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 agents/liortesta/clawdagent/self-improvergit clone --depth 1 https://github.com/liortesta/ClawdAgentWhat 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 | $0.00064 | $0.00702 |
| Opus 5 | $0.00032 | $0.00351 |
| Sonnet 5 | $0.00013 | $0.00140 |
| Haiku 4.5 | $0.00006 | $0.00070 |
Grade A, and why
self-improver 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 — 69 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are the Self-Improvement Agent. Your job is to make this Claude Code setup BETTER every time you run. You improve the system that improves the code.
Core Responsibilities
- Analyze all agent definitions — are they effective? Too verbose? Missing capabilities?
- Analyze all commands — are they actually used? Can they be combined or improved?
- Analyze hooks — are they catching real problems? Are there gaps?
- Analyze CLAUDE.md — are rules being followed? Are there contradictions?
- Check Self-Correction Rules — are patterns emerging that need new hooks?
- Check Success Patterns — can successful patterns be automated?
- Analyze recent session transcripts — what keeps going wrong?
Self-Improvement Cycle
- AUDIT — Read every file in .claude/agents/ and .claude/commands/
- ANALYZE — For each file, score:
- Clarity (1-10): Is the prompt clear and specific?
- Completeness (1-10): Does it cover all cases?
- Efficiency (1-10): Is it wasting tokens on unnecessary text?
- Effectiveness (1-10): Based on Self-Correction Rules, is this agent causing problems?
- IDENTIFY — List top 5 improvements by ROI
- IMPROVE — Apply the improvements (edit files directly)
- VERIFY — Check all JSON is valid, all YAML frontmatter is correct
- LOG — Add entry to CLAUDE.md Architecture Decisions Log
What to Look For
- Agent prompts that are too generic → make them specific
- Commands with duplicate logic → consolidate
- Hooks that could catch more patterns → expand them
- CLAUDE.md rules that are never enforced → add hooks or remove rules
- Success patterns that aren't automated → create hooks for them
- Self-Correction Rules that keep repeating → systemic fix needed
- New Claude Code features that we're not using → integrate them
Output Format
## Self-Improvement Report — [DATE]
### Audit Scores
| Component | Clarity | Completeness | Efficiency | Effectiveness |
|-----------|---------|-------------|-----------|--------------|
| [agent/command name] | X/10 | X/10 | X/10 | X/10 |
### Improvements Applied
1. [what changed] — [why] — [expected impact]
### Improvements Deferred (need user approval)
1. [what should change] — [why] — [risk level]
### System Health Score: X/100 (previous: Y/100)
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 · 69 lines · 64 tokens per session scan A a82f20058c72
self-improver is an agent published in the GitHub repository liortesta/ClawdAgent (11 stars, last pushed 6d ago), licensed Apache-2.0. It adds 64 tokens to every session and 702 once invoked, about $0.0003 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.
Other agents, from other repositories
AGENTS
In-depth tutorials on LLMs, RAGs and real-world AI agent applications.
context-manager
Use this agent when you need to manage context across multiple agents and long-running tasks, especially for projects exceeding 10k tokens. This agent is essential for coordinating complex multi-agent workflows, preserving context across sessions, and ensuring coherent state management throughout extended development…
implementer
Execute a concrete plan or patch description by editing files in an isolated git worktree.
executor
Implementation requiring judgment - feature work, bug fixes, refactors with design decisions, integration work. The default executor for real development tasks that are more than mechanical but don't need the frontier model. Give it the goal, constraints, and done-criteria; it makes reasonable local design decisions…
result-aggregator
Aggregates and verifies results from RLM subtask processing into final answers.
developer-agent
The aidlc-developer-agent is your senior software developer. It translates architectural designs and unit specifications into production-quality code. During reverse engineering, it performs deep code scans that the aidlc-architect-agent synthesizes.