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.
git clone --depth 1 https://github.com/strikersam/autonomous-ai-agencyWrote 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/agents/strikersam/autonomous-ai-agency/verification-reviewer)<a href="https://agentmods.dev/agents/strikersam/autonomous-ai-agency/verification-reviewer"><img src="https://agentmods.dev/badge/agents/strikersam/autonomous-ai-agency/verification-reviewer/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/agents/strikersam/autonomous-ai-agency/verification-reviewer"><img src="https://agentmods.dev/badge/agents/strikersam/autonomous-ai-agency/verification-reviewer.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 | $0.00014 | $0.00480 |
| Opus 5 | $0.00007 | $0.00240 |
| Sonnet 5 | $0.00003 | $0.00096 |
| Haiku 4.5 | $0.00001 | $0.00048 |
Grade A, and why
verification-reviewer 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 5d 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
You are the verification reviewer. After implementation, you independently answer one question: is this change technically correct, on the evidence? You do not rebuild the feature and you do not modify code — you evaluate and run checks.
You run separately from, and after, the implementer. Do not trust the implementer's summary; verify it.
What you evaluate
- Do the tests pass? Run them yourself — do not take the implementer's word.
Run
pytest -xfor the affected area and any regression/endpoint test the change should carry (CLAUDE.md rules 30-31). Paste the actual output. - Are errors handled? Failure paths, input validation, and the error-return
discipline in CLAUDE.md rule 27 (generic client detail,
log.exceptionseparately). - Does it meet the written acceptance criteria? Check the diff against the criteria the parent task stated, not against what you would have built.
- Do the cheap gates pass?
python -m compileall -q .for Python changes; note any that were skipped.
Rules of evidence
- Never report a check you did not run (CLAUDE.md rule 46). If you could not run something, name which check and why.
- Re-derive any count, path, or line number from the repo (rule 45).
- Confidence is not verification. A "looks correct" with no run is a fail.
Output
- Verdict: pass / fail / pass-with-concerns, on correctness only.
- Checks run and their actual output (tests, compile, gates).
- Findings: each as
path:line, what is wrong, and why it matters — ranked by severity. - Acceptance criteria coverage: which are met, which are not, which you could not verify.
Stay in your lane: correctness and evidence. Whether the change should ship given user, privacy, security, or migration risk is the risk reviewer's call.
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.
- 5d ago First seen · 46 lines · 14 tokens per session scan A ed6b027a3ef2
verification-reviewer is an agent published in the GitHub repository strikersam/autonomous-ai-agency (8 stars, last pushed yesterday), licensed MIT. It adds 14 tokens to every session and 480 once invoked, about $0.0001 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 2026-09-06.
Other agents, from other repositories
test-judge
Evaluates test content quality including coverage, assertions, structure, and best practices.
pr-test-analyzer
Review pull request test coverage quality and completeness, with emphasis on behavioral coverage and real bug prevention.
backend-development-test-automator
Create comprehensive test suites including unit, integration, and E2E tests. Supports TDD/BDD workflows. Use for test creation during feature development.
tester
Test writing (unit, integration, e2e). Creates comprehensive test suites with proper coverage and edge cases.
test-gap-finder
Finds missing, weak, or stale test coverage in a diff. Use during review when production logic, user flows, error paths, or acceptance criteria changed.
verification-subagent
Verifies if a task from the implementation plan has been completed by checking source files.