본문으로 건너뛰기
cccskills
무료GitHub에 공개

rseng-lessons-learned

Covers capturing and reusing what a project learns: a lessons-learned record fed from debugging sessions, code review findings, failed and successful research approaches, incidents and near-misses; blameless postmortems for the big ones; retrospectives on a cadence; and routing each lesson into the artifact that prevents its repetition (test, doc, checklist, onboarding note). Use PROACTIVELY when a nontrivial bug is fixed, a review uncovers a recurring pattern, an approach is abandoned, or an incident is resolved - and when the user asks to record a lesson, run a retrospective or postmortem, wants a LESSONS or NOTES file, or asks why the same mistake keeps happening.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

fdiblen/rseng-agent-skills스타 24(GitHub 24 + cccskills 0)

실행 점수 미검증

설치 방법 보기

포함된 파일(2)

  • SKILL.md6.2 KB
  • references.md310 B

SKILL.md(원문)

설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.

Lessons learned and project memory

Every project pays tuition - the bug that took three days, the approach that silently failed, the review comment made for the fourth time. The tuition is only wasted if the lesson evaporates. This skill is the capture mechanism: write the lesson down at the moment it is learned, in the repository, and then - the step most teams skip - route it into an artifact that makes repetition impossible. A lessons file nobody acts on is a diary; a lesson that became a test is institutional memory.

Capture at the trigger moments

The lesson is freshest and cheapest at the moment of resolution. Capture proactively - offer, in one line, to record it - when:

  • A nontrivial bug is fixed: what the symptom was, what the cause turned out to be, what evidence found it, what would have prevented it (rseng-debugging's evidence trail is the draft).
  • A code review surfaces a recurring pattern: the third time the same class of comment appears, it is a lesson (and a candidate lint rule or checklist line - rseng-pair-programming).
  • A research approach is abandoned: WHY it lost - data, method, numerics, scaling - so the next person (or the same person, next year) does not re-walk the dead end. Negative results are project knowledge (rseng-software-reuse records reuse decisions the same way).
  • An incident or near-miss resolves: data almost lost, wrong results almost published, credential exposed - these get the fuller postmortem below.
  • Something WORKED surprisingly well: lessons are not only failures; a practice worth repeating is worth recording.

The record: small, structured, in the repo

  • One file, versioned with the code (LESSONS.md or docs/lessons/): dated entries, newest first; each entry a few lines - context, what happened, the lesson, and the action taken. Searchability beats elegance; write plainly.
  • Tag entries by theme (numerics, data handling, process, dependency...) so patterns become visible when three entries share a tag - a themed cluster is a systemic issue asking for a systemic fix.
  • Personal research notes scale down the same way: a dated notebook of decisions and observations per analysis (rseng-notebooks' narrative side) - the lab-notebook habit applied to computation.
  • Keep it honest and blame-free in wording: lessons name causes and defenses, never culprits - a record people fear lands in performance reviews stops being written (rseng-honesty's blame-free framing; rseng-ai-declaration records agent-contributed lessons like any other).

Postmortems and retrospectives

  • Blameless postmortem for the big ones (the SRE practice transfers whole): timeline of what happened, contributing causes (plural - single-cause stories are usually wrong), what limited or worsened the impact, and concrete prevention actions WITH owners in the tracker (rseng-project-tracking). Blameless is the load-bearing word: the goal is that the next person in the same position, with the same information, does better - not that someone is sorry.
  • Retrospectives on a cadence: at milestones or each planning cycle, three questions - keep doing, stop doing, start doing - fed by the lessons file since last time (structured formats and exercises exist when a team wants variety). Fifteen minutes is enough; skipping it is how the lessons file goes stale.

Route the lesson into an artifact

The closing discipline - every lesson ends with "and therefore", picking the artifact that prevents repetition:

  • A bug lesson becomes a regression test (rseng-testing) or a defensive check (rseng-defensive-coding).
  • A review-pattern lesson becomes a lint rule (rseng-code-quality), a checklist line, or a PR-template question.
  • A process lesson becomes a tracker template or a cadence change (rseng-project-tracking).
  • A gotcha lesson becomes a documentation warning (rseng-documentation) or an onboarding note - the future colleague reads the lesson where they will hit the problem, not in an archive.
  • A dead-end lesson becomes a paragraph in the methods notes or the decision log, so the paper and the successors know what was tried (rseng-research-integrity values the recorded negative).

An agent applying this skill closes the loop in one motion: record the lesson AND draft the artifact, then offer both.

Working with this skill

This skill is source-independent: it adapts blameless-postmortem and retrospective practice to research software work. It is fed by rseng-debugging, rseng-pair-programming and rseng-project-tracking, and it feeds rseng-testing, rseng-documentation and rseng-trainer (lessons are tomorrow's teaching material).

Learn more (verified):

Recorded lessons are raw material for the project's build story (rseng-trainer): the mistakes worth learning from belong in the teaching document, not only the log.

