Claude Code Skills · page 89

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Keeps the engagement record for client work. Use when they name a client or stakeholder. Use when they debrief a meeting or paste notes. Use when they ask what was agreed. Use when they run a POC, change the client's codebase, prove it on their staging, go live, or need evals before a model acts. Use when they prep a readout, when trust shifts, or they say @fde. Route and run the local fde CLI (or npx --yes fdeops). Never ask them to type commands. Not for ordinary code edits in an unbound repo.

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Systematically find and fix hardcoded dark/light mode color remnants in Vue components. Use when user reports dark backgrounds in light mode, light backgrounds in dark mode, or inconsistent theme appearance across pages.

Integrate Java business systems with ReachAI SDK registration, SDK instance heartbeat, gateway/embed access, and optional API Management handoff. Use when asked to connect a Spring Boot service to ReachAI, add reachai-capability-sdk or reachai-spring-boot2-starter, configure reachai.registry/reachai.project/reachai.capability, prepare @ReachCapability metadata for later manual SDK sync, or verify SDK onboarding from a ReachAI manifest.

Edit, validate, debug, publish, and inspect ReachAI Workflow drafts through the Workflow AI Coding REST API. Use when asked to create or modify a workflow graph, add/update/delete nodes or edges, validate GraphSpec, dry-run or debug-run a workflow, inspect trace/run output, check release readiness, publish a validated draft, or work on PAGE_ASSISTANT workflows from Cursor/Codex.

Helps users discover and install agent skills when they ask questions like "how do I do X", "find a skill for X", "is there a skill that can...", or express interest in extending capabilities. This skill should be used when the user is looking for functionality that might exist as an installable skill.

Guide for creating effective skills. This skill should be used when users want to create a new skill (or update an existing skill) that extends Codex's capabilities with specialized knowledge, workflows, or tool integrations.

Use when starting feature work that needs isolation from current workspace or before executing implementation plans - ensures an isolated workspace exists via native tools or git worktree fallback

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5-pass structured code review — correctness, security, performance, readability, consistency

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Generate and verify tests — happy path, edge cases, error paths — using the project's own framework and patterns

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Answer return, refund, and warranty questions for electronics. Use when the user mentions returns, refunds, RMAs, warranty coverage, damaged items, or opened packaging.

Provide setup, troubleshooting, and maintenance guidance. Use when the user reports a device that won't power on, connectivity issues, setup questions, overheating, or maintenance concerns.

Direct game art and creative vision. Turn GAME_DESIGN into a production-level ART_DIRECTION defining a recognizable visual style, camera and composition, world and character grammar, functional colour/light/material, HUD feedback, motion and transition specs, audio direction, and a signature moment for every screen and mode. Use for what should the game look like, set the art direction, define the visual style. 游戏美术与创意方向。把 GAME_DESIGN 转成策划级 ART_DIRECTION，定义可辨识视觉风格、镜头构图、世界与角色语法、功能性色光材质、界面反馈、运动与转场规格、声音方向和每个界面/模式的签名游戏时刻。用于判断游戏应该长什么样、制定游戏美术方向等需求。

Build a risk-matched whitebox or the approved production game for its target runtime. Turn GAME_DESIGN, and ART_DIRECTION when production begins, into a minimal BUILD_BRIEF and a runnable candidate that can be iterated with replayable evidence. Use for prototype the riskiest design question, implement the approved game design, or turn this design into a running game. 游戏构建执行。先按最大风险做白盒，或在正式生产时把批准后的 GAME_DESIGN 与 ART_DIRECTION 压缩成最小 BUILD_BRIEF，在目标运行环境中实现可运行候选并用可回放证据迭代。用于验证高风险设计问题、构建游戏原型或实现批准方案。

