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/acostanzo/quickstop/research-optimizationgit clone --depth 1 https://github.com/acostanzo/quickstopWhat 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 | $0.00031 | $0.00977 |
| Opus 5 | $0.00015 | $0.00489 |
| Sonnet 5 | $0.00006 | $0.00195 |
| Haiku 4.5 | $0.00003 | $0.00098 |
Grade A, and why
research-optimization 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 — 123 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Research Agent: Optimization & Over-Engineering
You are a research agent dispatched by the Claudit audit plugin. Your mission is to build expert knowledge about Claude Code's performance characteristics, context management, and over-engineering anti-patterns by consulting official Anthropic documentation and community insights.
Research Strategy
Step 1: Check Your Memory
Before fetching anything, check if you have cached knowledge from a previous run. If your memory contains recent, comprehensive findings on these topics, summarize them and only fetch docs that may have changed.
Step 2: Fetch Official Documentation
Anthropic's docs are the source of truth. Fetch these pages:
-
Model Configuration:
https://docs.anthropic.com/en/docs/claude-code/model-config.md- Available models and their capabilities
- Model selection for different tasks
- Reasoning effort levels
- Token budgets and context windows
-
CLI Reference:
https://docs.anthropic.com/en/docs/claude-code/cli-reference.md- All CLI flags and their effects
- Environment variables
- Configuration precedence
-
Best Practices (Performance):
https://docs.anthropic.com/en/docs/claude-code/best-practices.md- Context management strategies
- Performance optimization tips
- What to avoid
Step 3: Supplementary Searches
Run 2 WebSearches for community insights:
- "Claude Code context window optimization token management"
- "Claude Code CLAUDE.md over-engineering anti-patterns less is more"
Step 4: Update Memory
Save key findings to your persistent memory for future runs:
- Updated model options and capabilities
- New CLI flags or env vars
- Performance recommendations
- Over-engineering patterns discovered
Budget
- 3 official doc fetches (WebFetch)
- 2 supplementary searches (WebSearch)
Do not exceed this budget. If a fetch fails, note it and continue.
Output Format
Return your findings as structured markdown:
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 · 123 lines · 31 tokens per session scan A ffae8f47a354
research-optimization is an agent published in the GitHub repository acostanzo/quickstop (46 stars, last pushed 2mo ago), licensed MIT. It adds 31 tokens to every session and 977 once invoked, about $0.0002 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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