitchen-sink-single-page (overwhelms) from progress-theater (steps without genuine staging) from genuinely-staged (each step earns its own page)
chatbot-flow-designDesigning conversational flows for chatbots and AI agents on websites. Distinguishes scripted-bot (rigid trees, fail edge cases) from hallucinating-bot (LLM without structure, makes things up) from structured-guided-conversation (LLM-powered with intent architecture and fallback discipline)
funnel-flow-architectureArchitecting cross-tool conversion flows that match audience and stage. Distinguishes silo-funnels (every tool standalone) from kitchen-sink-funnels (every audience squeezed through one path) from matched-funnels (architecture matched to audience-and-stage)
onboarding-wizard-designDesigning first-run product onboarding wizards. Distinguishes tutorial-overload (dump everything upfront) from skip-friendly-empty (skipped onboarding leads to abandoned product) from earned-progressive-disclosure (right things at the right moments)
interactive-product-tourDesigning in-product tours and contextual help. Distinguishes tooltip-spam (every button has a tour stop) from one-and-done (tour shows once, never seen again) from contextual-when-needed (surfaces help at the moment friction occurs)
upgrade-flow-designDesigning free-to-paid conversion flows. Distinguishes paywall-everywhere (gates everything aggressively) from free-forever-trap (no upgrade path surfaces) from value-triggered-upgrade (paywall surfaces at moments of demonstrated value)
scheduler-and-booking-designDesigning schedulers and booking flows. Distinguishes any-time-friction (no qualification, just a booking link) from interrogation-gate (so much qualification it scares users off) from qualified-fast-path (just enough qualification to set up the call well)
comparison-tool-designDesigning comparison tools that help users decide. Distinguishes feature-list-dump (every feature in a row, no decision support) from hidden-recommendation (biased comparison pretending to be neutral) from honest-comparison-with-guidance (genuine comparison plus opinionated recommendation)
product-configurator-designDesigning interactive product configurators. Distinguishes infinite-options (decision paralysis from too many options) from canned-bundles-only (no real customization) from guided-configuration (smart defaults plus meaningful constraints plus escape hatches)

Marketing (3)

Paid media discipline: strategy, creative, and performance analytics. Pairs with the paid media platforms in the /integrations catalog at rampstack.co.

SkillWhat it does
paid-media-strategyHypothesis to spend: channel selection, budget allocation, audience targeting, bid strategy, attribution reality, and the failure modes that burn agency-scale budgets
ads-creative-developmentHook patterns, format selection, video pacing, variation systems, testing methodology, fatigue detection, and the platform-specific creative norms that separate ads from clutter
ads-performance-analyticsRead paid media dashboards without fooling yourself: attribution models, platform reporting quirks, ROAS vs LTV, multi-platform reconciliation, incrementality testing, and the interpretation failures that compound into wasted budget

Research (6)

SkillWhat it does
ux-researchResearch planning, user interviews, qualitative synthesis
usability-testingTest design, moderation, findings reports
journey-mappingCustomer journey maps, service blueprints, friction analysis
discovery-research-synthesisSynthesizing customer interviews, research notes, and support tickets into actionable PM decisions. Distinguishes data-dump (no synthesis) from insight-theater (overpolished narrative) from actionable synthesis (decision-grade clarity)
user-feedback-aggregationCollecting and synthesizing user feedback across channels into continuous decision signal. Triage discipline that distinguishes loudest-voice (whoever complains most) from averaged-noise (every signal weighted equally) from triaged-synthesis (weighted by source quality and decision relevance)
competitor-experience-auditCross-site experience patterns and gaps across a vertical

Cross-cutting workflows (5)

SkillWhat it does
form-strategyForm design, validation patterns, spam prevention, conversion tuning
content-migrationPlatform migrations with SEO equity preservation
internationalizationLocale strategy, hreflang, translation workflow, RTL design
dependency-managementPackage updates, security patches, lockfile hygiene
cost-optimizationInfrastructure spend audits, rightsizing, contract negotiation

Process and team (5)

SkillWhat it does
stakeholder-communicationStatus updates, exec readouts, project communications
documentation-strategyDocumentation systems, what to document, maintenance cadence
vendor-evaluationTool and vendor selection using a structured rubric
team-onboarding-playbook30-60-90 onboarding plans for new hires and contractors
skill-creation-walkthroughThe meta-skill: how to write your own custom skills

Recommended MCPs

Skills compose best when Claude has live access to your data and tools. Model Context Protocol (MCP) servers provide that bridge. The skills in this library work without any MCPs, but pair them with the right ones and they go from "frameworks Claude follows" to "workflows Claude executes against your real systems."

