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/basher83/agent-auditor/improvegit clone --depth 1 https://github.com/basher83/agent-auditorWhat 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 | $0.00009 | $0.00782 |
| Opus 5 | $0.00005 | $0.00391 |
| Sonnet 5 | $0.00002 | $0.00156 |
| Haiku 4.5 | $0.00001 | $0.00078 |
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 yesterday.
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 — 121 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Continuous Improvement Analysis
You are analyzing recent Claude Code sessions to identify failures, corrections, and learning opportunities.
Configuration
Search window: ${1:-7} days back from today
Output file: ai_docs/continuous-improvement/lessons-learned-<TODAY>.md (use ISO date format)
Task Instructions
Step 1: Calculate Date Range
Today's date is available in your <env> context. Calculate:
- End date: Today
- Start date: Today minus ${1:-7} days
- Use ISO format (YYYY-MM-DD) for all date operations
Step 2: Search for Incidents (Multi-Strategy)
Execute the following searches in parallel for the calculated date range:
A) User Corrections (search_user_prompts) Search for these phrases indicating something went wrong:
- "wrong"
- "stop"
- "why did you"
- "not what I asked"
- "no"
- "what are you"
Use format: "index" first, then fetch full details only for relevant matches.
B) Problem-Solution Patterns (find_by_concept) Query for observations tagged with concept: "problem-solution"
C) Bug Fixes (find_by_type) Query for observations of type: "bugfix"
Step 3: Aggregate and Deduplicate
- Collect all findings from the three search strategies
- Note: This command does NOT deduplicate across previous reports
- Include all incidents found in the time window
- For each incident, capture:
- Timestamp (ISO format with time if available)
- Source (claude-mem URI or reference)
- User correction (exact quote if from prompts)
- Context (what was happening, what went wrong)
Step 4: Generate Report
Write the report to ai_docs/continuous-improvement/lessons-learned-<TODAY>.md:
Report structure:
# Lessons Learned - <DATE>
**Search Period:** Last ${1:-7} days (<START-DATE> to <END-DATE>)
**Report Generated:** <TODAY>
**Total Incidents Found:** <COUNT>
---
## Search Strategy
This report combines three search methods:
1. User correction phrases in prompts
2. Problem-solution concept observations
3. Bugfix type observations
---
## Incidents
### Incident 1: <Title/Summary>
**Timestamp:** YYYY-MM-DD HH:MM:SS
**Source:** <claude-mem URI or session reference>
**User Correction:** (if applicable)
> <exact quote>
**Context:**
<What was happening, what went wrong, what was learned>
---
### Incident 2: ...
[Continue for all incidents...]
---
## Search Results Summary
**Total searches performed:** 3 strategies
**Results by strategy:**
- User correction phrases: <COUNT> results
- Problem-solution concepts: <COUNT> results
- Bugfix observations: <COUNT> results
**Note:** This report shows ALL incidents in the time window. Manual reconciliation with existing lessons-learned files is required to identify truly new incidents.
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.
- yesterday First seen · 121 lines · 9 tokens per session scan A 14c989a62289
improve is a command published in the GitHub repository basher83/agent-auditor (5 stars, last pushed 6mo ago), licensed MIT. It adds 9 tokens to every session and 782 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 2026-08-31.
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memory-store
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no-vibe
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teach-me-testing
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