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Analyze bug-fix commits in a Git repository to uncover recurring failure patterns. Produce individual bug analyses and a reusable pattern guide that removes project-specific details, helping inform future code reviews.
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Expert in understanding and navigating legacy codebases. Covers code archaeology techniques, finding hidden knowledge, mapping dependencies, and extracting understanding from code without documentation. Knows how to read the stories that old code tells. Use when "legacy code, old codebase, understand this code, archaeology, what does this do, no documentation, ancient code, " mentioned.
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Trace how AI tooling developed in a Git repository by identifying configuration file origins, monthly commit activity, major changes, and relevant releases. Produce a Markdown report that explains adoption phases and turning points using Git evidence.
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Use when a task needs the judgment of a postsecondary Anthropology or Archaeology professor — designing a gen-ed or major-track course, building a tenure or promotion dossier, defending a department against an enrollment-driven program review, mentoring a graduate student's fieldwork grant application, or writing an IRB/NAGPRA-compliant fieldwork plan.
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Systematic framework for resurrecting and modernizing legacy codebases through archaeology, resurrection, and rejuvenation phases. Activate on "legacy code", "inherited codebase", "no documentation", "technical debt", "resurrect", "modernize". NOT for greenfield projects or well-documented active codebases.
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Chief Security Officer mode. Infrastructure-first security audit: secrets archaeology, dependency supply chain, CI/CD pipeline security, LLM/AI security, skill supply chain scanning, plus OWASP Top 10, STRIDE threat modeling, and active verification. Two modes: daily (zero-noise, 8/10 confidence gate) and comprehensive (monthly deep scan, 2/10 bar). Trend tracking across audit runs. Use when: "security audit", "threat model", "pentest review", "OWASP", "CSO review".
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When a bug or regression is reported, the FIRST step — before fixing — is to research how and when it was introduced (git archaeology), then record that finding on the tracking issue/PR. Use the moment a bug, error, broken build, or "this used to work" is reported, before writing a fix. Produces a first-bad-commit (or "not a code regression") note you paste onto the issue.
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The case-law chronicle of this repo's major failures — symptom, root cause, evidence, status, and the rule each battle minted — so nobody re-fights a settled battle or re-proposes a rejected fix. Use when a bug/incident looks familiar ("didn't this happen before?"), when you're about to propose an approach that may have been tried and rejected (e.g. shipping a migration from a package), when someone asks "why does rule X exist" / "why is done-by-lgtm a thing" / "what was incident 988", or when a bug-archaeology run hits the shallow-clone wall and needs the pre-June-2026 history. Trigger phrases: "has this broken before", "why can't packages ship migrations", "why did graduation fail", "why is the green-build lesson a rule", "what did we learn from incident NN". (For a live "build is green but the app is broken" symptom, start at crouton-diagnostics-index. NB keep this description free of any space-then-hash sequence — an unquoted YAML scalar treats space-hash as a comment start and silently truncates.)
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Systematic framework for resurrecting and modernizing legacy codebases through archaeology, resurrection, and rejuvenation phases. Activate on "legacy code", "inherited codebase", "no documentation", "technical debt", "resurrect", "modernize". NOT for greenfield projects or well-documented active codebases.
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Read existing code to reconstruct system specifications, including architecture, data flows, APIs, and business rules. Produces documentation tied to code locations, separating observed behavior from inferences and questions that need clarification.
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Prepare evidence for a talk about project experience by comparing git history, release notes, and a source summary. Produce commit and contributor metrics, release analysis, and a factual timeline that flags conflicting or unverified information.
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Use when positioning an American Anthropologist (AA) manuscript against the literature so it reads as a four-field contribution. AA readers span sociocultural, archaeology, biological, and linguistic anthropology, so the paper must engage the conversations they expect and decenter a US/Anglophone-only canon. Stakes the contribution and citational politics; it does not write the lit review.
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Use when starting or navigating any American Anthropologist (AA) manuscript as the entry point. Routes to the right AA sub-skill based on lifecycle stage, subfield (sociocultural, archaeology, biological, linguistic), and section type (Research Article, Vital Topics Forum, World/Public/Multimodal Anthropologies, Review Essay). It dispatches; it does not draft content.
