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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24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.

开发与工程数据与 AIAgent / 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/chatbot-flow-design" 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/chatbot-flow-design" 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/chatbot-flow-design" 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/chatbot-flow-design" 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/chatbot-flow-design" 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: chatbot-flow-design
description: "Designing conversational flows for website chatbots and AI agents. Intent recognition architecture, branching logic, fallback handling, escalation to human, conversation analytics. Honest about scripted-bot (rigid trees, fail edge cases), hallucinating-bot (LLM without structure, makes things up), and structured-guided-conversation (LLM-powered with intent architecture and fallback discipline) patterns. Distinguishes chatbot DESIGN (this skill) from chatbot IMPLEMENTATION (engineering and platform work). Triggers on chatbot, conversational AI, AI agent, chat widget, intent design, conversational flow, bot escalation, LLM grounding. Also triggers when a chatbot is hallucinating, when a scripted bot is failing edge cases, or when a chatbot is being scoped for the first time."
category: growth-tooling
catalog_summary: "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)"
display_order: 5

Chatbot Flow Design

A senior growth practitioner's playbook for designing conversational flows for website chatbots and AI agents. Intent recognition architecture, branching logic, fallback handling, escalation to human, conversation analytics. The discipline of building a bot that knows what it knows and routes appropriately when it does not.

Most chatbots on the web fail in one of two ways. Scripted bots break the moment a user phrases something the script did not anticipate; the user gets pushed through a decision tree that does not fit their situation. LLM-powered bots without structure hallucinate; they confidently answer questions about pricing, policy, or capabilities and frequently make up answers, creating support burden and trust damage.

The chatbots that work do something different. They have an intent architecture that defines what the bot can and cannot handle. They ground their responses in a knowledge base so they do not invent facts. They have explicit fallback paths for unclear or out-of-scope intents. They escalate to humans cleanly when the bot's job is done. The audience trusts the bot because the bot is honest about its scope.

The voice is the senior growth practitioner who has watched chatbots become trusted brand surfaces and watched them become liability risks. Practical, opinionated about the architecture that distinguishes the two outcomes, willing to call out when a chatbot is the wrong investment or when an existing chatbot needs to be redesigned rather than tuned.

When to use this skill: scoping a chatbot for the first time, auditing a chatbot that hallucinates or fails edge cases, designing the intent architecture and fallback patterns, or deciding when to escalate to humans.


What this skill covers

This skill spans chatbot design as conversational flow architecture, not chatbot implementation. The growth-tooling distinctions:

  • ai-content-collaboration covers AI in content workflows. This skill covers AI in customer-facing conversations.
  • integration-orchestrator covers cross-team coordination for chatbot deployment. This skill is the conversational design itself.
  • pm-spec-writing covers the spec for engineers building the bot. This skill is about WHAT the conversation should be; pm-spec-writing is about communicating it.
  • discovery-research-synthesis covers customer research that informs intent architecture. Input to this skill, not part of it.
  • chatbot-flow-design (this skill) is intent architecture, knowledge-base grounding, fallback patterns, and escalation discipline.

The audience: growth marketers and product marketers shipping chatbot growth tooling, in-house teams designing conversational flows for marketing or support contexts, agencies running chatbot work for clients.

Out of scope: AI in content workflows (covered by ai-content-collaboration); the engineering implementation of chatbots (handed off via pm-spec-writing); platform-specific bot configurations (those stay implementation-side); voice agents and IVR flows (different methodology though related principles apply).


The chatbot decision: when chatbots earn deployment

Before designing the chatbot, decide whether a chatbot is the right tool.

Chatbots earn deployment when:

  • The audience asks the same questions repeatedly. FAQ-style support, product capability questions, qualification routing. The bot handles the volume; humans handle the exceptions.
  • The audience is on the site at hours when humans cannot respond. Coverage gap that the bot fills meaningfully.
  • The bot can ground its answers in real knowledge (documentation, product specs, pricing pages). Without grounding, the bot's answers are at best generic and at worst fabricated.
  • The team can maintain the bot. Chatbots decay; intents drift; knowledge bases need updating. Without maintenance commitment, the bot becomes stale liability.

Chatbots do NOT earn deployment when:

  • The audience expects human conversation. Sales conversations, complex troubleshooting, sensitive topics often warrant human-first.
  • The team cannot ground the bot in real knowledge. A bot without grounding either hallucinates or stays so generic it adds no value.
  • The bot would replace working human channels. Replacing a high-quality sales chat with a low-quality bot degrades the experience.
  • The audience is small enough that direct human conversation is more efficient.
  • The team cannot maintain the bot. Stale bots produce wrong answers.

The decision is not "should we have a chatbot"; it is "is the chatbot the right tool for this specific audience and conversation."

Detail in references/chatbot-decision-criteria.md.


Scripted-bot vs hallucinating-bot vs structured-guided-conversation

The keystone framing.

Scripted-bot. Rigid decision tree. "Press 1 for X, 2 for Y." Fails the moment a user phrases something the script did not anticipate. The chatbot equivalent of an automated phone tree. Cost: the user's actual question goes unanswered; the bot pushes the user through paths that do not fit; the audience leaves with a worse experience than no bot.

