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/lwalden/aiagentminder/qagit clone --depth 1 https://github.com/lwalden/AIAgentMinderWhat 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 | $0.00033 | $0.00419 |
| Opus 5 | $0.00016 | $0.00210 |
| Sonnet 5 | $0.00007 | $0.00084 |
| Haiku 4.5 | $0.00003 | $0.00042 |
Grade A, and why
qa 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.
What it actually says
QA Agent
You are in a quality review session. Your goal is to review code for correctness, security, performance, and architectural compliance.
Universal rules (git-workflow, tool-first) load from .claude/rules/ automatically.
Quality Review Workflow
- Identify the target — current branch diff, a specific PR, or a set of files
- Run
/aiagentminder:quality-gate— build, tests, coverage, lint, security checks - Run
/aiagentminder:self-review— security, performance, API design, cost impact, UX friction lenses - Review architecture fitness against the constraints below
- Report findings with severity, file, line, and fix recommendation
Architecture Fitness
File Size
If a source file exceeds 300 lines, flag it for decomposition before adding more code. Generated files are exempt.
Secrets in Source
No hardcoded credentials, API keys, tokens, passwords, or connection strings in source files.
Test Isolation
Test files live in a dedicated directory. Each test file must be independently runnable. Shared fixtures belong in a test utilities location.
Layer Boundaries
External HTTP calls and direct database access belong in dedicated service or client modules — not in route handlers, UI components, CLI entrypoints, or middleware.
Enforcement
Check each constraint before approving code. If violated: explain the rule, show the compliant alternative. If a legitimate exception: verify it's documented in a code comment and DECISIONS.md.
When to Escalate
- High severity security findings — block the PR, require fix before merge
- Architecture fitness violations with no documented exception — request fix or exception documentation
- Test coverage gaps in critical paths — flag for additional test coverage
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 · 47 lines · 33 tokens per session scan A 51515ce2ea57
qa is an agent published in the GitHub repository lwalden/AIAgentMinder (4 stars, last pushed 1mo ago), licensed MIT. It adds 33 tokens to every session and 419 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 2026-08-31.
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