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Code Review

Community
ericrisco
code-review

Use to judge a concrete diff, branch, or GitHub PR on its own merits with no rsc-SDD spec/plan chain to key off — the spec-less giving pass behind /code-review: only findings you can defend, one verdict, read-only unless --comment or --fix. NOT the SDD gate keyed to 02-DOCS/wiki/sdd/ that also processes incoming review comments (that is `review`).

Overview

Publisherericrisco
Repositoryrsc-harness
Skill namecode-review
Stars
239
Forks
24
Bundled files
2
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.

  • 2 bundled files

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

  • Open source

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

Installation

Install the Code 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.
    https://github.com/ericrisco/rsc-harness/tree/main/skills/code-review
  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/ericrisco/rsc-harness.git /tmp/rsc-harness
mkdir -p .claude/skills
cp -r /tmp/rsc-harness/skills/code-review .claude/skills/code-review
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Code 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 Code 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 Code 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.

Code review — standalone, spec-less diff judgment

You are reviewing a concrete change — a git diff, a branch, a GitHub PR, a pasted patch — on its own merits. No rsc-SDD spec/plan/constitution chain is required and you should not pretend one exists. This is the doctrine behind the executable /code-review slash command: same evidence bar, written as a discipline you run by hand. If the user is mid-SDD and wants to process incoming comments against 02-DOCS/wiki/sdd/, that is ../review/SKILL.md; a naked diff or an inbound third-party PR is this skill.

The north star is signal-to-noise. Report only findings you would stake your name on. A clean diff is APPROVE, not a manufactured nit. High-false-positive review gets tuned out by humans in about two weeks; the bar to aim for is the logic-error review where under 1% of findings come back marked wrong. Padding does not make you look thorough — it trains the reader to ignore you.

Get the change and its intent first

Three inputs, in this order: the diff, its stated purpose, and the touched surface (the files around the hunks, not just the hunks).

bash
# A GitHub PR
gh pr diff 1432
gh pr view 1432 --json title,body,files,additions,deletions

# A local branch against the trunk
git diff main...HEAD
git diff --stat main...HEAD   # see blast radius before reading

# A pasted patch — read it as given

A review with no notion of intent is a review of vibes. If no purpose is stated, infer it from the diff and say what you assumed ("Assuming this is meant to add idempotency to the webhook handler…") so the reader can correct a wrong premise — a silent wrong premise produces a confidently wrong review. Then read the whole changed file, not just the green/red lines: the structural failure of standalone review is judging a hunk without its context and shipping generic pattern-matched suggestions.

The pass order

Run these in order. Passes 1–5 are correctness/safety and are blocking-eligible; pass 6 is cleanup and is usually [should-fix] or [nit]. A clean pass is a reportable result ("contracts: nothing changed shape, no finding"), not a pass you silently skip.

#PassThe questionTypical defects
1Intent fidelityDoes it do what it claims?Wrong behaviour, missing case from the stated goal, scope creep
2Correctness & boundariesRight on the edges?Off-by-one, null/empty/unicode, overflow, timezone, concurrency, swallowed errors
3Contracts & dataDo callers/data still hold?Broken API shape, migration without backfill, nullable made non-null, enum drift
4Security boundaryUntrusted input → dangerous sink?Unsanitized input to query/shell/template, authz gap, secret in code/log
5Tests as evidenceDo the tests prove the change?Tests assert nothing, test the mock, miss the new branch, were deleted to go green
6Reuse / simplification / efficiencyCould existing code do this?Reimplemented helper, copy-paste divergence, N+1, needless allocation in a hot loop

Pass 4 is a boundary pass — trace untrusted input to its sink and flag the reachable ones. For a real STRIDE/OWASP threat model with exploitability ranking and vulnerable→fixed diffs, hand off to ../secure-coding/SKILL.md.

Adjacent jobs, delegated by name: running the lint/type/test gates until they are green is ../verify/SKILL.md (this skill judges whether green is correct); root-causing one confirmed failure is ../debug/SKILL.md; cross-checking spec/plan/tasks before code exists is ../analyze/SKILL.md.

Confidence floor and the false-positive skip-list

The 80% rule: if you are not at least ~80% sure a finding is real, you have two moves — trace the code until you are sure, or downgrade it to [question]. Never ship a guess dressed as a defect.

Skip these common false positives outright (or demote to [question]/[nit]):

  • Guarded upstream — the "missing" check happens in the caller you can see; trace before you flag.
  • Framework-enforced — the framework already does it (e.g. an ORM that parameterizes, a router that validates).
  • Behind an off-everywhere flag — real but unreachable in any deployed config → [nit], not blocking.
  • Test-only / generated code held to the prod bar — don't demand prod-grade error handling in a fixture or a generated client.
  • Style the linter owns — quotes, import order, line length. If a tool enforces it, don't spend a finding on it.