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Use when deciding whether an anthropology project fits American Anthropologist (AA) and which section to target. AA is the four-field AAA flagship, so the test is significance across anthropology (sociocultural, archaeology, biological, linguistic), not single-subfield novelty. Helps frame the question and pick a section; it does not collect data or conduct fieldwork.
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Use when defending the research design of an American Anthropologist (AA) manuscript — fieldwork and participant observation, interview and archival design for ethnographic/qualitative work, material/contextual analysis for archaeology, and appropriate lab/quantitative design for biological anthropology. AA judges each mode on its own terms with reflexivity. Strengthens the design; it does not write code or run fieldwork.
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Infrastructure-first security audit across 14 phases: attack surface mapping, secrets archaeology (git history), supply chain analysis, CI/CD pipeline security, LLM/AI threat detection, OWASP Top 10, STRIDE threat modeling. Confidence gates, exploit scenarios, parallel verification, trend tracking. Read-only.
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Long-running iterative cycle — one entry, seven modes (debug|improve|planex|review|security|defensive|ui). Shared archaeology/audit → fix → verify → generalize → discover → persist skeleton with mode-specific dimensions. User-invoked campaign entry; single-step fixes route via /maestro-next
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Chronicles settled battles, reverts, and dead ends. Use when a fix echoes a past failure. Do not use for live triage; use night-market-debugging-playbook.
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Search and record project memory (Discussions, journal, ADRs). Use before re-investigating anything. Do not use for settled battles; see failure-archaeology.
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Evidence-driven architecture research for understanding real systems and making technical decisions. Use when doing architecture landscape studies, source-backed system archaeology, build-vs-buy or adopt/adapt/build decisions, open-source and commercial comparisons, revisiting an earlier architecture choice, or handling requests such as 架构调研, 架构选型, 竞品架构, 技术尽调, 同类方案, 开源替代, how is X built, and what should we learn from X. Do not use for small mechanical changes, market-only discovery, or detailed design after the technology direction is already fixed.
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Git expert combining atomic commits, rebase/squash, and history search (blame, bisect, log -S). Use for any git operations requiring structured commit strategies, history rewriting, or code archaeology. Triggers: 'commit', 'rebase', 'squash', 'who wrote', 'when was X added', 'find the commit that'.
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Investigating git history, analyzing regression root causes, and performing code archaeology. Time-travels through commits to uncover truth. Use for git history investigation.
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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). 🇨🇳 你的竞品刚刚爆了。你完全不知道他们怎么做到的。该拆官网？盯社媒？画增长飞轮？这份 SOP 给你从 Wayback Machine 快照到 X/Twitter 传播链路还原到飞轮六阶段评分的完整方法论。基于 150+ AI 创业公司竞品分析实战，含 Lovable 完整案例。 🇯🇵 競合が突然バズった。どうやって成長したか全く分からない。このSOPは、Waybackスナップショットからツイッター伝播チェーン分析、フライホイール6段階スコアリングまでの完全な競合調査フレームワークを提供します。150以上のAIスタートアップ分析から構築。 🇰🇷 경쟁사가 갑자기 폭발적으로 성장했습니다. 어떻게 그렇게 빨리 성장했는지 모릅니다. 이 SOP는 Wayback 스냅샷부터 X/Twitter 전파 체인 분석, 플라이휠 6단계 스코어링까지 완전한 경쟁사 조사 프레임워크를 제공합니다. 150개 이상의 AI 스타트업 분석에서 구축. Triggers: "competitor research" | "competitive analysis" | "competitor analysis" | "growth flywheel" | "market research" | "竞品调研" | "竞品分析" | "增长飞轮" | "传播链路" | "競合分析" | "경쟁사 분석" | "social media teardown" | "Twitter propagation" | "content strategy analysis"
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Mine past agent sessions for working prompts, decisions, and patterns. Use when "what did I ask?", "find that prompt", session archaeology, or agent history.
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