본문으로 건너뛰기
cccskills
무료GitHub에 공개

agent-work-receipt-designer

Design a normalized, machine-readable "work receipt" that answers what changed, why, by whom, and with what validation, for any AI-coding-agent task regardless of backend (Claude Code, Codex, Cursor, Aider, CI, or homegrown). Use when defining a receipt schema, normalizing heterogeneous agent logs into one evidence shape, gating PR merges on artifact-backed proof, or designing an OSS maintainer's fast-reject/fast-merge workflow. NOT for validating a DAG node's output against its own schema (use output-contract-enforcer), continuous runtime monitoring of a live coordination daemon (use runtime-verification-for-agents), or general Port Daddy claims/notes/salvage mechanics (use port-daddy-agent-skill).

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

설치 방법 보기

포함된 파일(11)

  • SKILL.md10.4 KB
  • agents/openai.yaml666 B
  • CHANGELOG.md179 B
  • examples/expected-output.md4.6 KB
  • examples/sample-input.json3.8 KB
  • README.md1.0 KB
  • references/backend-normalization.md6.5 KB
  • references/field-model.md5.8 KB
  • schemas/work-receipt.schema.json7.3 KB
  • scripts/receipt_lint.mjs12.5 KB
  • templates/output-template.md2.7 KB

SKILL.md(원문)

설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.

Agent Work Receipt Designer

Design the receipt schema and discipline that lets any AI coding agent's work be reviewed, trusted, and reversed without re-reading the chat.

Use This For

  • Defining a cross-tool receipt schema so Claude Code, Codex, Cursor, Aider, and CI runs all land in one evidence shape.
  • Turning a raw agent transcript into a typed record: identity, intent, context used, actions, validation, spend, risks, rollback, provenance.
  • Gating PR merges so a maintainer can reject a low-proof agent PR in seconds and merge a high-proof one with confidence.
  • Auditing whether "tests pass" claims are backed by a captured exit code or artifact, or are just the agent's word.
  • Designing durable, replayable handoffs that survive the originating session or worktree being deleted.

Do Not Use This For

  • Validating one DAG node's output against its declared JSON schema mid-pipeline (output-contract-enforcer).
  • Continuously auditing a live coordination daemon against formal invariants (runtime-verification-for-agents).
  • General Port Daddy session/claims/salvage mechanics unrelated to producing a receipt (port-daddy-agent-skill).

Process

flowchart TD
  A[Collect raw backend log] --> B[Extract identity + intent]
  B --> C[Extract context used]
  C --> D[Extract actions: commands, tool calls, diff]
  D --> E[Extract validation: exit codes + artifacts only]
  E --> F[Rank risks worst-first]
  F --> G[Attach rollback pointer]
  G --> H[Hash/sign for provenance]
  H --> I[Lint receipt, publish durably]
  1. Collect the raw log for the backend that ran (transcript JSONL, exec events, chat history, CI step logs). See references/backend-normalization.md for the per-backend extraction map.
  2. Extract identity (agent/model/backend/sessionId/operator) and intent (goal/scope/stopCondition) — a vague stop condition is the earliest sign the resulting receipt will be weak.
  3. Extract contextUsed: files actually read, governing rules consulted (CLAUDE.md/AGENTS.md/ADRs), attachments.
  4. Extract actions: every command with its real exit code, tool-call tallies, and a diffSummary a reviewer can triage from before opening the diff.
  5. Extract validation from tool-result payloads or captured logs only — never from the agent's own narration. A passed: true test with no exitCode and no artifactPath is a self-report, not proof; mark it passed: false instead.
  6. Rank risks[] most-severe-first and flag the one thing a reviewer should check first (checkFirst: true). See the reviewer-first ordering rule in references/field-model.md.
  7. Attach rollback.checkpoint, then hash (and sign, if attributability must survive the session) for provenance, and run scripts/receipt_lint.mjs before treating the receipt as done.

Output Contract

Produce a single JSON object matching schemas/work-receipt.schema.json with all nine required sections: identity, intent, contextUsed, actions, validation, spend, risks, rollback, provenance. validation.artifactBacked must only be true when every passed: true test carries a real exitCode or artifactPath.

Use scripts/receipt_lint.mjs to score a receipt deterministically and return { pass, score, missingFields, findings, recommendations }.

Anti-Patterns

Receipt As Chat Transcript

Novice: Paste the full conversation log and call it the receipt. Expert: A receipt is a typed, queryable record with a diff summary and artifact-backed validation — a reviewer should never have to scroll a transcript to find what changed. Detection: The "receipt" has no risks[], no diffSummary, and no distinguishable JSON shape; it's prose with timestamps.

Self-Reported Success

Novice: "I ran the tests and they pass" is recorded as the validation, full stop. Expert: Every passed: true test entry must carry a real exitCode or artifactPath pointing at captured output; an agent's own claim is not evidence. Detection: receipt_lint.mjs returns a self-reported-validation or artifact-backed-flag-lying finding at critical severity, and pass is forced to false.

Reviewer Homework Dump

Novice: Dump every file touched, every tool call, and an empty or unordered risks[], leaving the reviewer to triage from scratch. Expert: Rank risks worst-first, mark the one to check first, and lead with a diff summary — the receipt should remove reviewer labor, not relocate it. Detection: risks[] is empty on a nontrivial change, or not sorted critical > high > medium > low; receipt_lint.mjs flags empty-risks or risks-not-reviewer-first.