Below is the MCP shortlist by skill area. None of these are required (except the Ahrefs MCP for the SEO audit suite). All are categorical recommendations: where multiple options exist for the same job, pick the one that fits your stack.

SEO, competitive intelligence, and search data

The SEO audit suite (skills 23-29) is built around Ahrefs as its primary backend; foundation SEO skills (16-22) work with any equivalent. Competitive intelligence MCPs (Ahrefs, Semrush, Similarweb) cover overlapping but distinct data shapes: backlinks and keywords, traffic estimation, audience behavior. Use them in combination for the strongest signal.

A note on MCP costs: many of these MCPs are wrappers around APIs you are already paying for through a subscription, where MCP calls do not add marginal cost. Others (Ahrefs, Semrush, Similarweb, DataForSEO) use paid API credits per call, and long agentic sessions against these platforms can burn meaningful credit volume quickly. The cost model is documented on each integration's landing page at rampstack.co/integrations. Free with rate limits is called out where it applies (Google Search Console, PageSpeed Insights). When in doubt, check the platform's API pricing before running multi-hour agent workflows.

Backlink and keyword data

  • Ahrefs MCP - primary backend for the audit suite; backlink profiles, keyword data, content explorer, site audit. Referenced explicitly by seo-audit-orchestration and the 6 audit suite skills (backlink, keyword gap, content gap, traffic, site health, rank tracking). Credits-per-call.
  • Semrush MCP - alternative or complement to Ahrefs with stronger US keyword data and SEO-PR features (Topic Research, brand monitoring) Ahrefs does not cover. Pairs with seo-keyword, seo-competitor, seo-content-gap-audit. Verify the official MCP endpoint at authoring time; Semrush has shipped first-party MCP tooling. Credits-per-call.
  • DataForSEO MCP - programmatic SEO data (SERP, keywords, backlinks) at developer-friendly pricing; useful as a third source for cross-validation when methodology decisions hinge on data agreement. Credits-per-call (free tier available).

Traffic estimation and competitive intelligence

  • Similarweb MCP - competitive traffic estimation, audience demographics, channel mix (organic, paid, direct, referral, social, email), industry benchmarks, audience overlap analysis. Pairs with seo-competitor, seo-traffic-diagnosis (external-factor layer), brand-discovery (competitive scan), analytics-strategy (industry benchmarks). Where Ahrefs answers "how do they rank" and Semrush answers "what keywords drive what," Similarweb answers "how much traffic, from where, from whom." Credits-per-call.

Search Console and Core Web Vitals

  • Google Search Console MCP - free, official Google data; essential for seo-traffic-diagnosis and any audit that needs ground-truth click and impression data. Free with rate limits.
  • PageSpeed Insights MCP - free, paired with performance-optimization and seo-site-health-audit for Core Web Vitals field data. Free with rate limits.

Development and code

  • GitHub MCP - paired with code-review-web, pm-spec-writing, roadmap-planning, incident-response. Lets Claude read PRs, file issues, search code, and reference real commits.
  • Filesystem MCP - local file and code operations; pairs with most dev and content skills
  • Sentry MCP - paired with monitoring-and-alerting and incident-response. Real error data turns generic incident frameworks into specific diagnoses.

Hosting and infrastructure

  • Cloudflare MCP - paired with domain-strategy, security-baseline, performance-optimization. DNS records, redirects, page rules, security headers.
  • Vercel MCP - paired with launch-runbook and incident-response. Deployments, env vars, build logs.
  • Supabase MCP - paired with code-review-web, pm-spec-writing, backup-and-disaster-recovery. Schema, queries, edge functions.

