Claude Code Skills · page 95

Collect and synthesize opinions from multiple AI agents. Use when users say "summon the council", "ask other AIs", or want multiple AI perspectives on a question.

This skill should be used when the user is building, planning, or strategizing and the key question is whether to optimize content (what) or change form (how/medium). Trigger on "내용 vs 형식", "content vs form", "metamedium", "형식을 바꿔볼까", "새로운 포맷", "관점 전환", "perspective shift", "다른 방법 없을까", "같은 방식이 안 먹혀", "diminishing returns". Applies Alan Kay's metamedium concept to surface form-level alternatives. For requirement clarification use vague; for strategy blind spots use unknown.

This skill should be used when the user provides a strategy, plan, or decision document and wants to surface hidden assumptions and blind spots using the Known/Unknown 4-quadrant framework. Trigger on "known unknown", "4분면 분석", "blind spots", "뭘 놓치고 있지", "뭘 모르는지 모르겠어", "전략 점검", "전략 분석", "assumption check", "가정 점검", "quadrant analysis", "what am I missing". Strategy-level blind spot analysis with hypothesis-driven questioning. For requirement clarification use vague; for content-vs-form reframing use metamedium.

This skill should be used when the user's request or requirement is ambiguous and needs iterative questioning to become actionable. Trigger on "clarify requirements", "refine requirements", "요구사항 명확히", "요구사항 정리", "뭘 원하는 건지", "make this clearer", "spec this out", "scope this", "/clarify". Turns vague inputs into concrete specs. For strategy blind spots use unknown; for content-vs-form reframing use metamedium.

개발 커뮤니티에서 기술 주제에 대한 다양한 의견 수집. "개발자 반응", "커뮤니티 의견", "developer reactions" 요청에 사용. Reddit, HN, Dev.to, Lobsters 등 종합.

This skill should be used when the user asks to "기술 의사결정", "뭐 쓸지 고민", "A vs B", "비교 분석", "라이브러리 선택", "아키텍처 결정", "어떤 걸 써야 할지", "트레이드오프", "기술 선택", "구현 방식 고민", or needs deep analysis for technical decisions. Provides systematic multi-source research and synthesized recommendations.

This skill should be used when the user asks to "트윗 가져와", "트윗 번역", "X 게시글 읽어줘", "tweet fetch", "트윗 내용", "트윗 원문", or provides an X/Twitter URL (x.com, twitter.com) and wants to read, translate, or analyze the tweet content. Also useful when other skills need to fetch tweet text programmatically.

This skill should be used when the user asks to "check email", "read emails", "send email", "reply to email", "search inbox", or manages Gmail. Supports multi-account Gmail integration for reading, searching, sending, and label management.

Google 캘린더 일정 조회/생성/수정/삭제. "오늘 일정", "이번 주 일정", "미팅 추가해줘" 요청에 사용. 여러 계정(work, personal) 통합 조회 지원.

Interactive markdown review with web UI. Use when user says "review this", "check this plan", "피드백", "검토해줘" or specifies a file path to review.

This skill should be used when the user asks to "카톡 보내줘", "카카오톡 메시지", "KakaoTalk message", "채팅 읽어줘", "~에게 메시지 보내줘", or needs to send/read messages via KakaoTalk on macOS.

Generate Korean podcast episodes from any source (URLs, tweets, articles, PDFs) — analyzes content, writes a script, generates audio via OpenAI TTS, converts to MP4, and auto-uploads to YouTube. Use this skill whenever the user says 'make a podcast', 'convert to podcast', 'podcast', 'create an episode', 'turn this into audio', 'YouTube podcast', 'turn this article into a podcast', 'publish as audio', or provides sources and wants them transformed into a listenable format. Supports partial execution: script-only, TTS-only, or upload-only.

This skill should be used when user wants to access, capture, or reference Claude Code session history. Trigger when user says "capture session", "save session history", or references past/current conversation as a source - whether for saving, extracting, summarizing, or reviewing. This includes any mention of "what we discussed", "today's work", "session history", or when user treats the conversation itself as source material (e.g., "from our conversation").

This skill should be used when the user asks to "analyze session", "세션 분석", "evaluate skill execution", "스킬 실행 검증", "check session logs", "로그 분석", provides a session ID with a skill path, or wants to verify that a skill executed correctly in a past session. Post-hoc analysis of Claude Code sessions to validate skill/agent/hook behavior against SKILL.md specifications.

This skill should be used when the user asks to "wrap up session", "end session", "session wrap", "/wrap", "document learnings", "what should I commit", or wants to analyze completed work before ending a coding session.

Analyze tasks and dynamically assemble expert agent teams using Claude Code's TeamCreate API. Scouts your codebase, designs optimal agents, and executes with validation.

