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 skills add sublimecoder/aios --skill github-deep-reviewgit clone --depth 1 https://github.com/sublimecoder/aiosWrote 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/sublimecoder/aios/github-deep-review)<a href="https://agentmods.dev/skills/sublimecoder/aios/github-deep-review"><img src="https://agentmods.dev/badge/skills/sublimecoder/aios/github-deep-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/skills/sublimecoder/aios/github-deep-review"><img src="https://agentmods.dev/badge/skills/sublimecoder/aios/github-deep-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 | $0.00026 | $0.01269 |
| Opus 5.5 | $0.00010 | $0.00508 |
| Sonnet 5.5 | $0.00005 | $0.00254 |
| Haiku 4.5 | $0.00003 | $0.00127 |
Grade A, and why
github-deep-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 1mo 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 — 125 lines — stays where its author put it; the contents beside it link to each section on GitHub.
GitHub Deep Review
Review rigorously: high-confidence, evidence-first, code-aware, and willing to say "not proven" when the trail is weak. The goal is not a generic summary. The goal is to understand the bug class, find the real cause if possible, decide the best fix after reading enough code, and call out whether a larger refactor would improve the design.
Start
Use gh, not web browsing, for GitHub refs:
gh issue view <n> --json number,title,state,author,body,comments,labels,updatedAt,url
gh pr view <n> --json number,title,state,author,body,comments,reviews,files,commits,statusCheckRollup,mergeStateStatus,headRefName,headRepositoryOwner,url
gh pr diff <n> --patch
Treat every author uniformly. For PRs, collect author context by default from the gh JSON above — you already have the author login, PR history, and commit list. When it helps the review, pull prior contributions with gh api users/<login> or gh search prs --author <login> --repo <owner>/<repo>, and include a short Author context: block near the top of the review.
For repo-local review, also inspect:
git status --short --branch
git fetch origin
git log --oneline --decorate -20
rg "<key symbol/error/config/endpoint>"
If the repo has local instructions, issue/PR skills, docs lists, test guidance, or maintainer runbooks, read those before deciding.
Review Contract
Always answer these, explicitly:
- URL/ref: issue or PR number and affected surface.
- What is the bug or behavior being fixed?
- Can we identify the root cause? If yes, where in code and why. If no, what evidence is missing.
- For regressions, who/what introduced it and when? Include commit/PR provenance when traceable by bounded history; say unknown instead of guessing.
- Is the current/proposed fix the best possible fix after reading adjacent code?
- Would a bigger refactor improve correctness, clarity, or future maintainability?
- What proof exists: tests, live repro, CI checks, docs, dependency docs/source, shipped/current behavior.
- What remains risky or unverified.
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.
- 1mo ago First seen · 125 lines · 26 tokens per session scan A 0b6187b2ad58
github-deep-review is a skill published in the GitHub repository sublimecoder/aios (2 stars, last pushed 1mo ago), licensed MIT. It adds 26 tokens to every session and 1,269 once invoked, about $0.0001 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 2026-08-31.
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release-rollback
A release and rollback checklist for code or scripts, covering readiness checks, release decisions, and recovery after an incident.
release-pr-review
Review pass on an open release PR (release/ → main) — the step between git-wrapup and release-and-publish when a project releases in gated release PR mode. Reads the PR's commit range through the code-simplifier lens plus a correctness review, verifies whatever an automated reviewer left on the PR, lands fixes as…
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Reconstruct an OKF bundle by event-sourcing a repository's history (git log and Claude session transcripts). Use when creating an .okf/ bundle for an existing repository that predates this skill, or when resuming an interrupted backfill session. Triggers on: "reconstruct the OKF bundle", "backfill the knowledge…
prismer-github
GitHub via gh CLI: PRs, issues, reviews, repos, auth.