Analytics and monitoring

  • PostHog MCP - paired with analytics-strategy, cro-optimization, journey-mapping. Event taxonomy review and funnel analysis grounded in real data.
  • Datadog MCP - paired with monitoring-and-alerting, incident-response. SLO design and alert routing against actual metrics.

Communication and project management

  • Slack MCP - paired with incident-response, stakeholder-communication, after-action-report. Read channel context, draft updates, post incident comms.
  • Linear MCP (or Jira MCP) - paired with pm-spec-writing, roadmap-planning. Spec writing against the actual issue tracker, not a generic template.

Research and search

  • Web search (built into Claude in most environments) - paired with brand-discovery, seo-keyword, seo-competitor, ux-research
  • Tavily MCP or Brave Search MCP - alternatives for deeper research workflows

Where to find them

  • modelcontextprotocol.io/servers - the canonical directory of MCP servers
  • The Connectors directory inside Claude.ai (Settings → Connectors)
  • claude mcp add in Claude Code for direct installation
  • Vendor websites for first-party servers (most major SaaS tools now ship official MCPs)

Building your own MCP

If a skill in this library would benefit from a tool integration that does not yet exist, the MCP documentation walks through building one. The seo-audit-orchestration skill is a worked example of how to design a skill suite around a specific MCP's capabilities.


Authoring conventions

Every skill follows the same structure. See SKILL_AUTHORING.md for the full spec.

Highlights:

  • Stack-agnostic. No specific framework versions in SKILL.md. Stack-specific patterns go in reference files. The Ahrefs-powered audit suite is the single named-tool exception.
  • Future-proof. Reference durable specs (W3C, WHATWG, Schema.org, MDN, NN/g, WCAG). Avoid trend pieces.
  • Uniform structure. Every SKILL.md has the same section order: When to use, When NOT to use, Required inputs, The framework, Workflow, Failure patterns, Output format, Reference files.
  • Tight length. SKILL.md under 250 lines. References under 400.
  • Punchy voice. Short sentences. Concrete examples beat abstract advice.

Repository structure

skills/
  skill-name/
    SKILL.md
    references/
      template.md
      checklist.md
      example.md
SKILL_AUTHORING.md          (the authoring guide)
CONTRIBUTING.md             (how to contribute)
MAPPING.md                  (origin notes for skills ported from existing work)
README.md                   (this file)
LICENSE                     (MIT)

Trust and security

Skills are instructions and code that run with your agent's permissions, so how a catalog is maintained matters. Changes reach main only through pull requests with signed commits and linear history. Each skill is hashed into a checksum manifest (SKILLS.lock) you can verify against, and reviewed against a documented safety checklist before it merges.

This process catches known classes of unsafe content and lets you confirm a skill matches the reviewed version. It is not a promise that any skill is risk-free. See SECURITY.md for the full process and how to report an issue.


Contributing

Contributions are welcome. Whether you want to fix a typo, add a reference file, or propose an entirely new skill, the bar is the same: follow the uniform structure, keep the voice consistent, and prove the skill earns its place.

See CONTRIBUTING.md for the full process.

The fastest path: use the skill-creation-walkthrough skill itself. It teaches the same authoring discipline used across all 103 skills, with worked examples and a blank template.


Acknowledgments

Thanks to @IgnacioChiaravalle for the community feedback that shaped PR #36: a CONTRIBUTING.md typo fix, a cross-linking pass between SKILL.md files and their reference files, and the new ARIA patterns reference for the accessibility-audit skill.


Resources

Official Anthropic documentation

Other skill libraries worth knowing

Companion concepts


License

MIT. Use it. Fork it. Ship things with it.

