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A complete, opinionated library of Claude Skills covering the full lifecycle of building, launch...
用 Codex 或 Claude 安装复制这段 Prompt,粘贴到 Codex、Claude 或其他助手里,让它先审查 Skill 页面再帮你安装。
复制前请先查看来源、License 和安全提示。
来源文件:README.md
A complete, opinionated library of Claude Skills covering the full lifecycle of building, launching, running, and growing a brand and a website.
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.
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.
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.
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:
SKILL.md and at least one reference filecreative-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.
Six entry-point skills, one per audience track. Run any of these standalone, or compose them with the rest of the catalog.
| Skill | What 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 |
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.
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.
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.
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 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 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 →
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.
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.
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.
The brands are filterable by architecture, typographic register, and category. The intent is reference work, not consumable templates.
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.
Filter by architecture, vertical, or brand voice; click any mark card to read its design rationale.
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.
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 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.
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.
Skills install in three different places depending on where you use Claude. Pick the platform that matches your workflow.
If your Claude.ai plan supports custom Skills:
.zip (one zip per skill folder containing SKILL.md and the references/ subfolder).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.
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.
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.
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.
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:
creative-briefseo-onpageseo-backlink-auditseo-content-gap-audit plus content-strategyafter-action-reportskill-creation-walkthroughYou can also call a skill explicitly: "Use the seo-audit-orchestration skill to run a full audit on example.com."
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.
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.
This catalog is the open-source methodology layer. Commercial surfaces at rampstack.co extend it:
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.
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.
claude-skills is the parent catalog. Curated subsets and companion repos focus on specific specialties:
| Repo | Focus | Skills |
|---|---|---|
| claude-skills | Full catalog (you are here) | 103 |
| claude-skills-starter | General-purpose lite | 14 |
| claude-skills-seo | SEO consulting | 12 |
| claude-skills-pm | Product management | 12 |
| claude-skills-widgets | UI patterns + components | 65 + 32 |
| awesome-claude-skills | Curated discovery list | n/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.
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.
All 103 skills are shipped. Each has a complete SKILL.md plus at least one reference file (template, checklist, or playbook).
| Skill | What it does |
|---|---|
brand-discovery | Audience research, competitive scan, positioning territory exploration |
creative-brief | Project briefs that align stakeholders before work starts |
creative-direction | Four-axis aesthetic brief (tone, aesthetic, audience, sensory ambition) for cross-skill coherence |
information-architecture | Sitemap, navigation, URL structure, content types, taxonomy |
content-strategy | Editorial strategy, content calendar, topical authority planning |
| Skill | What it does |
|---|---|
brand-ideation | Naming, positioning territories, mood directions, narrative angles |
brand-identity | Logo system, color, typography, imagery, iconography, motion |
brand-style-guide | The canonical reference document for the full brand system |
brand-voice | Voice attributes, tone shifts, vocabulary, paired-example library |
brand-archetype-system | 12 archetype defaults across 18 verticals: color, type, voice, imagery starters |
logo-design | Logo variants across architectures (wordmark, lockup, monogram, letterform-as-symbol), with rationale and application specs |
creative-brief-selector | Live-reference-grounded creative briefs with divergence check against prior builds |
| Skill | What it does |
|---|---|
design-system | Component library, design tokens, design system documentation |
design-standards | Production-grade page and component design standards |
art-direction | Photography, illustration, and visual direction for campaigns |
vertical-site-conventions | Vertical page and site composition built to the experience bar |
| Skill | What it does |
|---|---|
pillar-content-architecture | Hub-level content architecture: pillar topic selection, cluster planning, internal linking, URL structure, pillar and cluster page anatomy, topical authority signals, refresh discipline |
content-brief-authoring | Per-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-copy | Website copy, blog content, content production frameworks |
landing-page-copy | Landing pages, sales pages, hero-to-CTA flow |
email-sequences | Onboarding flows, lifecycle campaigns, transactional copy |
programmatic-seo | Designing 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-qa | Pre-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-collaboration | 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 |
long-form-content-frameworks | Structural 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-system | Systematic 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-repurposing | Cross-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-distribution | Content 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-reviews | Evidence tiers, methodology disclosure, honest review claims |
Tool-agnostic SEO skills. These define the conceptual frameworks. The SEO audit suite below adds the Ahrefs MCP-powered execution layer.