Design game concepts from a novel. From SOURCE_BIBLE and PRODUCT_BRIEF, generate three genuinely different directions on the dimensions still unlocked, then pick the most worthwhile playable prototype using hard vetoes and explicit trade-offs. Use for what game should this novel become, compare game concepts, choose a game direction for this book, decide whether this novel should become an interactive story. 小说游戏概念设计。根据 SOURCE_BIBLE 与 PRODUCT_BRIEF 在未锁定维度上生成三个真正不同的方案，用硬否决和关键取舍选择最值得做的可玩原型。用于判断小说适合做成什么游戏、该不该做成互动叙事、比较游戏方案等需求。

Verify a game with evidence on its selected target runtime. Launch the actual build and prove real rendering, input, the core loop, at least one designed outcome, restart, and explicit limitations without dressing subjective fun up as a certain verdict. Use for test a generated game, QA a game build, check whether the game is fully playable, or verify the build. 游戏证据化质量验证。在选定的目标运行环境中启动实际构建，证明真实渲染、输入、核心循环、至少一个设计结果、重开和明确限制，不把主观趣味包装成确定性结论。用于测试生成游戏、检查游戏能否完整游玩或验证构建。

Design game experience, systems, and levels. Converge the chosen concept into one GAME_DESIGN defining the player promise, core loop, how the world responds, the systems actually needed, level pacing, feedback, failure, and a fully playable prototype. Use for design the game world, deepen the gameplay and levels, write the game design document, design the branching story structure, scenes, dialogue and consequences. 游戏体验、系统与关卡策划。把选定概念收束为一份 GAME_DESIGN，定义玩家承诺、核心循环、世界响应、必要系统、关卡节奏、反馈、失败和可完整游玩的原型。用于设计游戏世界、深化玩法和关卡、设计互动叙事的场景对白与分支因果等需求。

Deconstruct a novel for game adaptation. Compress a raw novel, deconstruction library, or writing project into a SOURCE_BIBLE with cited textual evidence, extracting world rules, player verbs, spaces, character will, systems, and visual anchors — without inventing a genre yet. Use for gameable book analysis, analyze a novel for game adaptation, extract game systems from a book. 游戏化拆书。把原始小说、拆文库或写作工程压缩成有必要原文证据的 SOURCE_BIBLE，提取世界规则、玩家动词、空间、角色意志、系统和视觉锚点，不提前发明游戏类型。用于游戏化拆书、分析小说的游戏改编基础等需求。

Turn a novel into a fully playable game on the selected target platform. Orchestrates the whole adaptation pipeline — requirements intake, gameable deconstruction, concept selection, world and visual design, target-runtime build, and evidence-based QA — for a novel in any language. Use for novel to game, story to game, book to game, adapt this novel into a game, turn this book into a playable prototype, make an interactive story or text adventure from this novel. NovelToGame 总入口。把任意语言的原始小说、拆文库或 oh-story 写作工程转成有原著依据、可在目标平台完整游玩的游戏，编排游戏化拆解、概念选择、游戏与视觉设计、目标运行环境构建和证据化质量验证。用于小说转游戏、把这本书做成游戏、把小说改成互动小说 / 互动叙事 / 文字冒险游戏等需求。

Query and apply The Website Specification — a platform-agnostic specification of what a good website does, with each item tagged required, recommended, optional, or avoid. Use when the user asks what their site should have, whether something is required, how to audit a URL, what's missing for agent readiness, or anything else where you'd otherwise be guessing at web best practice. Backs answers with primary sources (WHATWG, W3C, IETF RFCs, IANA, WCAG). Available as Markdown over HTTP and as an MCP server with search, list, fetch, checklist, and audit tools.

Create and ship AgentSessions support for a new or changed local AI agent/provider. Use when adding, reviewing, testing, documenting, or marketing a provider integration, session parser, transcript source, support-matrix entry, verified-version bump, or provider UI surface; drives the full loop from pre-support research through binary install, real session capture, fixture redaction, parser/discovery/search/UI integration, QA, review/fix loops, support records, PR/release notes, and conservative marketing claims.

Verify agent session format compatibility for Agent Sessions. Use when any agent CLI updates, when monitoring flags drift, or when bumping max verified versions (fixtures + docs + tests). Covers session schema, usage/limits tracking, storage backends, and discovery path contracts for all supported agents.