内容与创作Agent / MCP / Skill 创作

低风险

  • 来源需自行核对维护者身份。
  • 未检测到明显脚本安装指令。
  • 未检测到明显外部权限要求。
  • 未检测到高风险命令。
  • 扫描发现:0 条。

Codex — Git Clone 安装

  1. 安装前请先查看来源仓库和风险报告。
  2. 克隆仓库:git clone https://github.com/rampstackco/claude-skills.git
  3. 将 "skills/ai-content-collaboration" 文件夹复制到 Codex 的 skills 目录中。
  4. 重启 Codex 让新的 skill 生效。

Codex — 手动复制安装

  1. 安装前请先查看来源仓库和风险报告。
  2. 从源仓库下载 SKILL.md 及相关文件。
  3. 在 Codex 的 skills 目录中创建新文件夹。
  4. 将所有 skill 文件复制到新文件夹中。
  5. 重启 Codex 让新的 skill 生效。

Claude Code — Git Clone 安装

  1. 安装前请先查看来源仓库和风险报告。
  2. 克隆仓库:git clone https://github.com/rampstackco/claude-skills.git
  3. 将 "skills/ai-content-collaboration" 文件夹复制到 Claude Code 的 skills 目录中。
  4. 重启 Claude Code 让新的 skill 生效。

Claude Code — 手动复制安装

  1. 安装前请先查看来源仓库和风险报告。
  2. 从源仓库下载 SKILL.md 及相关文件。
  3. 在 Claude Code 的 skills 目录中创建新文件夹。
  4. 将所有 skill 文件复制到新文件夹中。
  5. 重启 Claude Code 让新的 skill 生效。

Cursor — Git Clone 安装

  1. 安装前请先查看来源仓库和风险报告。
  2. 克隆仓库:git clone https://github.com/rampstackco/claude-skills.git
  3. 将 "skills/ai-content-collaboration" 文件夹复制到 Cursor 的 skills 目录中。
  4. 重启 Cursor 让新的 skill 生效。

Cursor — 手动复制安装

  1. 安装前请先查看来源仓库和风险报告。
  2. 从源仓库下载 SKILL.md 及相关文件。
  3. 在 Cursor 的 skills 目录中创建新文件夹。
  4. 将所有 skill 文件复制到新文件夹中。
  5. 重启 Cursor 让新的 skill 生效。

GitHub Copilot — Git Clone 安装

  1. 安装前请先查看来源仓库和风险报告。
  2. 克隆仓库:git clone https://github.com/rampstackco/claude-skills.git
  3. 将 "skills/ai-content-collaboration" 文件夹复制到 GitHub Copilot 的 skills 目录中。
  4. 重启 GitHub Copilot 让新的 skill 生效。

GitHub Copilot — 手动复制安装

  1. 安装前请先查看来源仓库和风险报告。
  2. 从源仓库下载 SKILL.md 及相关文件。
  3. 在 GitHub Copilot 的 skills 目录中创建新文件夹。
  4. 将所有 skill 文件复制到新文件夹中。
  5. 重启 GitHub Copilot 让新的 skill 生效。

Windsurf — Git Clone 安装

  1. 安装前请先查看来源仓库和风险报告。
  2. 克隆仓库:git clone https://github.com/rampstackco/claude-skills.git
  3. 将 "skills/ai-content-collaboration" 文件夹复制到 Windsurf 的 skills 目录中。
  4. 重启 Windsurf 让新的 skill 生效。

Windsurf — 手动复制安装

  1. 安装前请先查看来源仓库和风险报告。
  2. 从源仓库下载 SKILL.md 及相关文件。
  3. 在 Windsurf 的 skills 目录中创建新文件夹。
  4. 将所有 skill 文件复制到新文件夹中。
  5. 重启 Windsurf 让新的 skill 生效。
查看 SKILL.md 原文
name: ai-content-collaboration
description: "How humans and AI compose in content workflows. Where AI legitimately participates, where humans must own, hybrid workflow patterns, voice ownership preservation, the AI slop problem, disclosure and transparency, team calibration, and the ethics of intellectually honest AI-assisted content production. Triggers on AI content workflow, AI-assisted writing, hybrid content production, AI in editorial, AI slop, AI disclosure, AI usage policy, AI content ethics, voice preservation with AI, team AI calibration. Also triggers when content feels generic despite quality tools, when team AI usage has drifted into inconsistency, or when a regulated or trust-sensitive context requires explicit AI policy."
category: content
catalog_summary: "How humans and AI compose in content workflows: participation boundaries, hybrid patterns, voice ownership, the AI slop problem, disclosure and transparency, team calibration, and the ethics of honest AI-assisted production"
display_order: 8

AI Content Collaboration

A senior editorial leader's playbook for how humans and AI compose in content workflows. Pragmatic, tool-agnostic, honest about both what AI in the loop enables and what it threatens.