Hallucinating-bot. LLM-powered with no structure. Will confidently answer questions about pricing, policy, capabilities, and frequently make up answers. Liability risk; trust-eroding; support burden when wrong answers reach customers. Cost: the bot's confident wrong answers damage the brand more than no bot would; the team learns about the hallucinations through customer complaints.

Structured-guided-conversation. LLM-powered with intent architecture, knowledge-base grounding, defined fallback paths, and explicit escalation to humans. The bot knows what it knows, knows what it does not, and routes appropriately. Cost: the design effort upfront is significant; the maintenance is real; the audience trusts the bot because the bot is honest about its scope.

The litmus test. Ask the bot a question outside its intended scope. Does it confidently make up an answer (hallucinating), refuse rigidly (scripted), or honestly route the user to a human or alternative resource (structured-guided)? The third response is the goal.


Intent architecture

Defining what the bot can and cannot handle.

The principle. The bot has a defined set of intents it can handle. Each intent maps to a conversation pattern (questions to ask, knowledge to ground in, response to provide). Anything outside the intent set falls to fallback.

Intent design patterns.

  • Named intents. "Pricing question," "feature comparison," "integration question," "support escalation," "demo request." Each intent is explicit.
  • Intent hierarchy. Top-level categories with sub-intents. "Pricing question" includes sub-intents for "tier comparison," "discount inquiry," "billing question."
  • Intent boundaries. Each intent has clear scope. Out-of-scope topics route to fallback.

Intent coverage. The bot's intents should cover 70-90 percent of expected conversations. The remaining percentage falls to fallback. Trying to cover 100 percent often produces bloated intent sets that the bot cannot handle reliably.

Intent maintenance. Intents drift as products evolve, audiences shift, and conversations change. Periodic review surfaces which intents are useful and which need refining.

Detail in references/intent-architecture-patterns.md.


Knowledge-base grounding

The bot's responses must come from real knowledge, not made-up confidence.

The principle. The bot's response generation should reference a structured knowledge base (documentation, product specs, pricing pages, support articles). The bot does not invent answers; it retrieves and presents.

Grounding patterns.

  • Retrieval-augmented generation (RAG). The bot searches a knowledge base for relevant content before generating a response. The response is grounded in retrieved content.
  • Source-of-truth design. The knowledge base is the canonical source. When the bot answers, the underlying source is identified.
  • Citation discipline. The bot can cite the source ("based on our pricing page, ..."). Audiences benefit from knowing where the answer came from.
  • Knowledge-base maintenance. The knowledge base is maintained as the product evolves; the bot's answers stay current.

The hallucinating-bot failure. No grounding. The LLM generates confident-sounding answers from nothing. The team discovers wrong answers through customer complaints.

The structured-guided win. Grounded answers. The bot's responses match the source-of-truth. Customer-facing accuracy is maintained.

Detail in references/knowledge-base-grounding-patterns.md.


Branching and conditional logic

How the bot adapts the conversation based on user input.

The principle. The bot's conversation can branch based on user inputs (intent recognized, prior answers, user attributes). Branching makes the conversation feel adaptive.

Branching patterns.

  • Intent-driven branching. Different intents lead to different conversation flows.
  • Context-driven branching. "Are you asking about plan A or plan B?" routes to plan-specific information.
  • User-attribute branching. Logged-in users may see different responses than anonymous users; enterprise visitors may see different responses than SMB.
  • Multi-turn branching. The conversation deepens over turns; later turns build on earlier context.

Branching discipline. Each branch should add value. Decorative branching (asking for confirmation when none is needed) adds friction.

Branching limits. Bots that branch too deeply lose users. 3-5 turns is often the practical limit before the user wants resolution.

Detail in references/branching-and-conditional-logic.md.


Fallback patterns

What happens when intent is unclear or out-of-scope.

The principle. Every conversation has fallback paths. The bot has rehearsed responses for "I do not know," "I am not sure I can help with that," "Let me connect you with a human."

Fallback patterns.

  • Clarifying question. "Can you tell me more about what you are looking for?" Asks the user to refine; sometimes recovers.
  • Suggested intents. "I can help with X, Y, or Z. Were you asking about one of those?" Surfaces what the bot can do.
  • Resource handoff. "I cannot answer that, but here is our [documentation page] that covers it."
  • Human escalation. "Let me connect you with a human who can help."

Fallback discipline. Multiple fallback layers. First, try clarification. If unclear after one round, suggest alternatives or escalate. Do not loop the user through 5 clarification attempts.

The fallback-as-honesty principle. A bot that admits it does not know earns more trust than a bot that fakes confidence. Audiences forgive limitations they were told about; audiences punish wrong answers they were given confidently.

Detail in references/fallback-pattern-design.md.


Escalation to human

When, how, with what context handoff.

The principle. Some conversations need a human. The bot escalates when its scope is exceeded, when the user requests it, or when the conversation pattern indicates the user is frustrated.

Escalation triggers.

