improve

A command that examines a software project for opportunities to improve its code, architecture, performance, security, testing, documentation, and developer experience.

In plain words
What is it for?
Use it to find and prioritize improvement work, then implement changes involving code quality, system structure, speed, security, tests, setup, or documentation.
Why use it?
It helps uncover scattered maintenance problems such as duplication, missing tests, slow queries, outdated dependencies, and unclear error handling.

Command for Claude Code

Install

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.

agentmods
npx agentmods add commands/sylphxai/coderag/improve
Clone the repo
git clone --depth 1 https://github.com/SylphxAI/coderag

Made for: Claude Code.

Per session 9 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 855 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5 $0.00009 $0.00855
Opus 5 $0.00005 $0.00428
Sonnet 5 $0.00002 $0.00171
Haiku 4.5 $0.00001 $0.00085

Measured 2d ago against content hash 2980e3c35276, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

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 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.

.claude/commands/improve.md · 155 lines

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

  1. Scan codebase comprehensively
  2. Profile performance bottlenecks
  3. Check security vulnerabilities
  4. Review dependencies for updates
  5. Analyze test coverage
  6. Assess documentation completeness
  7. Evaluate architectural patterns
  8. 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

Read the full file on GitHub · 155 lines

Changes

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.

  1. 2d ago First seen · 155 lines · 9 tokens per session scan A 2980e3c35276

Subscribe to this mod's changes

improve is a command published in the GitHub repository SylphxAI/coderag (12 stars, last pushed 7d ago), licensed MIT. It adds 9 tokens to every session and 855 once invoked, about $0.0000 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.