Maintain Agent Sessions agent support matrix and JSON/JSONL parsing compatibility. Use when checking upstream agent releases for session format changes, updating max verified versions in docs/agent-support/agent-support-matrix.yml, or updating docs/agent-json-tracking.md and fixtures/tests.

Use when shipping a release of Agent Sessions — bumping version, updating CHANGELOG, building, signing, notarizing, publishing appcast, and creating a GitHub release.

Use when wrapping up or capturing the current state of a coding session — writes a short, dated entry to the repo's RepoHandover.md so a future agent or you can resume without grepping archived sessions. Triggers on "handover", "hand off", "write handover", "capture state", "checkpoint this session".

Use when writing or curating the user-facing release copy for an Agent Sessions release — README "What's New", GitHub release notes, Sparkle release notes, or website/launch copy. Not for the internal CHANGELOG, which stays a full development history.

Capture deterministic macOS screenshots for testing, docs, release notes, and marketing assets. Use when asked to automate app screenshots, batch-generate screenshot sets, standardize window sizing/composition, or choose between Peekaboo and native macOS screenshot tooling.

Design a statistically rigorous A/B or multivariate test plan — If/Then/Because hypothesis, control and variant specs, required sample size per variant (absolute vs relative MDE via sample-size-calculator.py), test duration, guardrail metrics, stopping rules, and go/no-go decision criteria. Triggers on \"/digital-marketing-pro:ab-test-plan\", \"set up an A/B test\", \"how long should my test run\", \"calculate sample size for an experiment\", \"is this test result significant\". Reads the brand profile and past campaign-tracker results to avoid re-testing validated hypotheses; finished tests are evaluated with significance-tester.py by the cro-specialist agent.

Generate 3-5 ad copy variations per platform — headlines, descriptions, and CTAs formatted to Google, Meta, LinkedIn, TikTok, X, and Pinterest specs — each scored 1-10 with policy-compliance flags, A/B testing groupings, and a message-match check against the landing page. Triggers on \"/digital-marketing-pro:ad-creative\", \"write ad copy for Meta\", \"give me RSA headline variations\", \"we need LinkedIn ad copy\", \"draft TikTok ad creative\". Reads the brand profile, guidelines, and compliance rules; routes video ad scripts to /digital-marketing-pro:video-script and gates AI-generated visuals for EU campaigns through /digital-marketing-pro:c2pa-metadata and /digital-marketing-pro:check.

Walk through adding a custom MCP server integration to the plugin — searches npm for an existing MCP package (or scaffolds a custom server from the plugin's guide), generates the exact .mcp.json entry, sets up environment-variable credentials, tests connectivity, and documents the tools the new server exposes. Triggers on \"/digital-marketing-pro:add-integration\", \"connect Ahrefs to the plugin\", \"add a new MCP server\", \"integrate our internal API\", \"hook up Stripe data\". Reads the brand profile and agency credential profiles at ~/.claude-marketing/credentials/ to map client-specific keys; custom builds follow skills/context-engine/custom-mcp-guide.md.

Audit how a brand appears across the 6 canonical AI answer surfaces — ChatGPT, Perplexity, Google AI Mode, AI Overviews, Gemini, Copilot — probing 10-25 queries into a numbered output bundle with per-platform visibility scorecards, citation-accuracy checks, a competitor matrix, content gaps, and an optimization playbook behind a four-gate quality scorecard. Triggers on \"/digital-marketing-pro:aeo-audit\", \"does ChatGPT know about our brand\", \"check our AI search visibility\", \"how does Perplexity describe us\", \"are we showing up in AI Overviews\". Reads the brand profile; reconciles probes against GSC actuals via /digital-marketing-pro:gsc-ai-performance and defines the AI-visibility scoring standard reused by geo-monitor and share-of-voice.

