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Found 86 Skills
Use this agent when you need to understand the historical context and evolution of code changes, trace the origins of specific code patterns, identify key contributors and their expertise areas, or analyze patterns in commit history. This agent excels at archaeological analysis of git repositories to provide insights about code evolution and development patterns. <example>Context: The user wants to understand the history and evolution of recently modified files.\nuser: "I've just refactored the authentication module. Can you analyze the historical context?"\nassistant: "I'll use the git-history-analyzer agent to examine the evolution of the authentication module files."\n<commentary>Since the user wants historical context about code changes, use the git-history-analyzer agent to trace file evolution, identify contributors, and extract patterns from the git history.</commentary></example> <example>Context: The user needs to understand why certain code patterns exist.\nuser: "Why does this payment processing...
Stencil & Tablet — Bone paper with stencil-cut headlines and a six-color earth palette: archaeology meets brand. Anything that should feel archival, tactile, and weighty-graphic: museum and cultural-institution decks, art / architecture brands, longform research, heritage and craft brands, manifestos.
Audit how a brand appears in AI-powered search (ChatGPT, Perplexity, Claude, Gemini). Use when user mentions "AI search," "how do I show up in ChatGPT," "AI discoverability," "AEO," "LLM visibility," or wants to understand their brand's AI presence.
Your competitor just launched. You have no idea how they grew so fast. Should you reverse-engineer their website? Track their social media? Map their growth flywheel? This gives you the complete SOP — from Wayback Machine snapshots to X/Twitter propagation analysis to growth flywheel scoring. Built from 150+ AI startup competitive analyses including the Lovable case study (4.3M views, 229K impressions launch day). By @WeiYipei. Inside: 4-step research framework (website → social → traffic → ad spend) · 3-version Wayback evolution analysis · X/Twitter propagation-chain mapping · growth-flywheel 6-stage scoring · KOL identification · content-effectiveness ranking · ICP + freemium pricing teardown · self-check checklist. New in 1.2: time-machine competitor archaeology — Google date-range search back to pre-fame years, star-history launch-day reconstruction (AppFlowy ~40-channel zero-budget launch teardown), competitor funding news as a launch-timing signal, and the "open source alternative" positioning play. Sourced from first-hand founder interviews (AFFiNE co-founder, 2026-04, self-reported figures marked). 🇨🇳 Your competitor just blew up. You have no idea how they did it. Should you tear down their website? Monitor their social media? Map their growth flywheel? This SOP provides you with a complete methodology from Wayback Machine snapshots to X/Twitter propagation chain reconstruction to 6-stage growth flywheel scoring. Built from 150+ AI startup competitive analysis practices, including the full Lovable case study. 🇯🇵 Your competitor suddenly went viral. You have no idea how they grew. This SOP provides a complete competitive research framework from Wayback snapshots to Twitter propagation chain analysis to 6-stage flywheel scoring. Built from analysis of over 150 AI startups. 🇰🇷 Your competitor has suddenly grown explosively. You don't know how they grew so fast. This SOP provides a complete competitor research framework from Wayback snapshots to X/Twitter propagation chain analysis to 6-stage flywheel scoring. Built from analysis of over 150 AI startups. Triggers: "competitor research" | "competitive analysis" | "competitor analysis" | "growth flywheel" | "market research" | "competitor research" | "competitive analysis" | "growth flywheel" | "propagation chain" | "competitive analysis" | "competitor analysis" | "social media teardown" | "Twitter propagation" | "content strategy analysis"
The single entry point into the daily-work arc — a hybrid menu/argument router. Trigger whenever the user invokes /dev-workflows:daily, types /daily, asks "what should I do now", "where was I", "start my day", "what's next today", "wrap up my day", "end my day", or seems unsure which skill fits their current daily-work moment. Bare invocation shows a 5-station menu (starting / working / filing findings / reporting / wrapping up); with an argument (start | work | file | report | wrap, synonyms accepted) it jumps straight to the station and hands off to the right skill: ado-backlog:my-work, the situational toolbox (grill-then-plan, debug-mantra, study-design-verify, naming-audit, fit-gap-analysis, problem-description, ticket-trace, scrutinize, review-pr, dual-verifier, drive-to-legacy, crm-archaeology, generating-test-cases), findings-to-ado-backlog or ado-create-work-items (github-backlog twins on request), management-talk, or invoice-generator.
Audits and fixes technical SEO, researches search demand, creates content briefs, and measures SEO/AEO performance. Use when asked to "audit SEO", "fix indexing", "improve AI visibility", "check keyword volume", "write an SEO brief", or "why did organic traffic drop". For writing the article use ghostwriter; for visual redesign use ui-design.
Use this skill to reverse-engineer an existing software system, map its architecture, data flow, privacy posture, coupling, quality characteristics, and feature surface, then produce an evidence-grounded clean-room design document, PRD, or migration plan under new constraints. Use for codebase archaeology, implicit contract extraction, architecture health assessment, or decomposition-readiness analysis. Do not use for greenfield architecture design, direct code review, bug hunting, security auditing, or implementation of API, data, platform, or migration changes; route those to the relevant neighboring skill.
Use to get a brand and its content CITED and RECOMMENDED by AI answer engines — the GEO (Generative Engine Optimization) / AI-search-visibility skill. Run when the user says "GEO," "get cited by ChatGPT/Perplexity/Google AI," "ChatGPT SEO," "LLM SEO / LLMO," "AI Overviews," "answer engine optimization (AEO)," "will AI recommend my brand," or wants to show up in AI-generated answers, not just the feed or Google links. Reads brand-profile and audience first. AI engines retrieve + fan-out and cite community/social sources heavily (Reddit, YouTube, Wikipedia); platforms disagree; earned media beats product pages; extractable, fresh content drives citation. Covers the four GEO levers (retrievable, earned mentions, extractable content, entity clarity), authentic social plays, and the manual prompt-audit method. Refuses astroturfing; never fabricates "share of voice." Sibling of social-seo (platform + Google search). Judges via the audit + GEO tools + AI referral traffic.
Audits and fixes both halves of being found — classic search and AI answers. Search side: rankings, site structure, page speed, titles and descriptions, and content that answers what customers search for. AI side: whether assistant crawlers can reach the site, pages render without JavaScript, an llms.txt file, schema.org data, and presence in the directories assistants read. For online stores it also runs a bulk catalog cleanup, rewriting product titles, descriptions, alt text, and SEO fields in approved batches. Works on any public website with no connector. Use this whenever the owner talks about being found online, including "nobody finds us on Google," "how do I show up in ChatGPT," "make my website AI friendly," "SEO audit," "GEO," "AEO," "AI search visibility," "my competitor ranks above me," "fix my product listings," or "I asked an AI about my industry and we weren't in the answer."
Room-based exploration with narrative evidence collection
Use when creating brand names, product names, or company names in 2026. Use when naming needs to work across languages, voice assistants, and AI recommendation engines. Use when previous name candidates failed trademark or had conflicts.
Investigate why code or a design decision exists. Use for rationale, rejected alternatives, regressions, postmortems, historical constraints, and evidence-backed thresholds; use how for runtime behavior.