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 commands/sylphxai/coderag/qualitygit clone --depth 1 https://github.com/SylphxAI/coderagWhat 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 | $0.00015 | $0.00869 |
| Opus 5 | $0.00008 | $0.00434 |
| Sonnet 5 | $0.00003 | $0.00174 |
| Haiku 4.5 | $0.00002 | $0.00087 |
Grade A, and why
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 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.
How it starts
The opening of the file, as written. The whole thing — 183 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Quality Assurance
Maximize quality metrics: tests, coverage, performance, bundle size.
Scope
Testing:
- Fix failing tests
- Add missing tests (.test.ts for all modules)
- Improve coverage (critical paths 100%, business logic 80%+)
- Test edge cases
- Test error paths
- Integration tests
- E2E tests (if applicable)
Benchmarking:
- Add benchmarks (.bench.ts for critical functions)
- Measure performance
- Identify regressions
- Optimize slow operations
Performance:
- Profile hot paths
- Optimize algorithms (reduce complexity)
- Fix memory leaks
- Reduce allocations
- Cache expensive operations
- Lazy load where possible
Bundle Size:
- Analyze bundle composition
- Remove unused dependencies
- Tree-shake effectively
- Code split strategically
- Compress assets
- Use dynamic imports
Code Quality:
- Fix lint errors/warnings
- Improve type safety (no
any) - Reduce complexity
- Improve maintainability
- Follow code standards
Security:
- Fix vulnerabilities
- Update dependencies
- Validate inputs
- Sanitize outputs
- Secure defaults
Execution
1. Test Coverage
# Run tests with coverage
npm run test:coverage
# Identify gaps
# Add tests for uncovered code
# Focus on critical paths first
Priority:
- Critical business logic (100%)
- Error handling (100%)
- Edge cases (90%)
- Happy paths (100%)
- Integration points (80%)
2. Benchmarking
# Run benchmarks
npm run bench
# Compare against baseline
# Identify regressions
# Optimize bottlenecks
Add benchmarks for:
- Algorithm complexity (O(n) vs O(n²))
- Database queries
- API calls
- Data processing
- Rendering (if UI)
3. Performance Profiling
# Profile application
node --inspect ...
# Or use Clinic.js, 0x, etc.
# Identify bottlenecks
# Optimize hot paths
# Measure improvements
Check:
- CPU usage
- Memory usage
- I/O operations
- Network calls
- Rendering time
4. Bundle Analysis
# Analyze bundle
npm run build -- --analyze
# Check size
ls -lh dist/
# Identify large dependencies
# Remove unnecessary code
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 · 183 lines · 15 tokens per session scan A ab8ea52deee4
quality is a command published in the GitHub repository SylphxAI/coderag (12 stars, last pushed 7d ago), licensed MIT. It adds 15 tokens to every session and 869 once invoked, about $0.0001 per session on Opus 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 2026-08-30.
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