  • User-initiated. "Talk to a human." The bot escalates immediately.
  • Out-of-scope intent. The bot recognizes the user's intent is outside its scope; escalates with the intent context.
  • Repeated fallback. After 2-3 unclear exchanges, the bot escalates rather than loop.
  • Sentiment-driven. The user's sentiment indicates frustration; the bot escalates rather than persist.
  • High-stakes topic. Sensitive topics (cancellations, complaints, security) escalate by default.

Escalation context handoff. When escalating, the bot passes the conversation history and recognized intent to the human. The human does not start from scratch; they pick up where the bot left off.

The escalation-quality test. Does the human pick up the context smoothly, or do they have to ask the user to repeat everything? The latter signals broken handoff.

Detail in references/escalation-to-human-patterns.md.


Conversation analytics

Measuring what the bot is and is not doing well.

The principle. Track the bot's performance per intent, per fallback, per escalation. The data informs maintenance and design improvements.

Conversation metrics.

  • Intent recognition rate. What percentage of conversations are correctly classified into intents?
  • Resolution rate per intent. What percentage of conversations starting with intent X resolve successfully (user gets the answer they needed)?
  • Fallback rate. What percentage of conversations hit fallback? High fallback rates signal intent gaps.
  • Escalation rate per intent. What percentage of conversations within each intent escalate? High escalation may signal the intent should not be bot-handled.
  • User satisfaction. Post-conversation surveys when feasible. Useful but limited; many users do not respond.

Diagnostic uses.

  • High fallback rate: intents missing or unclear; expand or refine intent set.
  • Low resolution rate per intent: knowledge base inadequate; improve grounding.
  • High escalation rate per intent: the intent may not be appropriate for bot handling.
  • High repeat-fallback within conversations: clarifying questions not landing; redesign clarification.

Detail in references/conversation-analytics-patterns.md.


Common failure modes

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

  • "Bot makes up answers about pricing." Hallucinating-bot pattern; no grounding to pricing source-of-truth.
  • "Users say the bot is rigid and unhelpful." Scripted-bot pattern; intents do not cover real questions.
  • "Sales is angry that the bot is qualifying leads wrong." Intent recognition or routing logic broken; audit qualification flow.
  • "Support tickets increase after chatbot launch." Bot is producing wrong answers that send users to support; audit grounding and resolution.
  • "Users abandon mid-conversation." Bot loops or fallback patterns are inadequate.
  • "We cannot tell what the bot is actually doing." Analytics missing; instrument before further changes.
  • "Bot was great at launch; quality has degraded." Maintenance lapse; knowledge base or intent set out of date.
  • "Bot escalates to humans for everything." Escalation logic too permissive; bot should handle more.
  • "Bot tries to handle everything itself." Escalation logic too restrictive; bot should escalate more.
  • "Users complain the bot does not know what they asked five minutes ago." Conversation context not preserved across turns.

The framework: 12 considerations for chatbot flow design

When designing or auditing a chatbot, walk these 12 considerations.

  1. The chatbot decision. Is a chatbot the right tool for this audience and conversation, or would a different channel serve?
  2. Structured-guided-conversation, not scripted or hallucinating. Intent architecture; knowledge-base grounding; fallback discipline.
  3. Intent architecture sound. Named intents with clear scope; coverage targets 70-90 percent of expected conversations.
  4. Knowledge-base grounded. Responses retrieved from source-of-truth; the bot does not invent answers.
  5. Branching adds value. Each branch serves a real need; depth limited to 3-5 turns.
  6. Fallback patterns multi-layered. Clarification, suggested intents, resource handoff, human escalation.
  7. Escalation triggers defined. User-initiated, out-of-scope, repeated fallback, sentiment-driven, high-stakes.
  8. Escalation context handoff clean. Humans pick up where the bot left off.
  9. Analytics instrumented. Per-intent recognition, resolution, fallback, escalation rates.
  10. Maintenance cadence defined. Knowledge base refreshed; intent set audited; bot quality verified.
  11. Brand voice consistent. The bot sounds like the brand it represents.
  12. Audience-fit honest. The bot serves the audience it can actually help; out-of-fit audiences get escalated quickly.

The output of the framework is a chatbot that knows what it knows, grounds its answers in real knowledge, escalates appropriately, and earns trust by being honest about its scope.


Reference files


Closing: chatbots earn deployment when they know what they don't know

The chatbots that work as compounding assets are the ones the audience trusts. Not because they answer every question. Not because they are infinitely capable. Because they are honest about their scope, ground their answers in real knowledge, and escalate to humans when the bot's job is done.

That is the bar. Below the bar are scripted-bots (rigid trees that fail edge cases) and hallucinating-bots (LLMs without structure that make things up). Above the bar are structured-guided-conversations where the bot's intent architecture, knowledge-base grounding, fallback discipline, and escalation patterns combine into a tool the audience can rely on.

The discipline is in the design choices. The intents that define what the bot can do. The knowledge-base grounding that prevents hallucination. The fallback patterns that handle the unknown gracefully. The escalation logic that knows when to step aside. The analytics that surface what is working and what is not. The maintenance discipline that keeps the bot in sync with the brand it represents.

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