This skill should be used when the user asks to "유튜브 정리", "영상 요약", "transcript 번역", "YouTube digest", "영상 퀴즈", or provides a YouTube URL for analysis. Extracts transcript, generates summary/insights/Korean translation, and tests comprehension with 9 quiz questions across 3 difficulty levels. Optional Deep Research for web-based follow-up.

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Answer prediction questions using market trading data, not opinions. Use when the user asks probability questions about geopolitics, economics, markets, industries, or any topic where real money is being traded on the outcome. Examples: 'What's the probability of WW3?', 'Will there be a recession?', 'Is AI in a bubble?', 'When will the Russia-Ukraine war end?', 'Is it a good time to buy gold?', 'Will SPY drop 5% this month?', 'Is NVDA options premium overpriced?'. The skill reads prices from prediction markets, commodities, equities, options chains, derivatives, yield curves, and currencies, then cross-validates multiple signals to produce a structured probability report.

Implement a Linear ticket end-to-end - fetch context, branch, code, tests, draft PR, update the ticket. Use when given a ticket ID like EVL-86 or DEP-123.

Generate the "How to test" section for the current branch's changes - exact commands, flows to exercise, expected results. Use before opening/updating a PR or when asked how to validate a change.

Use when the user wants a less default, more original solution for code, design, writing, naming, architecture, debugging, or strategy, especially when progress is stuck.

Use when the user wants a distinctive, production-ready frontend interface with clear visual direction, strong hierarchy, and polished interaction details.

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.

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.

Run the unified pre-publish quality gate on marketing content — wraps scripts/eval-runner.py to score hallucination risk, claim substantiation (with --evidence), brand-voice fit (with --brand), structure (with --schema), content quality, and readability, plus a C2PA provenance check for AI assets in EU-targeted campaigns; returns a composite score with a PASS / WARN / BLOCKED decision and per-issue fix suggestions. Reports only — it never edits the content. Triggers on \"/digital-marketing-pro:check\", \"is this safe to publish\", \"run a hallucination check on this draft\", \"validate this copy against the brand voice\", \"pre-publish quality gate\". Resolves the active brand profile automatically; pairs with /digital-marketing-pro:c2pa-metadata to fix missing manifests.

Run a multi-dimensional competitive teardown of 2-5 competitors — content strategy, SEO, paid ads, social, AI answer-engine visibility, and pricing/positioning — producing a competitor overview matrix, per-competitor SWOT, gap analysis, and strategic recommendations prioritized by opportunity size. Triggers on \"/digital-marketing-pro:competitor-analysis\", \"analyze our competitors\", \"how do we stack up against X\", \"competitive landscape report\", \"what are competitors doing that we aren't\". Point-in-time analysis, not ongoing tracking — pair with /digital-marketing-pro:competitor-monitor for that. Reads the brand profile, guidelines, and compliance rules.

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.

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.

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.

Build a complete case-study creation blueprint — a Challenge-Solution-Results narrative framework, 15-20 client interview questions plus 10 internal-team questions, a data-visualization plan, format specifications (PDF, web page, slide deck, video script outline, social snippets, sales one-pager), a distribution strategy, a permission/approval checklist, and a draft executive summary. Plans the case study; it does not produce the finished designed asset. Triggers on \"/digital-marketing-pro:case-study-plan\", \"turn this client win into a case study\", \"what should we ask the client in the interview\", \"plan a success story for sales enablement\", \"case study formats and distribution plan\". Reads the brand profile, guidelines, custom templates, and agency SOPs.

Score customer segments for churn risk from behavioral signals — email engagement decline, purchase recency, usage drops, support sentiment — producing a 0-100 risk scorecard with four tiers, per-tier intervention playbooks (actions, timing windows, channels, messaging), LTV-at-risk totals, and retention-ROI prioritization. Assesses and recommends; it does not send outreach or launch campaigns. Triggers on \"/digital-marketing-pro:churn-risk\", \"which customers are about to churn\", \"score our segments for churn risk\", \"email engagement is dropping, who is at risk\", \"build a retention intervention plan\". Pulls behavioral data from a connected CRM MCP (Salesforce or HubSpot) or user-provided exports, runs scripts/churn-predictor.py, and reads the brand profile for lifecycle context.

Generate a complete onboarding package for a new marketing client — kickoff meeting agenda, 20-30 question discovery questionnaire, stakeholder map with RACI matrix, platform-by-platform access checklist, 30-60-90 day milestone plan, communication cadence, escalation protocol, welcome email template, internal team brief, risk register, and a day-by-day first-week action plan. Triggers on \"/digital-marketing-pro:client-onboarding\", \"we just signed a new client\", \"build a kickoff agenda and discovery questionnaire\", \"30-60-90 day plan for the new account\", \"what access do we need from the client\". Reads the brand profile, guidelines, custom templates, and agency SOPs so the package matches house process.