Most content programs in 2026 use AI somewhere in the workflow. Pretending otherwise is dishonest; treating AI as a magic content factory is the failure mode this skill exists to prevent. The discipline is in between: knowing where AI legitimately accelerates, where humans must own, what hybrid patterns produce work that earns reader trust, and what crosses the line into AI slop or intellectual dishonesty.

This skill is the WORKFLOW layer that composes with every other content skill. Briefs can be AI-assisted; hub architectures can be AI-assisted; programmatic SEO is almost always AI-involved; editorial QA now includes AI-content audit by necessity. The collaboration discipline applies to all production stages, not to a single artifact type.

The voice is pragmatic and tool-agnostic deliberately. The methodology applies whether the AI in your loop is one of the major commercial models, an open-source model, or whatever ships next quarter. What stays constant is the workflow shape, the participation boundaries, the voice ownership question, and the ethical frame. What changes is which specific tool you reach for, which is implementation work that varies by team and budget.

When to use this skill: building or refining an AI-content workflow, calibrating a team on consistent AI usage, addressing the "we use AI but our work feels generic" problem, designing disclosure policies, or working through the ethics of AI-assisted content production for a regulated or trust-sensitive context.


What this skill is for

This skill spans the workflow layer of AI-assisted content production. It composes with all six other content-suite skills as the cross-cutting discipline.

  • content-strategy is program scope: what to produce. Strategy decisions can be AI-assisted; the program-level judgment stays human.
  • pillar-content-architecture is hub scope: how the topical hub fits together. Hub architecture can be AI-suggested; the architectural commitment stays human.
  • content-brief-authoring is per-piece scope: briefs each piece. Briefs can be AI-drafted from research; the contract decisions stay human.
  • content-and-copy is execution scope: writes each piece. Drafts can be AI-produced; voice and editorial judgment stay human.
  • programmatic-seo is scaled scope: generates pages from data. AI generation is the dominant production model; sampling QA is the human gate.
  • editorial-qa is gate scope: verifies before publish. AI-content audit is now a load-bearing gate; the audit's judgment stays human.
  • This skill is workflow scope: how the human and AI layers compose across all six stages above.

The audience: editorial leaders, content directors, content ops managers, agencies running AI-assisted production, in-house teams calibrating AI usage across writers. The voice is senior editorial leader to junior editor or content marketer. Pragmatic, honest, tool-agnostic.

What is not in scope: specific prompts (those are implementation; teams develop their own), specific tool endorsements (the methodology applies regardless of which tool is in the loop), specific integration code (varies by stack and team). Tool categories appear when they earn methodology relevance; specific tools appear only as illustrations of categories, never as recommendations.


Humans own, AI accelerates

The keystone framing.

The pathology to avoid is treating AI as either a magic content factory (cheap, fast, scaled, output quality optional) OR as a forbidden intruder (purity gospel that does not survive contact with deadlines). Both readings produce bad work.

The discipline that produces durable work: humans own the content; AI accelerates the work. Specifically:

Humans own. Editorial judgment, voice, distinctive POV, fact accuracy, ethical decisions, what to publish versus what to kill, brand voice, narrative arc, tone calibration, reader empathy, claim verification.

AI accelerates. Research synthesis, draft generation against a brief, copy edit suggestions, alternative phrasings, summary, transcription, quality-control automation at scale.

The line. AI does work that the human directs and verifies. AI does NOT make decisions about what publishes, who is quoted, what is true, or what voice the brand uses.

The litmus test. If your AI-assisted piece publishes without a human being able to defend every claim, every position, and every word, you have crossed the line. The piece is AI's work, dressed in your byline. Readers eventually notice.


Where AI legitimately participates

A non-exhaustive list of stages where AI in the loop is fine and often improves the work.