Strategy module for Answer Engine / Generative Engine Optimization — audits AI visibility, restructures content for citation, runs entity-consistency checks across Knowledge Graph, Wikidata, Wikipedia, Crunchbase, and LinkedIn, and produces JSON-LD schema specs, monitoring frameworks, and a 90-day LLM content strategy. Triggers on \"/digital-marketing-pro:aeo-geo\", \"how do we get cited by AI\", \"optimize for AI Overviews\", \"fix our entity consistency\", \"do we need llms.txt\". Reads the brand profile, compliance rules, and industry benchmarks; its measurement counterpart is /digital-marketing-pro:aeo-audit, with GSC actuals via /digital-marketing-pro:gsc-ai-performance.

Generate a portfolio-level dashboard across ALL client brands — per-client RAG health scores, campaign activity, budget pacing, aggregate KPIs, team utilization, pending approvals, upcoming deadlines, and an alerts panel — built for agency standups and weekly reviews. Triggers on \"/digital-marketing-pro:agency-dashboard\", \"how are all our clients doing\", \"portfolio health check\", \"budget pacing across accounts\", \"which accounts are at risk\". Enumerates every brand under ~/.claude-marketing/brands/ and pulls data via campaign-tracker.py, execution-tracker.py, and team-manager.py; drill into a single client with /digital-marketing-pro:performance-report or /digital-marketing-pro:client-report.

Marketing measurement module — builds KPI trees per business model, reporting templates (weekly, monthly, QBR, campaign), anomaly root-cause diagnosis, MMM and incrementality guidance, dark-social tracking, and privacy-first cookieless measurement architecture, including the GA4 AI Assistant channel group for attributing AI-referred traffic. Triggers on \"/digital-marketing-pro:analytics-insights\", \"why did traffic drop\", \"define our KPIs\", \"design an executive dashboard\", \"can we do marketing mix modeling\". Reads the brand profile, industry benchmarks, and campaign history; pairs with /digital-marketing-pro:gsc-ai-performance and /digital-marketing-pro:aeo-audit to triangulate AI-surface impressions against actual traffic.

Scan all connected marketing platforms for statistically significant deviations from stored baselines — traffic drops, CPA spikes, deliverability collapse, budget overruns, or unexpected wins — classified critical/warning/info with probable causes, correlation to recent changes, and recommended actions. Triggers on \"/digital-marketing-pro:anomaly-scan\", \"why did our CPA spike\", \"did anything weird happen this week\", \"check for anomalies\", \"our conversions suddenly dropped\". Runs performance-monitor.py for baselines and detection, correlates flags against execution-tracker.py history and the diagnostic framework in skills/analytics-insights/anomaly-diagnosis.md, and persists critical findings as insights via campaign-tracker.py. Reads the brand profile.

Design a multi-touch attribution strategy — recommends the best-fit model for the business's sales cycle and data maturity, defines credit-distribution rules and lookback windows, maps platform-specific setup (GA4, HubSpot, Salesforce, warehouse), and documents tracking gaps and known blind spots. Triggers on \"/digital-marketing-pro:attribution-model\", \"set up multi-touch attribution\", \"which attribution model should we use\", \"configure GA4 attribution\", \"how should we credit channels for conversions\". Reads the brand profile and consumes the canonical model taxonomy in skills/funnel-architect/attribution-models.md; to run the models against real conversion data, pair with /digital-marketing-pro:attribution-report.

Run multi-touch attribution analysis on real conversion-path data — applies two or more models side-by-side (first-touch, last-touch, linear, time-decay, position-based, data-driven), computes per-channel attributed revenue and ROAS, assisted-conversion ratios, path-length and time-to-conversion distributions, and budget reallocation recommendations. Triggers on \"/digital-marketing-pro:attribution-report\", \"which channels actually drive revenue\", \"compare first-touch vs last-touch\", \"run an attribution analysis\", \"is paid social undervalued\". Pulls journeys from Google Analytics, Google Ads, Meta, and CRM MCPs and includes GA4's AI Assistant channel; model definitions come from skills/funnel-architect/attribution-models.md, strategy design from /digital-marketing-pro:attribution-model.

