Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/u9401066/rootcause-mcp/code-review-workflownpx skills add u9401066/rootcause-mcp --skill code-review-workflowgit clone --depth 1 https://github.com/u9401066/rootcause-mcpWhat it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00000 | $0.01538 |
| Opus 5 | $0.00000 | $0.00769 |
| Sonnet 5 | $0.00000 | $0.00308 |
| Haiku 4.5 | $0.00000 | $0.00154 |
Grade A, and why
code-review-workflow scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
100% identical to code-review-workflow — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 182 lines — stays where its author put it; the contents beside it link to each section on GitHub.
name: code-review-workflow description: Complete code review workflow for PR/MR with multiple reviewers and automated checks. Triggers: PRW, 審查流程, review workflow, PR review, MR review, pull request, merge request, 程式碼審查流程, full review, 完整審查. version: 1.0.0 category: workflow compatibility:
- claude-code
- github-copilot
- vscode
- codex-cli orchestrates:
- code-reviewer
- security-reviewer
- test-generator
- ddd-architect allowed-tools:
- read_file
- grep_search
- semantic_search
- get_errors
- get_changed_files
- run_in_terminal
程式碼審查工作流
描述
完整的 PR/MR 審查流程，整合自動化檢查和多維度審查。
觸發條件
- 「審查 PR」「review PR」「審查流程」
- 「PRW: [PR 連結或分支]」
🔍 審查流程
┌─────────────────────────────────────────────────────────────┐
│ Code Review Workflow │
├─────────────────────────────────────────────────────────────┤
│ Phase 1: 📋 概覽 (Overview) │
│ ├─ 列出變更檔案 │
│ ├─ 統計變更行數 │
│ └─ 識別變更範圍 │
├─────────────────────────────────────────────────────────────┤
│ Phase 2: 🤖 自動化檢查 (Automated) │
│ ├─ 靜態分析 (ruff, mypy) │
│ ├─ 死碼檢測 (vulture) │
│ ├─ 安全掃描 (bandit) │
│ └─ 測試執行 │
├─────────────────────────────────────────────────────────────┤
│ Phase 3: 🏗️ 架構審查 (Architecture) │
│ ├─ [ddd-architect] DDD 合規檢查 │
│ ├─ 依賴方向驗證 │
│ └─ 模組化評估 │
├─────────────────────────────────────────────────────────────┤
│ Phase 4: 🔒 安全審查 (Security) │
│ ├─ [security-reviewer] OWASP 檢查 │
│ ├─ 敏感資料偵測 │
│ └─ 認證/授權審查 │
├─────────────────────────────────────────────────────────────┤
│ Phase 5: 📝 程式碼審查 (Code) │
│ ├─ [code-reviewer] 品質審查 │
│ ├─ 可讀性評估 │
│ ├─ 效能考量 │
│ └─ 測試覆蓋率 │
├─────────────────────────────────────────────────────────────┤
│ Phase 6: 📊 總結 (Summary) │
│ ├─ 生成審查報告 │
│ ├─ 建議修改項目 │
│ └─ 批准/請求修改 │
└─────────────────────────────────────────────────────────────┘
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 2d ago First seen · 182 lines · 0 tokens per session scan A 65db84ccee47
code-review-workflow is a skill published in the GitHub repository u9401066/rootcause-mcp (0 stars, last pushed 15d ago), licensed Apache-2.0. It costs nothing until one of its globs matches a file; then it loads 1,538 tokens. A static security scan graded it A with 0 findings. It is 100% identical to code-review-workflow, differing in 0 lines, and is treated as a copy.
Other skills, from other repositories
coding-icd10
Suggests candidate ICD-10-CM diagnosis codes (and ICD-10-PCS procedure codes) for diagnoses and procedures extracted by OpenMed, with rationale and a human-coder caveat. Use when the user wants to code a problem list, map a diagnosis span to a billable ICD-10-CM code, route a finding to the right chapter, cross-walk…
evaluating-with-leakage-gates
Evaluate an OpenMed de-identification or clinical NER model against the leakage-first release gates G1a through G8, which gate releases on residual PHI leakage rather than on F1. Use when the user wants to run the OpenMed eval harness on a synthetic golden set, decide whether a de-id model is RELEASABLE or…
mapping-to-snomed
Maps clinical concept spans extracted by OpenMed to SNOMED CT concepts through a USER-SUPPLIED terminology server (the user's own Ontoserver, Snowstorm, or UMLS/UTS), never a bundled vocabulary. Use when the user wants to code findings, disorders, procedures, body structures, or substances to SNOMED CT, run an ECL…
deidentifying-clinical-text
Remove, mask, or replace PHI/PII in clinical free text on-device with OpenMed's deidentify(). Use when the user needs to de-identify medical notes, strip patient identifiers, redact PHI before sharing or analysis, anonymize discharge summaries, or pick a de-id method (mask vs remove vs replace vs hash vs shiftdates).…
extracting-sdoh
Extracts social determinants of health (SDOH) — housing instability, food insecurity, unemployment, transportation barriers, social isolation, financial strain — from clinical narrative and maps the spans to ICD-10-CM Z-codes (Z55–Z65). Use after running OpenMed NER when the user wants SDOH surfacing, Z-code…
pick-a-pii-model
Select an on-device OpenMed PII model from the committed registry by language, runtime format, and size budget, then require recall validation before deployment. Use when an agent must choose a local PII detector for CPU, Apple Silicon, or a mobile export without relying on live model discovery.