  • Research synthesis. AI condenses long-form sources into briefs the writer reads. Saves hours; the writer still reads and verifies.
  • Outline generation against a brief. AI proposes an H2 / H3 structure from a brief; the editor approves or restructures.
  • First-draft generation. AI produces a draft against an explicit brief; the human edits substantially.
  • Alternative phrasings. AI offers 3 versions of a sentence; the human picks one or rewrites.
  • Copy edit suggestions. AI catches typos, awkward phrasings, repetition.
  • Summary and abstraction. AI condenses long pieces into TL;DRs.
  • Transcription. AI transcribes interview audio; the human verifies.
  • Translation drafts. AI produces a translation draft; a native speaker reviews and corrects.
  • Quality-control automation at scale. AI flags pages in a programmatic SEO set that need human review.
  • Idea generation. AI proposes 30 angles; the human picks 3.

In each case, AI accelerates work the human still owns. The acceleration is real; the ownership stays unchanged.

Detail in references/ai-participation-boundaries.md.


Where humans must own

The boundary list.

  • Editorial judgment. What to publish, what to kill, what is worth saying. AI cannot decide whether a piece is good enough to ship.
  • Voice. Brand voice, distinctive POV, the way THIS publication sounds different from the next one. AI default voice is generic by construction; voice is a human contribution.
  • Fact verification. Every claim, every statistic, every quote, every named person. AI hallucinates; humans verify.
  • Ethical decisions. What is appropriate to publish, what is harmful, what crosses lines, what disclosure is required.
  • Reader empathy. What the reader actually needs from this piece, not what the algorithm scores well.
  • Quote attribution. Real people who actually said the thing, with consent where relevant.
  • Tone calibration on hard topics. Grief, illness, sensitive history, contested politics. AI defaults to anodyne; humans calibrate to context.
  • Narrative arc. How the piece unfolds, where the reader's attention goes. AI produces shapes; humans choose them.
  • Final approval. The human who signs off is accountable for what shipped.

The "human in the loop" framing is necessary but insufficient. A human briefly reviewing AI-generated content before publish is not ownership; it is rubber-stamping. Ownership requires the human to have made the actual decisions the piece embodies.


Hybrid workflow patterns

Five patterns that work, with tradeoffs.

1. AI-first draft, human-edit-heavy. AI produces a 90% draft; the human spends 60% of the time editing. Output: efficient for high-volume editorial; risks generic voice if editing is light.

2. Human-first outline + research, AI-draft, human-rewrite. Human builds the outline and gathers research; AI drafts within that scaffold; human rewrites in voice. Output: preserves voice better; slower than AI-first.

3. AI-as-research-assistant, human-writes. AI condenses sources into a brief; human writes the entire piece from the brief. Output: highest voice fidelity; slowest.

4. Human-writes, AI-as-editor. Human drafts; AI suggests edits, alternative phrasings, copy edits; human accepts or rejects. Output: writer voice preserved; AI catches details.

5. AI-generates-at-scale, human-samples. For programmatic SEO. AI generates thousands of pages; human samples 50 to 200 with editorial-qa discipline. Output: scaled production; depends entirely on template quality and sampling discipline.

The pattern that fits depends on volume, voice sensitivity, team skill, and time budget. No pattern is "the right one"; pattern selection is a real decision that should match the production context.

Detail in references/hybrid-workflow-patterns.md.


Voice ownership preservation

Voice is the dominant casualty of careless AI workflows. The patterns that preserve voice.

  • Voice guidelines as prompt input. Every AI generation includes the brand voice guidelines as context. Generic AI defaults regress without this.
  • Sample text as voice anchor. Feed the AI 2 to 3 paragraphs of canonical brand voice as part of the prompt. AI mimics what it sees more than what it is told.
  • Mid-draft voice check. At the halfway mark of a long piece, have a human or a separate AI pass read for voice drift. Long AI generations regress halfway through almost always.
  • Final pass in human voice. The human edits the closing sections in their own voice; this is where the piece's emotional register often lands.
  • Reject the bland. Any sentence that could appear in any other piece on the topic gets rewritten. Voice lives in the specific.