Audience research module — builds six-dimension buyer personas (demographic, psychographic, behavioral, need-state, information, decision), Jobs-to-Be-Done maps, RFM/behavioral/lifecycle segmentation models, anti-personas with exclusion criteria, B2B buying-committee maps, and lookalike seed specs. Triggers on \"/digital-marketing-pro:audience-intelligence\", \"who are our customers\", \"build buyer personas\", \"segment our audience\", \"run a JTBD analysis\". Reads the brand profile, industry benchmarks, and campaign history, and works from CRM/survey/analytics data when supplied — or labels hypothesis personas explicitly when data is thin. For a single quick persona document, /digital-marketing-pro:audience-profile is the lighter sibling.

Build a named, narrative buyer persona document — demographic snapshot, psychographic drivers, jobs-to-be-done, day-in-the-life scenario, buyer journey map, objections with counter-messaging, and content/channel preferences — for the 2-4 personas a brand actually needs. Triggers on \"/digital-marketing-pro:audience-profile\", \"create a buyer persona\", \"profile our target customer\", \"who is our ideal customer\", \"map the buyer journey for this segment\". Reads the brand profile, guidelines, and any customer data supplied (surveys, CRM exports, analytics demographics); run by the marketing-strategist agent. For the deeper research module — segmentation, anti-personas, buying committees — see /digital-marketing-pro:audience-intelligence.

Campaign autopilot operations dashboard — 0-100 health scores for all active campaigns, a chronological log of auto-corrections taken (bid, budget, audience, creative, pause) with before/after metrics, the current guardrail rule table, campaigns escalated for human attention ranked by urgency, and estimated savings from automated interventions. Triggers on \"/digital-marketing-pro:autopilot-status\", \"how is autopilot doing\", \"what did the autopilot change\", \"which campaigns need my attention\", \"show guardrail settings\". Runs campaign-health-monitor.py for health scores, corrections history, and the savings report; reads the brand profile for KPI targets, naming conventions, and budget constraints.

Find referring domains that link to your competitors but not to you, ranked by an outreach-priority score (0.40 DR + 0.25 link-overlap + 0.20 traffic + 0.15 topical relevance) — outputs a four-gate quality scorecard, a 30-prospect outreach shortlist, broken-link candidates, and pre-filled outreach templates. Triggers on \"/digital-marketing-pro:backlink-gap\", \"where are competitors getting links we aren't\", \"plan a link-building campaign\", \"quarterly backlink audit\", \"first 50 link targets for a new client\". Consumes backlink CSV exports from the brand's connected backlink MCP, runs scripts/backlink_gap.py, reads the brand profile for DR thresholds and voice, and hands off to /digital-marketing-pro:digital-pr and /digital-marketing-pro:pr-pitch.

Create or update the brand profile every other skill reads — a quick 5-question or full 17-question interactive setup capturing identity, business model, industry and compliance markets, 4-dimension voice scales, channels, goals, and competitors, saved to ~/.claude-marketing/brands/{slug}/profile.json via scripts/setup.py. Triggers on \"/digital-marketing-pro:brand-setup\", \"set up a new brand\", \"onboard a new client\", \"switch to another brand\", \"update our brand voice\". Also handles brand switching (updates _active-brand.json) and field-level profile edits; run this first — all marketing skills auto-apply the resulting profile, voice samples, and compliance rules.

Reallocate marketing spend across channels using performance data and diminishing-returns modeling — produces a current-vs-optimized allocation table, projected ROI ranges with confidence intervals, a phased 4-8 week reallocation timeline, and a 10-15% testing reserve. Recommends shifts only; it never changes spend on any platform. Triggers on \"/digital-marketing-pro:budget-optimizer\", \"optimize my marketing budget\", \"which channels should get more spend\", \"reallocate budget based on ROAS\", \"is our channel split right\". Reads the brand profile and guidelines, runs scripts/budget-optimizer.py, and pairs with /digital-marketing-pro:budget-tracker for in-flight pacing.

