본문으로 건너뛰기
cccskills
무료GitHub에 공개

pl-analyze

PL (Project Lead) role sub-skill for incident analysis and process evaluation. Loaded through the pl core routing table, or when the owner explicitly asks for a retrospective or root-cause write-up; NEVER auto-load from ambient context.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

실행 점수 미검증

설치 방법 보기

포함된 파일(2)

  • SKILL.md6.1 KB
  • scripts/selftest/run-selftest.sh1.1 KB

SKILL.md(원문)

설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.

PL · Analyze — incident analysis & process evaluation

Scripts named below run as bash "${PL_SKILL_DIR}/scripts/<name>.sh" ..., where PL_SKILL_DIR is the absolute path of the pl skill directory; set it in the same Bash call. The workspace layout (ai-doc/..., ledgers) is documented in the pl core's State files section; a source that does not exist in this project is skipped and noted in the analysis.

Sub-skill of the PL role, also usable standalone. Words such as journey, callsign, night shift and take-over are defined once, in the pl core's Terms glossary. The rules in this file are the PL role's defaults; a project may choose otherwise.

Invoke when something went wrong — an escaped bug, a stalled run, a run killed at its time limit, an estimate blowup, a protocol breach, a close-audit FAIL — or when the owner ("owner" means whoever owns the work, often the user) asks for a retrospective, a root-cause account of what went wrong, or for periodic process evaluation (patterns in estimate errors, recurring frictions). The deliverable is never just an explanation: it is a verified cause AND a fix landed in the right place. Core iron rules bind unchanged; the owner is addressed in the user's own language.

Method — evidence first, always from disk

Sessions lie and die; disk is the memory. Rebuild the timeline exclusively from:

  • sno reach log --as <your-strict-address> --work <id> — the journey's full message provenance (sno reach export --work <id> --output <path> keeps the full thread bytes); command grammar is defined in ~/.local/lib/sno-reach/current/guide/agent-reach.md
  • ai-doc/JOURNAL/routing-ledger.jsonl — routed/closed/correction events (board_closed lines are written by bash "${PL_SKILL_DIR}/scripts/todo.sh" close; the PL writes the journey's own routed, closed and correction lines by hand)
  • ai-doc/ACTIVE/PL/exec-logs/ + dispatch files under ai-doc/ACTIVE/PL/dispatch/
  • git log/diff of the journey's commits when the project uses git (the fence file lists the paths the journey may touch)
  • the convergence series (the recorded remaining-work counts per fix cycle; bash "${PL_SKILL_DIR}/scripts/convergence-watch.sh" verdict --journey <id>)
  • ~/.local/state/agent-spawns.jsonl + agent-callsigns.jsonl + tmux autopsy (an executor killed at its time limit leaves its session up — read the screen)

Reports, memories, and your own recollection are HYPOTHESES to check against this record, never evidence. Timestamp every step; a contradiction between two sources is a finding, not noise. N different-looking failures arriving together get ONE joined timeline BEFORE any is diagnosed separately — join the timestamps in the target machine's system log first; cascades collapse into one root event and red herrings die at the join — several apparently separate failures are often one shared cause. A status table built from disk beats a report built from a stale card — trust the freshest disk join, not the most confident narrator.

Root-cause classification → fix location

The class DECIDES where the fix lands — never write a note where a mechanism belongs:

ClassSignatureFix lands in
Machinery buga script/skill deterministically did the wrong thingthe skill CODE (script fix + test), then reinstalled
Judgment lapsethe agent saw the signal and chose wrong / waited on a humanMECHANIZE it — an alarm/verdict/gate in scripts (e.g. a stalled-close alarm); a memory note is NOT a fix
Estimation errorestimate vs actual > 2×the est/actual pair → calibration material for agentic-time-estimate (when installed; otherwise a plain note of the pair)
Knowledge trapa known trap, hit againthe lessons file ai-doc/LESSONS.md (written only by the PL; a trap enters the index on its SECOND hit — see the pl core, Lessons file)
Process gapno rule existed for the situationretro proposal → owner approval → skill text patch (never self-edited mid-journey)

Design principle: the machine must lose patience before the owner does — every place the owner had to scold marks a missing mechanical control; an analysis is not done until that control is named (or shown to already exist).