The honest framing. Voice is the hardest thing to preserve in AI-assisted work and the easiest thing to lose. Programs that do not actively preserve voice end up with content that is technically correct, semantically generic, and indistinguishable from competitors using the same tools.

Detail in references/voice-ownership-preservation.md.


The AI slop problem

AI slop is the term of art for AI-generated content that is technically functional but reads as generic, derivative, and signal-less. Cross-reference editorial-qa's ai-content-audit-patterns reference for the detection patterns; this section addresses prevention.

Patterns that produce slop.

  • AI does too much of the work (no real human direction or rewriting)
  • Generic prompts (no brand voice context, no audience specificity, no anti-pattern guidance)
  • No editorial judgment in the loop (AI generates, human glances, ship)
  • Volume prioritized over quality (10x more pages can mean 10x more slop, not 10x more value)
  • No iteration (first draft ships; no rewrite for voice)

Patterns that prevent slop.

  • Strong briefs (per content-brief-authoring)
  • Voice guidelines as prompt context
  • Heavy human editing pass
  • Iteration: AI draft, then human rewrite, then AI suggestions, then human final
  • Editorial judgment at every gate

The reader-detection problem. Readers can often sense AI-flavored content even when they cannot articulate why. Generic openings, predictable structures, "perfect" grammar that is emotionally flat. Slop loses reader trust over time even when individual pieces are not penalized.

Detail in references/ai-slop-detection-and-avoidance.md and cross-reference editorial-qa's audit patterns.


Disclosure and transparency

When should AI usage be disclosed to readers?

The tiered framework.

  • Always disclose. Journalism, news reporting, attributed expert opinion, content where AI tools are the subject.
  • Default disclosure (consider context). Thought leadership where the byline is doing trust work, regulated industries, content that influences purchase decisions.
  • Generally not necessary. Marketing copy, descriptive product content, programmatic data pages, copy edit assistance only.
  • Clearly fine without disclosure. AI as research assistant only; AI for transcription; AI for spelling and grammar suggestions.

The principle. Disclose when the reader's understanding of the content's origin would change their trust in it. A bylined opinion piece purportedly by a named expert that is substantially AI-drafted is a trust violation; a product description on an ecommerce site that was AI-drafted is not.

Disclosure language patterns (when used).

  • "AI tools assisted in research and drafting; the author edited and verified all claims."
  • "This piece was generated programmatically from [data source]; reviewed by [team] before publish."
  • Avoid hedging language like "may have used AI" or "could have been AI-assisted"; be specific or omit.

Industry-specific norms vary. Major journalism organizations have published explicit AI usage standards. Content marketing has weaker norms but is moving toward disclosure for high-trust pieces.

Detail in references/disclosure-and-transparency-patterns.md.


Team training and calibration

Inconsistent AI usage across a team produces inconsistent output. The discipline.

  • Documented AI policy. Which uses are approved, which require explicit permission, which are prohibited.
  • Calibration sessions. Editors review AI-assisted pieces from multiple writers, surface differences, agree on standards.
  • Voice library updates. As voice evolves, the prompts and sample text fed to AI evolve with it.
  • Quality benchmarks. What does "AI-assisted but on-voice" look like for your brand? Document it with examples.
  • Tool standardization or intentional pluralism. Team uses one tool consistently OR documents which tools fit which tasks.
  • Forbidden patterns list. This team does not use AI for X (whatever X is for your context).
  • Onboarding. New writers learn the AI policy and calibration in their first 2 weeks.

The pathology. AI usage emerges informally, every writer develops their own patterns, output drifts, editors cannot pinpoint why pieces feel off. The discipline is making AI usage explicit, calibrated, and documented.

Detail in references/team-training-and-calibration.md.


Ethics: training data, attribution, intellectual honesty

AI tools were trained on copyrighted material. That is the simple ethical reality of every major LLM in 2026. The catalog's position on this question is not "AI use is unethical" (that would render the catalog itself hypocritical) but "intellectual honesty about AI involvement is non-negotiable."

The principles.

