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 agentmods add skills/steipete/agent-scripts/github-deep-reviewnpx skills add steipete/agent-scripts --skill github-deep-reviewgit clone --depth 1 https://github.com/steipete/agent-scriptsWhat 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 | $0.00026 | $0.01529 |
| Opus 5 | $0.00013 | $0.00764 |
| Sonnet 5 | $0.00005 | $0.00306 |
| Haiku 4.5 | $0.00003 | $0.00153 |
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 2d 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 — 126 lines — stays where its author put it; the contents beside it link to each section on GitHub.
GitHub Deep Review
Review like Peter: 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
For PRs, collect author context by default unless the author is Peter (steipete or an obvious Peter-owned account). Use the local workflow in ~/Projects/agent-scripts/skills/github-author-context/SKILL.md and include a short Author context: block near the top of the review when the author is not Peter.
After merge/rejection/close/review, use that same author-context workflow to append a contributor note only when the interaction creates durable future-review signal.
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 ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 2d ago First seen · 126 lines · 26 tokens per session scan A bfad7e3dd87a
github-deep-review is a skill published in the GitHub repository steipete/agent-scripts (6,580 stars, last pushed 2d ago), licensed MIT. It adds 26 tokens to every session and 1,529 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 2026-08-30.
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