Borrowing it
Nothing to install: this file belongs to basher83/agent-auditor. 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/basher83/agent-auditor/main/.claude/commands/improve.mdgit clone --depth 1 https://github.com/basher83/agent-auditorWrote 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/basher83/agent-auditor/improve)<a href="https://agentmods.dev/commands/basher83/agent-auditor/improve"><img src="https://agentmods.dev/badge/commands/basher83/agent-auditor/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/basher83/agent-auditor/improve"><img src="https://agentmods.dev/badge/commands/basher83/agent-auditor/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 | $0.00782 |
| Opus 5.5 | $0.00004 | $0.00313 |
| Sonnet 5.5 | $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 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.
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.
- 1mo ago 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 7mo ago), licensed MIT. It adds 9 tokens to every session and 782 once invoked, about $0.0000 per session on Opus 5.5. 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.
Other commands, from other repositories
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.
speckit.bug.fix
Apply the remediation from a bug assessment and record what was changed.
speckit.bug.test
Validate that a previously fixed bug is resolved and record the verification report.