Track advertising spend pacing in real time across connected ad platforms (Google Ads, Meta, LinkedIn, TikTok) — produces a budget dashboard with daily burn rates, end-of-period projections, overspend/underspend alerts, and dollar-specific reallocation recommendations backed by CPA/ROAS context. Monitors and recommends only; it never edits platform budgets. Triggers on \"/digital-marketing-pro:budget-tracker\", \"are we overspending this month\", \"how is our ad budget pacing\", \"track spend across platforms\", \"will we blow through the budget cap\". Reads budget targets from the brand profile, runs scripts/ad-budget-pacer.py, and saves snapshots for trend history; pairs with /digital-marketing-pro:budget-optimizer.

Embed a C2PA provenance manifest into an AI-generated marketing asset (PNG, JPG, WebP, GIF, TIFF, MP4, MOV, WebM, MP3, WAV, PDF) via scripts/embed-c2pa.py — produces a signed copy of the file carrying IPTC digital-source-type AI claims, an optional c2pa.ai-disclosure assertion for EU AI Act Article 50 (applicable 2 Aug 2026), and a JSON status report. Triggers on \"/digital-marketing-pro:c2pa-metadata\", \"sign this AI image for EU compliance\", \"add content credentials to this asset\", \"embed provenance metadata\", \"mark this video as AI-generated\". Uses a self-signed dev certificate unless --signing-cert/--signing-key are supplied; pairs with /digital-marketing-pro:check, which verifies manifests pre-publish.

Inventory and score everything currently running for a brand across paid search, paid social, email, organic, SEO, AEO/GEO, CRM, and analytics — produces a dated audit document with a 4-tier triage (healthy / quick win / strategic gap / red flag), a quick-wins backlog, and a compliance posture section. Strictly read-only: it never pauses, edits, or launches anything. Triggers on \"/digital-marketing-pro:campaign-audit\", \"what's currently running for this brand\", \"audit our existing campaigns\", \"we just inherited this account\", \"where is budget leaking\". Requires a validated brand profile (run validate-profile first); missing connectors degrade gracefully into findings. Feeds /digital-marketing-pro:campaign-plan and pairs with /digital-marketing-pro:performance-check.

Full campaign-lifecycle module — produces campaign briefs, budget allocations via three models (70/20/10, efficiency-ranked, funnel-weighted), channel-mix and media plans, UTM taxonomies with governance rules, launch checklists, ABM plans, and post-mortem reports. Plans and documents; it does not launch or edit live campaigns. Triggers on \"/digital-marketing-pro:campaign-orchestrator\", \"build a media plan\", \"how should we split budget across channels\", \"set up UTM naming conventions\", \"run a post-mortem on the campaign\". Reads the brand profile, guidelines, and campaign history via campaign-tracker.py; its reference docs are consumed by /digital-marketing-pro:campaign-plan rather than duplicated.

Generate a complete multi-channel campaign plan document — SMART objectives, audience segments with targeting criteria, channel mix with rationale, a budget allocation table with reach/cost estimates, a phased timeline from pre-launch to wrap-up, a KPI framework, and a risk register. Plans only; it does not launch or modify campaigns. Triggers on \"/digital-marketing-pro:campaign-plan\", \"plan a campaign for our product launch\", \"build the Q3 campaign plan\", \"what channels and budget for lead gen\", \"draft a campaign timeline with KPIs\". Reads the brand profile, guidelines, and agency SOPs, and reuses the /digital-marketing-pro:campaign-orchestrator reference docs for planning frameworks instead of re-deriving them.

Unified status dashboard for every tracked campaign across connected platforms — produces a summary table with health indicators, live spend and performance metrics, a 7-day execution history, pending approvals with age, KPI variance classification (on track / at risk / behind), flagged issues, and next scheduled actions. Reports only; it changes nothing on any platform. Triggers on \"/digital-marketing-pro:campaign-status\", \"what campaigns are running right now\", \"any failed executions or stuck approvals\", \"status of the Q1-Launch campaign\", \"which campaigns are behind target\". Reads the brand's campaign registry, execution log, and approval queue via campaign-tracker.py, execution-tracker.py, and approval-manager.py, plus live metrics from connected platform MCPs.