Borrowing it
Nothing to install: this file belongs to zahardev/aicontext. 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/zahardev/aicontext/main/.claude/skills/gh-review-fix-loop/SKILL.mdgit 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/skills/zahardev/aicontext/gh-review-fix-loop)<a href="https://agentmods.dev/skills/zahardev/aicontext/gh-review-fix-loop"><img src="https://agentmods.dev/badge/skills/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/skills/zahardev/aicontext/gh-review-fix-loop"><img src="https://agentmods.dev/badge/skills/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 | $0.00053 | $0.00076 |
| Opus 5 | $0.00026 | $0.00038 |
| Sonnet 5 | $0.00011 | $0.00015 |
| Haiku 4.5 | $0.00005 | $0.00008 |
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 8d 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
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.
- 8d ago First seen · 7 lines · 53 tokens per session scan A 3ce6f4e6b15b
gh-review-fix-loop is a skill published in the GitHub repository zahardev/aicontext (2 stars, last pushed yesterday), licensed MIT. It adds 53 tokens to every session and 76 once invoked, about $0.0003 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.
Other skills, from other repositories
validate-changes
Evaluate staged changes using LLM-as-a-Judge before committing.
requesting-code-review
Use when implementation is done and you need a structured pre-PR review workflow. Triggers: 'ready for review', 'review my changes before PR', 'pre-merge check', 'is this ready', 'submit for review'. NOT for: post-merge review (use code-review) or deciding how to integrate (use finishing-a-development-branch).
ticketreview-commit
Thorough code review, verify requirements met, commit with detailed message.
github-code-review
Comprehensive GitHub code review with AI-powered swarm coordination.
review-delta
Review only changes since last commit using impact analysis. Token-efficient delta review with automatic blast-radius detection.
work-unit-commits
Plan commits as reviewable work units. Trigger: implementation, commit splitting, chained PRs, or keeping tests and docs with code.