  • Do not pass AI work as fully human-written. Bylined content where the byline implies human craft requires substantial human craft.
  • Do not claim AI did not help when it did. False denials are worse than disclosure.
  • Do not generate content that closely mirrors copyrighted source material. AI tools can produce near-replicas of training data when prompted carelessly; humans verify originality.
  • Attribute when borrowing. Ideas, frameworks, statistics that came from specific sources get cited.
  • Do not fabricate quotes or expertise. Hallucinated quotes attributed to real people are dishonest regardless of whether AI generated them.
  • Be honest about AI capabilities and limits. Do not oversell AI as more capable than it is.

The intellectual-honesty frame supersedes any specific policy debate. Teams that treat AI usage with intellectual honesty produce content readers can trust over time. Teams that hide, deny, or rationalize lose trust eventually.

Detail in references/ethics-and-intellectual-honesty.md.


Common failure modes

Rapid-fire. Diagnoses in references/common-collaboration-failures.md.

  • "We used AI and the content feels generic." Voice not preserved; not enough human rewriting.
  • "Hallucinated facts made it to publish." Fact-verification gate skipped or rushed.
  • "Different writers produce wildly different AI-assisted output." No team calibration.
  • "Our AI-assisted SEO content got penalized." Slop volume plus thin templates plus no QA discipline.
  • "We cannot tell what was AI versus human." No AI usage tracking; teams should document at the workflow level.
  • "Readers complained about AI-flavored content." Slop reaching audience; intensify human craft pass.
  • "We disclosed AI usage and lost credibility." Depends on context; disclosure is sometimes a trust gain, sometimes a loss; calibrate to audience norms.
  • "Our AI tools changed and our content shifted." Over-coupled to one tool's specific behavior; methodology should be tool-agnostic.
  • "We are producing 10x more content but the same audience growth." Volume was not the constraint that was binding; quality was.
  • "The team is using AI inconsistently." Calibration sessions overdue.
  • "An expert byline turned out to be substantially AI-drafted." Ethics breach; correct, disclose, recalibrate.

The framework: 12 considerations for AI content collaboration

When designing or auditing an AI-assisted content workflow, walk these 12 considerations.

  1. Humans own; AI accelerates. Make this explicit in your workflow, not implicit.
  2. Participation boundaries. Document where AI legitimately helps, where humans must own.
  3. Hybrid pattern selection. Match the pattern to volume, voice sensitivity, time budget.
  4. Voice guidelines as prompt input. Every AI generation includes brand voice context.
  5. Voice drift sampling. Long pieces drift mid-way; sample throughout.
  6. Fact verification gate. Every claim, every quote, every stat verified before publish.
  7. AI slop prevention. Heavy human editing, strong briefs, iteration.
  8. Disclosure tiering. Disclose when origin would change reader trust; calibrate to audience.
  9. Team calibration. Documented policy, calibration sessions, voice library.
  10. Tool-agnostic methodology. Workflow shape stays constant as tools change.
  11. Ethical floor. Intellectual honesty, no fabrication, no hidden AI in trust-sensitive work.
  12. Final accountability. The human who signs off is accountable; AI does not sign off.

The output of the framework is a workflow document the team can reference: AI participation rules named, hybrid pattern selected, voice preservation patterns specified, disclosure tier set, calibration cadence committed, ethical floor articulated, accountable signer named for each piece.


Reference files


Closing: collaboration, not replacement

AI in content workflows is neither magic nor menace. It is a category of tooling that, like every tooling category before it, rewards disciplined use and punishes careless use. The teams producing memorable AI-assisted content are the ones holding the line on human ownership, voice, fact accuracy, and intellectual honesty. The teams producing AI slop are the ones treating AI as a content factory.

The discipline is not anti-AI; it is pro-craft. Craft was always what made content worth reading; AI does not change that, it just raises the cost of skipping it.

When in doubt about whether an AI-assisted workflow is ready, ask: is human ownership specified, are participation boundaries documented, is voice preservation built into the prompt and review patterns, is fact verification a halt-condition, is disclosure tiered to audience trust, is the team calibrated, and is the ethical floor explicit? If yes to all of those, the workflow is ready. If no to any, the gap is where the program will produce slop and lose reader trust.

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