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/zahardev/aicontextWrote 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/zahardev/aicontext/gh-review-fix-loop)<a href="https://agentmods.dev/commands/zahardev/aicontext/gh-review-fix-loop"><img src="https://agentmods.dev/badge/commands/zahardev/aicontext/gh-review-fix-loop/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/zahardev/aicontext/gh-review-fix-loop"><img src="https://agentmods.dev/badge/commands/zahardev/aicontext/gh-review-fix-loop.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 | $0.00035 |
| Opus 5.5 | $0.00004 | $0.00014 |
| Sonnet 5.5 | $0.00002 | $0.00007 |
| Haiku 4.5 | $0.00001 | $0.00003 |
Grade A, and why
gh-review-fix-loop 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 26d 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
Read and follow .aicontext/prompts/gh-review-fix-loop.md
$ARGUMENTS
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.
- 26d ago Changed · -38 tokens per session 4d1257242ca6
- 1mo ago First seen · 8 lines · 47 tokens per session scan A d1c647bf5dd3
gh-review-fix-loop is a command published in the GitHub repository zahardev/aicontext (2 stars, last pushed 17d ago), licensed MIT. It adds 9 tokens to every session and 35 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 2026-09-08.
Other commands, from other repositories
guardian
Revisión de código IA como pre-commit hook — Guardian Angel.
codex-review
Run OpenAI Codex code review on current changes or a specific branch/commit.
request-review-plan
Request Code Review Phases 1-2: Planning scope and assembling reviewer context.
git
The pre-finish status: branch, hygiene findings, message checks, workflow lint, template state.
validate-pr-description
Use when validating a PR title and description for conventional commit format, issue linking keywords, and template compliance before submission.
address-pr-feedback
Systematically address PR review comments. Fetches all threads, categorizes by status (acknowledged, silently fixed, unaddressed), and guides user through posting replies and implementing fixes with explicit approval.