Output shape

Owner-facing report, written in the user's own language, four sections: (1) conclusion — what broke, one line; (2) evidence chain — the 2–4 load-bearing facts, each with its path/command; (3) where the fix lands — the fix and its location per the table above; (4) recurrence prevention — the check/alarm/rule that now exists. Agent-facing: the same content as an English postmortem section in the journey's journal entry, plus ledger correction events wherever the durable record was wrong.

Routing the lessons

A finished analysis routes each lesson to the file that OWNS the behavior: dispatch-time rules → pl-dispatch; in-flight controls and alarms → pl-watch; close/probe rules → pl-audit; environment checks → pl-env; board/memory/Reach-substrate law → the pl core. A lesson without a named owner file is not landed.

Boundaries

  • Script/machinery fixes land directly ONLY within the core iron-rule-1 exception: PL-owned control scripts under the pl skill's scripts/, with a test, then reinstalled and reported to the owner. Anything else that needs code — repo source, other skills' scripts — is dispatched to an executor, never hand-edited. SKILL text changes still ride the retro → owner-approval path (core boundary).
  • Estimation analysis only FILES the calibration pair — rate/multiplier changes belong to the estimator skill's own validation loop (when installed).
  • Analysis of a journey never reopens settled owner rulings; if the cause traces to a ruling, report the trace, don't relitigate.

리뷰

아직 리뷰가 없습니다. 사용해 본 소감을 남겨 주세요.

댓글

로그인 없이 쓸 수 있습니다. 사용 후기, 질문, 작동하지 않은 환경 등을 편하게 남겨 주세요.

아직 댓글이 없습니다. 첫 댓글을 남겨 보세요.

보내면 바로 공개됩니다. 이메일 주소나 전화번호 같은 개인 정보는 쓰지 마세요. URL은 2개까지입니다.

0 / 1,000자

이 양식은 reCAPTCHA로 보호되며 Google 개인정보처리방침 및 서비스 약관이 적용됩니다.

같은 저장소의 스킬

Answer how long agent work will take, how much longer active work needs, or what clock time applies. Estimate full AI-agent wall time, measure active progress first, and get owner-facing times from `sno report-time`. Never estimate human-developer time.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

sno-ai/sno-station-skills7622026년 10월 24일 업데이트

Prove that a reported run caused its intended real effect. Walk the executor path from entry to effect, capture one observable per hop, and report a result a no-op could not produce.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

sno-ai/sno-station-skills7622026년 10월 24일 업데이트

Give the owner one page when they come back from sleep, a meeting or a long run: what got done, what is stuck, what waits for their answer, and what it cost in quota. Use when the owner asks what happened while they were away, wants a status page, or when you finish a long stretch of work and must report it.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

sno-ai/sno-station-skills7622026년 10월 24일 업데이트

Report how many problems the two brains found: mutual review (the other model checks the work, from the peer-review records) and self-check (an agent finds its own error in its own conversation, judged by that agent's own model), side by side. Use when the owner asks whether the second agent or the reviews are worth it, or wants the week's review results.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

sno-ai/sno-station-skills7622026년 10월 24일 업데이트

charter

무료

Write, revise or review a charter: the one document that tells an executor what to deliver, what is out of bounds, what the owner already decided, and how each success check will be proven. Use when work needs a written brief before someone else does it, or when asked to write, change or check a charter.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

sno-ai/sno-station-skills7622026년 10월 24일 업데이트

Route production coding tasks from Claude Code to the user-level Codex Shell, TypeScript, Python, or Rust workflow through a direct `codex exec` run. Use for "codex-coder", "ask Codex to write code", "generate with Codex", language migrations, or mixed Shell/TypeScript/Python/Rust implementation.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

sno-ai/sno-station-skills7622026년 10월 24일 업데이트

sno-ai의 스킬 모두 보기

이 스킬의 문제 신고