No severity inflation. Rank by blast radius × reachability, not by how clever the catch was. A typo in a log string is a nit even if it took effort to spot.

Severity and finding format

  • [blocking] — wrong/unsafe; merging causes a real defect. Must be fixed.
  • [should-fix] — a real problem with bounded blast radius; fix it or consciously accept it.
  • [nit] — minor; the reader may ignore it without consequence.
  • [question] — you suspect an issue but cannot prove reachability; asking, not asserting.

Every finding carries where / why / repro / fix:

text
[should-fix] api/orders.py:88 — duplicated total logic
  where:  `subtotal = sum(i.price * i.qty for i in items)` re-implements
          `cart.compute_subtotal()` (cart/totals.py:14), which also applies
          per-item discounts this copy silently drops.
  why:    discounted items now bill at full price on this path only;
          the two implementations will drift on the next discount change.
  repro:  order containing any item with `discount_pct > 0` → charged the
          undiscounted amount; covered by no test.
  fix:    call `cart.compute_subtotal(items)` instead of inlining the sum.

Rule: no repro or stated mechanism → it is a [question], not a blocker. "This could overflow" with no path is a question; "n*1000 with n up to 3M exceeds int32 at orders.py:51" is a finding.

Verify before you flag

Read the surrounding code, trace the value, confirm the path is reachable.

  • Bad: "Looks like SQL injection." (pattern-match)
  • Good: "search() interpolates req.query.q straight into db.execute(\… WHERE name='${q}'`)at search.ts:22;q` is unvalidated user input → injection." (traced)

If you cannot trace it to a concrete value and a reachable sink, you do not yet have a finding.

When several reviewers looked at the same diff, merge before reporting. Have each one end with a ```json findings block, run npx @ericrisco/rsc review consolidate <reports…>, and send each blocker it marks verify to the finding-verifier agent. That is the review skill's step, and it applies here unchanged.

The verdict

End every review with exactly one, plainly — no mushy middle:

  • APPROVE — no blockers, no should-fix. Point the user to ../ship/SKILL.md to merge.
  • APPROVE WITH NITS — mergeable; nits listed but none gate the merge.
  • CHANGES REQUESTED — at least one [blocking]. List precisely what unblocks it, so the author knows when they are done.

Effort dial

Mirror the slash command's effort level: low/medium → fewer, high-confidence findings (raise the confidence floor, focus on passes 1–4). high/max → broader coverage; uncertain findings are allowed but must be labelled [question], never inflated into blockers. This is coverage vs precision, not the user's register — it changes what you look at, not how you word it.

Emitting comments and applying fixes

Read-only by default. You produce findings + a verdict and stop there. Two opt-in modes:

  • --comment → post the findings as an inline-anchored review on the PR.
  • --fix → apply the agreed findings to the working tree.
bash
# Summary review (the verdict)
gh pr review 1432 --request-changes -b "CHANGES REQUESTED — see inline. Blocker: orders.py:88 …"
gh pr review 1432 --approve -b "APPROVE — correctness and contracts clean."

Inline line-anchored comments go through the GitHub REST API — see references/pr-workflow.md for the JSON shape, fork-PR handling, and large-diff strategy. If --fix puts you on the default branch, branch first; commit or push only when the user asks; git authorship is Eric (no Claude co-author or generated footer).

Anti-patterns

Failure modeReality
"It compiles and the tests pass, so it's correct."Tests prove green, not correct. Pass 5 asks whether the tests actually exercise the new branch — green for the wrong reason is a finding.
Listing everything you would have done differently.That is noise. Report defects and reuse wins you can defend; preference is not a finding.
"It's just a dependency bump, skim it."Bumps carry supply-chain and transitive risk and behaviour changes. Check the changelog/lockfile diff, not just the version string.
Applying every nit "to be safe" under --fix.Each unrequested edit is scope creep and a regression surface. Apply the agreed findings only.

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 Code Review AI skill do?

Use to judge a concrete diff, branch, or GitHub PR on its own merits with no rsc-SDD spec/plan chain to key off — the spec-less giving pass behind /code-review: only findings you can defend, one verdict, read-only unless --comment or --fix. NOT the SDD gate keyed to 02-DOCS/wiki/sdd/ that also processes incoming review comments (that is `review`).

Why use Code Review on TypingMind?

Because you install it once and use it with any model. Code 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 Code Review in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/ericrisco/rsc-harness/tree/main/skills/code-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 Code 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 Code Review?

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

Is the Code Review AI skill free?

Yes. It is published on GitHub by ericrisco 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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