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Github Deep Review

CommunityPopular
steipete
github-deep-review

GitHub deep review: bugs, PRs, best fix, stale-or-real, read code first.

Overview

Publishersteipete
Repositoryagent-scripts
Skill namegithub-deep-review
Stars
6.6K
Forks
547
Bundled files
1
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 1 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by steipete on GitHub. Read the source before you install it.

Installation

Install the Github Deep Review AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/steipete/agent-scripts.git /tmp/agent-scripts
mkdir -p .claude/skills
cp -r /tmp/agent-scripts/skills/github-deep-review .claude/skills/github-deep-review
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Github Deep Review in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Github Deep Review on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Github Deep Review is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

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:

bash
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:

bash
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.

Code Reading Depth

Read past the first touched file. Follow the real call path:

  • entrypoint -> validation/parsing -> routing/dispatch -> owner module -> shared helper -> persistence/network/runtime boundary
  • config/schema/docs -> runtime usage -> doctor/migration/fix path
  • provider/channel/plugin owner code -> generic core seam, only if multiple owners need it
  • tests around the touched surface plus adjacent regression tests

When behavior depends on a dependency, read the upstream docs/source/types or current package contract before assuming.

Prefer current source and executable proof over issue comments. Treat stale comments, old CI, and old release behavior as hints until rechecked.

Provenance

For bug/regression reviews, include a compact Provenance: answer when feasible:

  • Use git log -S/-G, git blame, and linked PRs/issues to locate candidates, not prove introduction. Before saying introduced by, inspect raw parents with git --no-replace-objects cat-file -p <sha> and verify that git --no-replace-objects diff --no-ext-diff --no-textconv <raw-parent> <sha> -- <path> changed the implicated behavior, using tests/repro when feasible. A genuine root needs raw-header proof that it has no parents.
  • Blame ^sha, porcelain boundary, and shallow/grafted history alone are not introduction proof. --root can hide boundary markers; git show and rev-list --parents can disguise a shallow boundary as a root. An available raw parent permits explicit comparison even at a shallow boundary; missing parents or an unverifiable patch require unknown with the gap, not inference from a subject, date, or author.
  • Separate code author, introducing PR author, merger, committer, automation trigger, and current PR author. Verify identities and triggers from explicit metadata/events; a role is not proof of causation, and an unverified identity stays unknown.
  • Use made visible by only for a verified trigger and carried forward by only for verified preexisting behavior. Apply the same evidence bar to summaries and owner hints, not just a Provenance: field. Include confidence: clear, likely, or unknown.
  • For features, docs, and refactors, write N/A; for untraceable bugs, report unknown with the missing evidence. Missing provenance does not invalidate an independently proven bug.

Fix Quality Bar

Good fixes usually:

  • live at the ownership boundary where the bug belongs
  • preserve public/backward-compatible behavior unless the issue is about retiring it
  • add a regression test at the smallest meaningful seam
  • avoid broad special cases, hidden migrations, semantic sentinels, and provider/channel IDs in generic core
  • update docs/changelog when user-visible behavior changes
  • fail clearly in runtime paths and repair through doctor/migration paths when that is the established contract

Call out when a fix is only symptom-level. If a slightly larger refactor makes the invariant obvious and reduces future bugs, recommend it. If the refactor widens risk without improving the bug class, say so.

PR Review Shape

Lead with findings when reviewing a PR. Findings need file/line/symbol references and a concrete failure mode. Avoid vague "consider" comments.

If no blocking issues:

  • say no blocking correctness issues found
  • list the strongest proof checked
  • name residual risk/test gaps
  • answer whether the design is the best available shape

Do not approve, comment, close, merge, push, or land unless the user asked for that action.

Issue Review Shape

For bugs/issues:

  1. Reconstruct the reporter's scenario and affected version/surface.
  2. Check whether current main already fixes it.
  3. Reproduce or create a minimal local/live proof when feasible.
  4. If clear, identify root cause and proposed fix.
  5. If solved on main, only comment/close when the user asks; include proof and the canonical commit/PR if known.

If reproduction is not feasible, say exactly what blocks it and what evidence would make the decision reliable.

Output Template

Use this shape when the user asks "what is this about", "is this the best fix", or "what did we fix":

text
Ref: #123 / PR #456
Surface: <runtime/CLI/provider/channel/docs>

Bug: <one or two sentences>
Cause: <code path + confidence>
Provenance: <introduced/made visible/carried forward by commit/PR/date, or N/A/unknown>
Best fix: <what should change and why>
Refactor: <yes/no, specific shape>
Proof: <tests/live/CI/source/dependency docs>
Risk: <remaining uncertainty>

Keep it concise, but do not skip the cause/fix/refactor/proof decision.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Github Deep Review AI skill do?

GitHub deep review: bugs, PRs, best fix, stale-or-real, read code first.

Why use Github Deep Review on TypingMind?

Because you install it once and use it with any model. Github Deep Review is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Github Deep Review in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/steipete/agent-scripts/tree/main/skills/github-deep-review. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Github Deep Review?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Github Deep Review?

As many as you like. As long as a model supports skills, you can use Github Deep Review with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Github Deep Review AI skill free?

Yes. It is published on GitHub by steipete under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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