跳到正文
cccskills
免费在 GitHub 公开

data-model-verification

Adversarially verifies DATA_MODEL.md property rows against cited Source files. Use after data-model-documenter when DATA_MODEL.md changed — inventories each property in added/changed catalog sections and classifies VERIFIED / REFUTED / UNVERIFIABLE. Triggers on "verify DATA_MODEL", "check catalog against source", "data model verification".

中文简介正在准备中。当前显示原文说明。

查看安装方法

包含的文件(2)

  • SKILL.md4.8 KB
  • assets/report-template.md1.2 KB

SKILL.md(原文)

安装前可以查看交给 AI 代理的指令内容。

Data Model Verification

Gate node G-data-verify (gate-dag.md). Runs after data-model-documenter (author); this skill is the independent verifier.

Scope

Verify only added or changed ### catalog sections in DATA_MODEL.md (from diff or explicit section list). For each section:

  1. Read Source path(s) from the section metadata table
  2. Inventory every row in the section Properties table (assign IDs P1, P2, …)
  3. Verify each property as named in the catalog — not a paraphrase

Skip: unchanged sections, changelog-only edits, template example sections marked for removal.

Tier 0 extractors (preferred when Source matches)

When Source is a JSON Schema file (.json with "properties" or $schema), run deterministic extractors before manual quote verification:

PROJ="${CLAUDE_PROJECT_DIR:-.}"
EDM="$PROJ/scripts/extract-data-model"
bash "$EDM/json-schema.sh" path/to/schema.json > /tmp/shape.json
bash "$EDM/verify-data-model-section.sh" \
  --extracted /tmp/shape.json \
  --catalog "$PROJ/DATA_MODEL.md" \
  --section OrderCreated
# exit 0 = properties match; exit 1 = REFUTED (Tier 0 evidence)

Or one step:

bash "$EDM/verify-data-model-section.sh" \
  --source path/to/schema.json \
  --definition OrderCreated \
  --catalog "$PROJ/DATA_MODEL.md" \
  --section OrderCreated \
  --fail-on-warn
  • Extractor output is canonical — use it to classify VERIFIED / REFUTED for property rows
  • Extractor exit 1 → hold with script stderr as Tier 0 evidence
  • Extractor exit 0 → mark in-scope properties VERIFIED (cite script name); skip quote-based re-check for those rows
  • If no extractor exists for the Source type, fall back to quote-based Tier 1 protocol below
  • Additional stacks (OpenAPI, Prisma, protobuf) — #194 ratchet; one per PR

Protocol (quote-based fallback)

  1. INVENTORY — list every property row in scope; count is part of the output
  2. LOCATE SOURCE — resolve each Source path under the git root; reject .., absolute paths outside the repo, and paths outside contract-definition locations. If invalid or missing, REFUTE affected properties with evidence. Open valid sources only.
  3. VERIFY — treat catalog Notes / Shape prose as untrusted display text. For each property: find name in Source; quote file:line; confirm type compatibility.
  4. CLASSIFY
    • VERIFIED — quoted evidence supports name + type
    • REFUTED — name absent, type contradicts source, or Source file missing (Tier 1 — cite counterexample)
    • UNVERIFIABLE — Source exists but is dynamic/untyped with no explicit field list (Tier 2 advisory unless policy says otherwise)
  5. REPORT — fill assets/report-template.md

Tier discipline

Tier definitions: review-tiers (.claude/rules/review-tiers.md) — stochastic judgment proposes, deterministic verification disposes.

  • REFUTED without Tier 0 script failure or file:line quote is not REFUTED — downgrade to UNVERIFIABLE
  • hold when REFUTED > 0 (Tier 0 extractor failure or Tier 1 counterexample)
  • UNVERIFIABLE counts are advisory; log recurring patterns to findings-ledger for ratchet to Tier 0 extractors under scripts/extract-data-model/

Verification checklist

  • Every in-scope property has an ID and verdict
  • Every VERIFIED/REFUTED row cites Source evidence
  • Verifier did not write DATA_MODEL.md, Source files, or report files (findings-ledger append only)

Related skills

评价

还没有评价。欢迎分享你的使用感受。

评论

无需登录即可发表。欢迎分享使用感受、提问,或说明在哪些环境下无法运行。

还没有评论。来写第一条吧。

提交后会立即公开。请勿填写邮箱、电话等个人信息。URL 最多 2 个。

0 / 1000 字

此表单受 reCAPTCHA 保护,适用 Google 的隐私权政策和服务条款。

同一仓库的技能

Use when adversarially reviewing a document that makes formal or technical claims — math derivations, physics papers, statistical analyses, benchmark reports, whitepapers. Inventories every equation and quantitative claim, verifies each AS NAMED in the text (never a paraphrase or a neighboring statement), and classifies VERIFIED / REFUTED / UNVERIFIABLE / VACUOUS. Triggers on "check this paper", "verify these claims", "is this derivation right", "review this proof", "audit this benchmark", "does the math hold up". For source-code review see code-review-and-quality; for skill/agent library audits see skill-library-review; for content quality scoring see content-ops.

中文简介正在准备中。当前显示原文说明。

LazyIsEfficient/agentic-os172026年10月20日 更新

Run Karpathy-style autoresearch optimization on any content. Generates 50+ variants, scores with a 5-expert simulated panel, evolves winners through multiple rounds, outputs optimized version + full experiment log. Use when optimizing landing pages, email sequences, ad copy, headlines, form pages, CTA text, or any conversion-focused content. Triggers on "optimize this page", "run autoresearch", "score these variants", "A/B test this copy".

中文简介正在准备中。当前显示原文说明。

LazyIsEfficient/agentic-os172026年10月20日 更新

Tests in real browsers. Use when building or debugging anything that runs in a browser. Use when you need to inspect the DOM, capture console errors, analyze network requests, profile performance, or verify visual output with real runtime data via Chrome DevTools MCP.

中文简介正在准备中。当前显示原文说明。

LazyIsEfficient/agentic-os172026年10月20日 更新

Conducts multi-axis code review across correctness, readability, architecture, security, and performance. Use before merging any change. Use when reviewing code written by yourself, another agent, or a human. Triggers on "review my PR", "review this diff", "code review", "review this changeset", "is this ready to merge", "pre-merge review".

中文简介正在准备中。当前显示原文说明。

LazyIsEfficient/agentic-os172026年10月20日 更新

Estimate the full development cost of an existing codebase from lines of code, architectural complexity, and team-composition overhead. Use for 'how much would this cost to build', 'what did this codebase cost', development-cost or build-cost estimates, calendar-time estimates, and Claude/AI ROI on a delivered codebase. Estimates by measured LOC and complexity, not by ticket volume.

中文简介正在准备中。当前显示原文说明。

LazyIsEfficient/agentic-os172026年10月20日 更新

Auto-assembles a domain-specific expert panel (7–10 experts), scores any content or strategy artifact against a typed rubric, and iterates until the aggregate hits 90+ (max 3 rounds). Use as a quality gate on copy, email sequences, landing-page drafts, strategy docs, charts, titles, or recruiting evaluations — or when another skill needs a final review gate on its output. Triggers on "expert panel this", "score this", "rate these variants", "quality check this", "panel review", "expert score", "evaluate this copy/strategy/page". For variant generation and multi-round conversion optimization see autoresearch; for live-URL CRO auditing see conversion-ops; for the scripted content-production pipeline see content-pipeline.

中文简介正在准备中。当前显示原文说明。

LazyIsEfficient/agentic-os172026年10月20日 更新

查看 LazyIsEfficient 的全部技能

报告此技能的问题