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.
git clone --depth 1 https://github.com/phuoctrung-ppt/ai-sdlc-workflowWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/rules/phuoctrung-ppt/ai-sdlc-workflow/005-code-quality)<a href="https://agentmods.dev/rules/phuoctrung-ppt/ai-sdlc-workflow/005-code-quality"><img src="https://agentmods.dev/badge/rules/phuoctrung-ppt/ai-sdlc-workflow/005-code-quality/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/rules/phuoctrung-ppt/ai-sdlc-workflow/005-code-quality"><img src="https://agentmods.dev/badge/rules/phuoctrung-ppt/ai-sdlc-workflow/005-code-quality.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What 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 5.1 | $0.00274 | $0.00274 |
| Opus 5.5 | $0.00110 | $0.00110 |
| Sonnet 5.5 | $0.00055 | $0.00055 |
| Haiku 4.5 | $0.00027 | $0.00027 |
Grade A, and why
005-code-quality 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 1mo 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.
What it actually says
Code Quality
Details: AGENTS.md §5 (code quality) and §12 (forbidden patterns).
Logging
- No
console.login production; Winston in API/worker;console.erroronly in frontend catch blocks - Structured JSON:
{ timestamp, level, correlationId, service, message, metadata } LoggerInterceptorassignscorrelation-idper request
Errors
- Custom
HttpExceptionfilters; envelope:{ data, meta, error } - Status codes: 200, 201, 204, 400, 401, 403, 404, 409, 422, 429, 500
Testing
- Coverage: 70% API services, 50% frontend;
*.spec.ts,*.e2e-spec.ts,*.test.ts - Mock external APIs (OpenAI, SendGrid, MinIO, Stripe); MSW for frontend
Performance
next/image+next/font(Inter); colors fromconfig-tailwind;lucide-reactonly- Cursor pagination for large lists; offset only for small admin lists
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.
- 1mo ago First seen · 31 lines · 274 tokens per session scan A 894eef9e34bf
005-code-quality is a cursor rule published in the GitHub repository phuoctrung-ppt/ai-sdlc-workflow (2 stars, last pushed 1mo ago), licensed MIT. It adds 274 tokens to every session, about $0.0011 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
Other cursor rules, from other repositories
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Optimized Level 1 workflow for quick bug fixes with token efficiency.
workflow-level1
Streamlined workflow for Level 1 Quick Bug Fix tasks.
common-fixes
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debug
Bug diagnosis and fixing standards - root cause analysis, minimal fixes, regression tests.
nodebench-auto-qa
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