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/adriannoes/awesome-agentic-aiWrote 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/adriannoes/awesome-agentic-ai/final-review)<a href="https://agentmods.dev/commands/adriannoes/awesome-agentic-ai/final-review"><img src="https://agentmods.dev/badge/commands/adriannoes/awesome-agentic-ai/final-review/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/adriannoes/awesome-agentic-ai/final-review"><img src="https://agentmods.dev/badge/commands/adriannoes/awesome-agentic-ai/final-review.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 | $0.00000 | $0.01027 |
| Opus 5 | $0.00000 | $0.00513 |
| Sonnet 5 | $0.00000 | $0.00205 |
| Haiku 4.5 | $0.00000 | $0.00103 |
Grade A, and why
final-review 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 11d 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 — 94 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Final Review - Comprehensive PR Review & Testing
Source: patyearone/final-review-gist
Step 0: Determine Review Pass
Before starting, check the git history to determine if this is a follow-up review:
git log --oneline -10 | grep -i "Co-Authored-By:"
- First pass: No recent AI co-authored commits on this branch, or commits are from a different feature.
- Follow-up pass: Recent AI co-authored commits exist from a previous final-review run on this same feature.
If this is a follow-up pass:
- Note this in the summary as "Review Pass #2" (or #3, etc.)
- Tell the review agents to check git history to understand WHY recent changes were made before suggesting reversals
- Be more conservative with changes — the previous pass already applied significant improvements
- Focus agents on catching issues introduced BY the previous review, not re-litigating decisions already made
Step 1: Create or Update the PR
- If on
main: Create a new feature branch with a descriptive name (e.g.,feature/add-user-metrics,fix/dashboard-loading), then commit the changes. - If already on a feature branch: Continue with existing branch.
Then handle the PR:
- If a PR doesn't exist, create one with a clear title and description.
- If a PR already exists, push any uncommitted changes to it.
Step 2: Launch Three Review Agents in Parallel
Use the Task tool to launch these three agents simultaneously.
Important context for all agents (if follow-up pass):
- "Check git log to see recent commits and their messages before making recommendations"
- "If a pattern looks intentional based on recent commit messages, don't recommend reversing it without strong justification"
- "Focus on issues that may have been INTRODUCED by recent changes, not re-reviewing the entire file"
Agent 1: Codebase Consistency Reviewer
- Are we duplicating logic that already exists elsewhere? Search for similar patterns, helper methods, or services.
- Are there other places where this same logic/fix should be applied? We don't want inconsistency.
- Check for opportunities to consolidate with existing utilities, concerns, or services.
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.
- 11d ago First seen · 94 lines · 0 tokens per session scan A 9fd1864f3e54
final-review is a command published in the GitHub repository adriannoes/awesome-agentic-ai (57 stars, last pushed 13d ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 1,027 tokens. 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.
Other commands, from other repositories
test-suite
Run comprehensive test suite with coverage analysis.
ia-test-browser
Run browser tests on pages affected by current PR or branch.
test
Agentic end-to-end test orchestrator — drive web/macOS flows, evaluate UX rubric, reconcile issues.
qa-automation
Set up a comprehensive test automation strategy.
qa-e2e
End-to-End tests with Playwright or Cypress.
distill
Creates E2E acceptance tests in Given-When-Then format from requirements and architecture. Use when preparing executable specifications before implementation.