<!-- related-skills:begin -->

Related skills

Check whether any of these applies before moving on:

  • rseng-code-quality - review-pattern lessons become lint rules
  • rseng-debugging - bug fix evidence drafts the lesson
  • rseng-documentation - gotcha lessons become doc warnings
  • rseng-project-tracking - postmortem actions get tracked owners
  • rseng-testing - bug lessons become regression tests
  • rseng-trainer - lessons become tomorrow's teaching material
<!-- related-skills:end -->

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

Covers operating AI coding agents securely: auditing whether the agent runs sandboxed, permission and dangerous-command configuration, containerized environments, secrets kept out of context, network egress and token privileges, prompt-injection risk, and the privacy of agent use itself. Use PROACTIVELY when permission gating is broadly disabled (skip-permissions modes), when secrets are visible to the agent's shell, or when the agent processes untrusted code, issues or web content; when a project holds personal or confidential data while a cloud agent is active (what leaves the machine, retention, training use), or when the user asks how to run coding agents safely or privately. (Supply-chain security: rseng-security; disclosing agent contributions: rseng-ai-declaration.)

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

fdiblen/rseng-agent-skills242026년 9월 23일 업데이트

Covers declaring AI involvement with the AI Declaration Format (https://ai-declaration.org): creating and maintaining an aidecl.yaml that records which AI tools and agents were used, what each contributed, when and how much, plus the README disclosure footnote, JSON/JSON-LD export and CI validation. Machine-readable disclosure is the practice; this format is one implementation. Use PROACTIVELY when an AI assistant or agent creates or modifies a project's content - generate aidecl.yaml if missing, update it after AI-assisted changes. Also use when the user asks about AI transparency, provenance, disclosure or EU AI Act disclosure, wants to state that NO AI was used, or mentions aidecl. For concealment requests see rseng-honesty; for the human review duty see rseng-human-verification.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

fdiblen/rseng-agent-skills242026년 9월 23일 업데이트

Covers long-term archiving of research software and data: Software Heritage save requests and SWHID persistent identifiers, Zenodo deposits with versioned DOIs and forge integration, choosing domain and institutional repositories, deciding what to archive (code, data, environments, documentation) and when, and archiving at project retirement. Use when the user wants software or data preserved beyond the life of a forge account, grant or lab, mentions Software Heritage, SWHIDs, Zenodo deposits or archiving, retires or hands over a project, prepares artifacts that must stay resolvable for a paper, or when funder policy requires long-term preservation. (Cutting the release itself is rseng-publishing-releasing; day-to-day data storage and repository choice is rseng-data-management.)

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

fdiblen/rseng-agent-skills242026년 9월 23일 업데이트

Covers processing research data that outgrows one machine's memory: out-of-core and chunked computation, Dask for scaling the scientific Python stack, Spark for distributed tabular pipelines, lazy evaluation, partitioning strategies, idempotent and restartable batch jobs, and knowing when NOT to distribute. Use when datasets no longer fit in memory, when the user mentions Dask, Spark, out-of-core or larger-than-memory data, when a pandas/NumPy workflow hits memory limits, or when designing batch pipelines over many files. (Cluster job submission and job arrays are rseng-hpc-computing; pipeline orchestration engines are rseng-workflows; profile first with rseng-performance-profiling.)

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

fdiblen/rseng-agent-skills242026년 9월 23일 업데이트

Covers continuous integration and delivery for research software: CI/CD concepts, automating builds and tests with GitHub Actions and GitLab CI/CD, and wiring an organization's GitLab CI infrastructure to a GitHub-hosted project. Use when the user asks to set up CI, write a pipeline or workflow file (.github/workflows, .gitlab-ci.yml), add automated builds/tests on push or pull request, publish a package or deploy docs when a tag or release is cut, cache dependencies to speed up CI runs, choose between GitHub Actions and GitLab CI, use self-hosted or GPU runners, mirror a repo, or report external CI status back to GitHub. For what to test and shrinking a test matrix see rseng-testing; for linter and pre-commit policy see rseng-code-quality.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

fdiblen/rseng-agent-skills242026년 9월 23일 업데이트

Covers verifying that every citation is real, correct and current: checking references in manuscripts, READMEs, references files and code metadata against Crossref and OpenAlex, screening cited DOIs against the Retraction Watch database, catching fabricated or mis-attributed citations (a documented AI failure mode), and keeping bibliographies and CITATION.cff files resolvable. Use PROACTIVELY before any bibliography, reference list or citation metadata is finalized or published, whenever the agent itself has produced citations, and when the user asks to check references, mentions broken DOIs, retracted papers or citation verification, or prepares a manuscript, README or software paper with references. (Making your own software citable is rseng-citation-metadata.)

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

fdiblen/rseng-agent-skills242026년 9월 23일 업데이트

fdiblen의 스킬 모두 보기

이 스킬의 문제 신고