| Skill | What it does |
|---|---|
seo-onpage | Single-page audits and optimization across 8 dimensions |
seo-technical | Crawlability, indexability, rendering, schema, page experience |
seo-keyword | Discovery, intent classification, clustering, prioritization |
seo-competitor | SERP overlap, content gaps, backlink gaps, technical comparison |
seo-offpage | Link building, digital PR, citations, linkable assets |
seo-content-audit | Keep/update/merge/redirect/delete decisions across a site |
seo-aeo-geo | AI search optimization, llms.txt, extraction-friendly content |
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.
| Skill | What it does |
|---|---|
seo-audit-orchestration | Master orchestrator: sequences the suite, produces a rollup report |
seo-backlink-audit | Profile health, anchor mix, toxic links, reclamation, gap analysis |
seo-keyword-gap-audit | Competitor keyword gaps with opportunity scoring and clustering |
seo-content-gap-audit | Missing topics, thin coverage, outdated content, decay diagnosis |
seo-traffic-diagnosis | Diagnose drops, stalls, or wins via 5-layer root cause analysis |
seo-site-health-audit | Triage Ahrefs Site Audit findings by SEO impact, not severity |
seo-rank-tracking | Setup, baseline, segmentation, alerting, dashboarding |
| Skill | What it does |
|---|---|
pm-spec-writing | PRDs, user stories, acceptance criteria, dev briefs |
roadmap-planning | Quarterly planning, prioritization, dependency mapping |
integration-orchestrator | Sequence creative-direction work across phases, gates, handoffs, and QA verification |
experiment-design | Hypothesis to decision: sample size, duration, segment analysis, interpretation, and the failure modes that produce wrong shipping calls |
feature-flagging | Flags as production infrastructure: types, naming, lifecycle, targeting, rollout, stale flag cleanup, governance |
experimentation-analytics | Read result panels without fooling yourself: confidence intervals, p-values, multiple testing, sequential testing, CUPED, ratio metrics, network effects, dashboard reconciliation |
experimentation-platform-orchestrator | Pick the right experimentation platform, migrate when wrong, coordinate when multi-platform: a decision framework for Statsig, PostHog, GrowthBook, Optimizely, Amplitude, Eppo, Kameleoon |
product-analytics-setup | Instrument 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-experimentation | Run 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-playbook | The operational discipline of launching a feature well: positioning, internal alignment, customer comms, sales enablement, support readiness, rollout strategy, monitoring, and post-launch measurement |
jtbd-framing | Jobs-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-design | OKR 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-management | Running 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) |
| Skill | What it does |
|---|---|
code-review-web | PR review, build error diagnosis, security and quality checks |
frontend-component-build | Component architecture, props design, accessibility from the start |
accessibility-audit | WCAG compliance audit with remediation plan |
performance-optimization | Core Web Vitals, asset optimization, render performance |
| Skill | What it does |
|---|---|
qa-testing | Pre-launch QA, regression testing, cross-browser checks |
| Skill | What it does |
|---|---|
launch-runbook | Go-live runbook, DNS cutover, deploy day procedures |
incident-response | Incident triage, comms, mitigation, escalation |
after-action-report | Post-mortems, retros, learnings documentation |
domain-strategy | DNS architecture, redirects, registrars, multi-domain portfolios |
monitoring-and-alerting | SLO design, uptime checks, alert routing, on-call rotations |
backup-and-disaster-recovery | RPO/RTO targets, backup strategy, restoration drills |
security-baseline | HTTPS, security headers, CSP, secrets management, vulnerability scans |
email-deliverability | DMARC, SPF, DKIM, sender reputation, deliverability monitoring |
media-asset-management | Image pipelines, video hosting, asset libraries, format selection |
| Skill | What it does |
|---|---|
analytics-strategy | Measurement frameworks, dashboard design, event taxonomy |
cro-optimization | Hypothesis-driven testing, conversion optimization |
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.
| Skill | What it does |
|---|---|
lead-magnet-design | Designing 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-design | Designing 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-design | Designing 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-design | Designing 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-design | Designing 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-architecture | Architecting 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-design | Designing 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-tour | Designing 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-design | Designing 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-design | Designing 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-design | Designing 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-design | Designing 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) |
Paid media discipline: strategy, creative, and performance analytics. Pairs with the paid media platforms in the /integrations catalog at rampstack.co.
