菜单
Tracks source-to-target lineage, transformation history, evidence, ownership and provenance. Use when documenting data flows, audit trails, or impact analysis for changes.
用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
基于 SOC 职业分类
Use when reviewing code before merge to assess correctness, tests, maintainability, security and user impact.
Use when reviewing plans, recommendations, prioritisation, risk registers, retrospectives or agent outputs for judgement distortion from cognitive bias — not for checking whether an argument's premises validly support its conclusion.
Use when evaluating whether premises in plans, ADRs, reviews, security justifications or agent recommendations validly support their conclusions — for logical fallacies, not judgement distortion from cognitive bias.
Use when a coding agent needs robust problem solving, explicit decomposition, alternative-path exploration, program-aided reasoning, ReAct loops, or self-correction.
Use when generated code, plans, tests, prompts or architecture need critique, repair and verification before completion; especially for coding tasks requiring self-review, test-driven repair or quality gates.
Identifies risks, scores likelihood and impact, selects treatment, assigns owners and review cadence. Use when building or updating a risk register, treating agent autonomy risks, or deciding mitigate, transfer, accept or avoid.
| name | data-lineage-and-provenance |
| description | Tracks source-to-target lineage, transformation history, evidence, ownership and provenance. Use when documenting data flows, audit trails, or impact analysis for changes. |
Use when data must be trusted, audited, explained, replayed, governed or traced.
For KG-enabled RAG or GraphRAG systems, use this alongside skills/knowledge-graph-rag/SKILL.md so graph facts, retrieval evidence and generated answers remain source-traceable.
Produce a practical, concise, traceable architecture artefact that a coding agent can use to guide implementation or review.
Apply DAMA-DMBOK2-style separation of data governance, architecture, modelling, security, integration/interoperability, master/reference data, metadata and quality. For cloud/shared data, apply CDMC-style expectations: ownership, classification, entitlement/access evidence, lineage/provenance, lifecycle/retention, quality controls and auditable evidence.
Do not claim trust without lineage/provenance evidence.
