Borrowing it
Nothing to install: this file belongs to SylphxAI/coderag. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/SylphxAI/coderag/main/.claude/commands/improve.mdgit clone --depth 1 https://github.com/SylphxAI/coderagWrote 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/commands/sylphxai/coderag/improve)<a href="https://agentmods.dev/commands/sylphxai/coderag/improve"><img src="https://agentmods.dev/badge/commands/sylphxai/coderag/improve/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/commands/sylphxai/coderag/improve"><img src="https://agentmods.dev/badge/commands/sylphxai/coderag/improve.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 | $0.00009 | $0.00855 |
| Opus 5.5 | $0.00004 | $0.00342 |
| Sonnet 5.5 | $0.00002 | $0.00171 |
| Haiku 4.5 | $0.00001 | $0.00085 |
Grade A, and why
improve 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.
This is a copy
100% identical to improve — 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 — 155 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Proactive Improvement
Analyze project comprehensively. Discover improvement opportunities. Prioritize and execute.
Discovery Areas
Code Quality:
- Code complexity hotspots
- Duplication patterns
- Test coverage gaps
- Error handling weaknesses
- Type safety improvements
- Naming inconsistencies
Architecture:
- Tight coupling
- Missing abstractions
- Scalability bottlenecks
- State management issues
- API design improvements
- Module organization
Performance:
- Slow algorithms (profiling data)
- Database query inefficiencies
- Bundle size optimization
- Memory usage patterns
- Network request optimization
- Caching opportunities
Security:
- Vulnerability scan (dependencies)
- Input validation gaps
- Authentication/authorization weaknesses
- Sensitive data exposure
- OWASP top 10 check
- Security best practices
Developer Experience:
- Development setup complexity
- Build time optimization
- Testing speed
- Debugging capabilities
- Error messages clarity
- Documentation gaps
Maintenance:
- Outdated dependencies
- Deprecated API usage
- Missing tests
- Incomplete documentation
- Configuration complexity
- Technical debt accumulation
Features:
- Missing functionality (user feedback)
- Integration opportunities
- Automation potential
- Monitoring/observability
- Error recovery
- User experience improvements
Analysis Process
- Scan codebase comprehensively
- Profile performance bottlenecks
- Check security vulnerabilities
- Review dependencies for updates
- Analyze test coverage
- Assess documentation completeness
- Evaluate architectural patterns
- Identify missing features
Prioritization Matrix
Impact vs Effort:
- High Impact + Low Effort → Do first
- High Impact + High Effort → Plan carefully
- Low Impact + Low Effort → Quick wins
- Low Impact + High Effort → Skip or defer
Categories:
- Critical: Security, data loss, crashes
- High: Performance degradation, poor UX
- Medium: Code quality, maintainability
- Low: Nice-to-haves, polish
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 · 155 lines · 9 tokens per session scan A 2980e3c35276
improve is a command published in the GitHub repository SylphxAI/coderag (12 stars, last pushed 1mo ago), licensed MIT. It adds 9 tokens to every session and 855 once invoked, about $0.0000 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 100% identical to improve, differing in 0 lines, and is treated as a copy.
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