| Skill | What it does |
|---|---|
paid-media-strategy | Hypothesis to spend: channel selection, budget allocation, audience targeting, bid strategy, attribution reality, and the failure modes that burn agency-scale budgets |
ads-creative-development | Hook patterns, format selection, video pacing, variation systems, testing methodology, fatigue detection, and the platform-specific creative norms that separate ads from clutter |
ads-performance-analytics | Read 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 |
| Skill | What it does |
|---|---|
ux-research | Research planning, user interviews, qualitative synthesis |
usability-testing | Test design, moderation, findings reports |
journey-mapping | Customer journey maps, service blueprints, friction analysis |
discovery-research-synthesis | Synthesizing 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-aggregation | Collecting 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-audit | Cross-site experience patterns and gaps across a vertical |
| Skill | What it does |
|---|---|
form-strategy | Form design, validation patterns, spam prevention, conversion tuning |
content-migration | Platform migrations with SEO equity preservation |
internationalization | Locale strategy, hreflang, translation workflow, RTL design |
dependency-management | Package updates, security patches, lockfile hygiene |
cost-optimization | Infrastructure spend audits, rightsizing, contract negotiation |
| Skill | What it does |
|---|---|
stakeholder-communication | Status updates, exec readouts, project communications |
documentation-strategy | Documentation systems, what to document, maintenance cadence |
vendor-evaluation | Tool and vendor selection using a structured rubric |
team-onboarding-playbook | 30-60-90 onboarding plans for new hires and contractors |
skill-creation-walkthrough | The meta-skill: how to write your own custom skills |
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.
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
seo-audit-orchestration and the 6 audit suite skills (backlink, keyword gap, content gap, traffic, site health, rank tracking). Credits-per-call.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.Traffic estimation and competitive intelligence
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
seo-traffic-diagnosis and any audit that needs ground-truth click and impression data. Free with rate limits.performance-optimization and seo-site-health-audit for Core Web Vitals field data. Free with rate limits.code-review-web, pm-spec-writing, roadmap-planning, incident-response. Lets Claude read PRs, file issues, search code, and reference real commits.monitoring-and-alerting and incident-response. Real error data turns generic incident frameworks into specific diagnoses.domain-strategy, security-baseline, performance-optimization. DNS records, redirects, page rules, security headers.launch-runbook and incident-response. Deployments, env vars, build logs.code-review-web, pm-spec-writing, backup-and-disaster-recovery. Schema, queries, edge functions.analytics-strategy, cro-optimization, journey-mapping. Event taxonomy review and funnel analysis grounded in real data.monitoring-and-alerting, incident-response. SLO design and alert routing against actual metrics.incident-response, stakeholder-communication, after-action-report. Read channel context, draft updates, post incident comms.pm-spec-writing, roadmap-planning. Spec writing against the actual issue tracker, not a generic template.brand-discovery, seo-keyword, seo-competitor, ux-researchclaude mcp add in Claude Code for direct installationIf 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.
Every skill follows the same structure. See SKILL_AUTHORING.md for the full spec.
Highlights:
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)
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.
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.
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.
MIT. Use it. Fork it. Ship things with it.
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: 8A 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.
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.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.
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.
A non-exhaustive list of stages where AI in the loop is fine and often improves the work.
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.
The boundary list.
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.
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 is the dominant casualty of careless AI workflows. The patterns that preserve voice.
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.
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.
Patterns that prevent slop.
content-brief-authoring)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.
When should AI usage be disclosed to readers?
The tiered framework.
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).
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.
Inconsistent AI usage across a team produces inconsistent output. The discipline.
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.
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.
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.
Rapid-fire. Diagnoses in references/common-collaboration-failures.md.
When designing or auditing an AI-assisted content workflow, walk these 12 considerations.
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.
references/ai-participation-boundaries.md - Where AI legitimately helps, where humans must own. The boundary list and the "human-in-the-loop is not ownership" distinction.references/hybrid-workflow-patterns.md - Five workflow patterns with tradeoffs and selection criteria. When each pattern fits production context.references/voice-ownership-preservation.md - Voice guidelines as prompt input, sample text as voice anchor, mid-draft voice check, final pass in human voice, reject-the-bland discipline.references/ai-slop-detection-and-avoidance.md - What produces slop, what prevents it. Cross-references editorial-qa's audit patterns.references/disclosure-and-transparency-patterns.md - Tiered disclosure framework, language patterns, industry norms.references/team-training-and-calibration.md - Documented policy, calibration sessions, voice library, quality benchmarks, onboarding.references/quality-calibration-with-ai-in-loop.md - How editorial standards shift when AI is in the workflow. Same standards, different failure modes.references/ethics-and-intellectual-honesty.md - Training data, attribution, fabrication boundaries, intellectual honesty as the supervening frame.references/common-collaboration-failures.md - 11+ failure patterns with diagnoses and fixes.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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