References

FileLoad When
references/field-model.mdNeed the canonical field-by-field meaning, a filled example, and the reviewer-first ordering rule.
references/backend-normalization.mdNeed to derive a receipt from a specific backend's raw logs, or need signing/attributability guidance.
examples/expected-output.mdNeed the shape of a finished, high-proof receipt.
templates/output-template.mdNeed a reusable receipt template to fill in.
schemas/work-receipt.schema.jsonNeed to validate a receipt's structure programmatically.
scripts/receipt_lint.mjsNeed deterministic scoring of a receipt's completeness and proof quality.
agents/openai.yamlNeed a subagent descriptor for delegated receipt generation.

Layout QA gate (mechanical — run before shipping)

Before calling any rendered page, artifact, dashboard, deck, or component done, run the mechanical overflow/collision checker. It renders the page headlessly and flags text-vs-text collisions, clipped/ellipsis-truncated elements, text escaping its container, and horizontal page scroll — the visual defects a screenshot hides and that only appear at a specific width or in one theme.

Resolve layout-overflow-guard from the active skill catalog before running it. The command below shows the standard Claude install path; use the path reported by your harness. If the skill is absent, install or sync it instead of skipping this gate.

python3 ~/.claude/skills/layout-overflow-guard/scripts/check_layout.py <file-or-url> \
  --widths 1280,1100,860,720,390 --themes light,dark

You do not need to read check_layout.py — invoke it with the Bash tool and act on its report and exit code (non-zero = a defect). The script's source never enters your context; only its findings do. Drive it to zero violations across every width and both themes before you ship. Full detail: the layout-overflow-guard skill.

<!-- BEGIN BUNDLE INDEX (auto: index_references.py) -->

Skill Bundle Index

Every file in this skill, and when to open it. Auto-generated; run scripts/index_references.py --fix.

root

  • CHANGELOG.md — Agent Work Receipt Designer — Changelog — - Initial skill creation - Core process defined - Reference files and deterministic receipt_lint script added
  • README.md — Agent Work Receipt Designer — Design the normalized, machine-readable receipt that answers "what changed, why, by whom, with what validation" for any AI-coding-agent task

agents/

examples/

references/

  • references/backend-normalization.md — Backend Normalization And Provenance — Use this when you need to derive a normalized receipt from a specific agent backend's raw logs, or when deciding how to sign/attribute a rec
  • references/field-model.md — Receipt Field Model — Use this when you need the canonical field-by-field meaning of a work receipt, a filled example to copy from, or the reviewer-first ordering

schemas/

scripts/

templates/

  • templates/output-template.md — Agent Work Receipt Template — [One-sentence description of the task the receipt covers.] Validate with node scripts/receipt_lint.mjs --input <this-file-as-json>.json be
<!-- END BUNDLE INDEX -->

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

Expert in 2000s-era music visualization (Milkdrop, AVS, Geiss) and modern WebGL implementations. Specializes in Butterchurn integration, Web Audio API AnalyserNode FFT data, GLSL shaders for audio-reactive visuals, and psychedelic generative art. Activate on "Milkdrop", "music visualization", "WebGL visualizer", "Butterchurn", "audio reactive", "FFT visualization", "spectrum analyzer". NOT for simple bar charts/waveforms (use basic canvas), video editing, or non-audio visuals.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

curiositech/port-daddy32026년 10월 19일 업데이트

Expert legal research agent for finding and scraping expungement data state by state. Knows authoritative sources, URL patterns, Firecrawl configuration, and 2026 legal landscape.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

curiositech/port-daddy32026년 10월 19일 업데이트

Expert in 3D computer vision labeling tools, workflows, and AI-assisted annotation for LiDAR, point clouds, and sensor fusion. Covers SAM4D/Point-SAM, human-in-the-loop architectures, and vertical-specific training strategies. Activate on '3D labeling', 'point cloud annotation', 'LiDAR labeling', 'SAM 3D', 'SAM4D', 'sensor fusion annotation', '3D bounding box', 'semantic segmentation point cloud'. NOT for 2D image labeling (use clip-aware-embeddings), general ML training (use ml-engineer), video annotation without 3D (use computer-vision-pipeline), or VLM prompt engineering (use prompt-engineer).

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

curiositech/port-daddy32026년 10월 19일 업데이트

Apply crisis decision-making research to agent routing, uncertainty triage, and coordination failure analysis in time-pressured systems. Use when diagnosing handoff failures, analytical paralysis, or expert judgment under incomplete information. NOT for routine coding, simple CRUD design, or static single-agent tasks with complete information.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

curiositech/port-daddy32026년 10월 19일 업데이트

Use for insight, reframing, contradiction, impasse, and anomaly-driven problem solving when execution effort no longer helps. NOT for routine optimization, error correction, or well-specified tasks with known solution paths.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

curiositech/port-daddy32026년 10월 19일 업데이트

Apply cognitive task analysis to expert work that depends on perceptual cues, branching judgment, and recurring monitoring loops. Use when decomposing expert capability into agent structure, simulation design, or validation interviews. NOT for ordinary step-by-step SOP capture or simple pipelines with no tacit cue layer.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

curiositech/port-daddy32026년 10월 19일 업데이트

curiositech의 스킬 모두 보기

이 스킬의 문제 신고