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Reviewing Code Changes

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trilwu
reviewing-code-changes

Perform a security review of a diff, branch, or pull request — assessing what the change introduces, weakens, or exposes, with a triage-first workflow and false-positive discipline. Use when asked to security review a PR or branch, check a diff before merge or commit, or assess whether a change is safe to ship.

Overview

Publishertrilwu
Repositorysecskills
Skill namereviewing-code-changes
Stars
144
Forks
15
Bundled files
Instructions only
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.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

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

Installation

Install the Reviewing Code Changes 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/trilwu/secskills.git /tmp/secskills
mkdir -p .claude/skills
cp -r /tmp/secskills/secskills-core/skills/reviewing-code-changes .claude/skills/reviewing-code-changes
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Reviewing Code Changes 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 Reviewing Code Changes 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 Reviewing Code Changes 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.

Reviewing Code Changes

Reviewing a diff is not auditing a codebase in miniature. The diff shows you what changed but hides what the change means — the caller that now receives untrusted data, the check that used to run, the assumption that no longer holds. Reviewing only the added lines is the standard way to miss the bug.

When to Use

  • Security review of a pull request, branch, or commit range
  • Pre-commit or pre-merge check on your own changes
  • Assessing risk of a dependency bump or config change
  • Reviewing an AI-generated change before it ships

When NOT to Use

  • Whole-codebase audit — use auditing-code-for-vulnerabilities
  • Runtime testing of the deployed change — use the relevant testing skill
  • Dependency/CI changes as the main subject — use auditing-supply-chain

Get the Real Diff First

bash
# Against the actual merge base, not against whatever HEAD happens to be
BASE=$(git merge-base HEAD origin/main)
git diff --stat $BASE...HEAD
git diff $BASE...HEAD

# Rename and whitespace noise hides real changes
git diff -M -C -w $BASE...HEAD

# Files changed most, and by whom — where to spend attention
git diff --numstat $BASE...HEAD | sort -rn | head -20

Three things to establish before reading code:

  1. What is this change trying to do? Read the PR description and the tests. A security review without the intent is pattern matching.
  2. Does the diff match the description? Unexplained files — a config change, a new dependency, a workflow edit riding along with a feature — are the highest-signal thing in a review.
  3. What is the change's exposure? A change to an unauthenticated request path deserves an order of magnitude more attention than an internal refactor.

Triage: Where to Spend the Review

Not all diffs deserve equal time. Rank hunks by what they touch:

PrioritySignals
CriticalAuth/authz logic, session handling, crypto, input parsing on an untrusted boundary, SQL/command/template construction, file path handling, deserialization, CI/CD workflows, IAM/security-group config, dependency additions
HighNew endpoints or routes, new tool/RPC surface, error handling on security paths, logging of user data, tenant scoping, cache keys, rate limits
MediumBusiness logic that consumes validated input, refactors that move security-relevant code
LowTests, docs, formatting, comments — but read test deletions
bash
# Fast triage of a large diff
git diff $BASE...HEAD --name-only | rg 'auth|login|session|crypto|password|token|admin|permission|\.github/workflows|Dockerfile|terraform|policy'
git diff $BASE...HEAD -U10 | rg -n '^\+.*(eval\(|exec\(|system\(|innerHTML|pickle|yaml\.load|Sprintf.*SELECT|shell=True|verify=False|InsecureSkipVerify)'

What to Look For

Removals and weakenings — read the minus lines first

The most dangerous diffs delete things. Search the removed lines specifically:

bash
git diff $BASE...HEAD | rg '^-' | rg -i 'auth|verify|valid|check|sanitiz|escape|permission|assert|csrf|limit'
  • A validation call deleted "because it was redundant"
  • An authorization decorator removed during a refactor
  • A test deleted rather than fixed
  • A TODO: add authz that was never done and is now shipping
  • A timeout, size cap, or rate limit removed
  • strict mode, a security header, or a CSP directive relaxed

Additions — the ordinary classes, in context

Apply the bug-class hunting from auditing-code-for-vulnerabilities, but scoped to the changed paths: injection sinks, authorization on new object lookups, SSRF in new outbound calls, path handling on new file operations, deserialization, secrets, weak randomness, unsafe defaults.

The two questions that catch most real issues in a diff:

Does new untrusted data reach an old sink? Does old untrusted data reach a new sink?

Both require reading outside the diff.

Context beyond the diff

Read the whole function for every non-trivial hunk. Then check callers:

bash
# Who calls the changed function, and do they still satisfy its assumptions?
rg -n 'changedFunctionName\s*\(' --type <lang>
# What else in the codebase uses the pattern this change introduced?
git log -1 --format=%H $BASE  # anchor, then compare behaviour before/after

Specifically check whether the change alters an invariant that other code relies on: a function that used to sanitize and now does not, a return value whose meaning flipped, a nullable that became non-null, an ordering guarantee that was dropped.

Config, infra, and pipeline changes riding along

bash
git diff $BASE...HEAD -- '*.yml' '*.yaml' '*.tf' 'Dockerfile*' '.github/**' '*.json'
  • New or bumped dependencies: who maintains them, and does the lockfile change match the manifest change? (See auditing-supply-chain.)
  • Workflow permissions widened, pull_request_target added, unpinned actions
  • Security group / firewall rules opened, buckets made public, IAM "*"
  • Debug flags, verbose logging of request bodies, disabled TLS verification
  • Feature flags that default to on

AI-generated and large mechanical changes

Machine-written diffs fail in characteristic ways: plausible-looking validation that does not actually constrain, error handling that swallows security-relevant failures, invented API usage that silently no-ops, and copied patterns applied where the surrounding assumptions differ. For a large generated diff, sample the security-relevant hunks and verify against the real library behaviour rather than reading for plausibility.

Verify Before Reporting

Diff review generates false positives faster than any other review mode, because the missing context is exactly what makes a hunk look wrong. Before raising a finding:

  1. Read the surrounding function and the callers. The check you think is missing is often two frames up.
  2. Check the framework's behaviour. Many "missing escaping" findings are handled by the template engine.
  3. Confirm reachability with the caller privilege required.
  4. Look for the same pattern elsewhere in the codebase. If it is everywhere and predates this PR, it is a codebase finding, not a PR blocker — say so, and file it separately.

Then state the finding with a concrete failure scenario: the input, the path, and the result. "This could be unsafe" is not a review comment; it is a request for someone else to do the analysis.

Output

Separate what blocks the merge from what does not:

markdown
## Security review — PR #482

**Blocking**
1. `api/reports.go:214` — export handler drops the tenant predicate present on
   the list path. Any authenticated user can export another tenant's report by
   ID. Add `AND tenant_id = ?` to the lookup.

**Non-blocking, should fix**
2. `api/reports.go:190` — report IDs are sequential, making enumeration
   trivial once (1) is fixed. Consider opaque IDs.

**Notes / pre-existing**
3. The `findByID` pattern without scoping appears in 14 other handlers and
   predates this PR. Filed as ISSUE-991 rather than blocking here.

**Reviewed but clear**
- New `/webhooks/stripe` route: signature verified before body parse, replay
  window enforced. No finding.
- Dependency bump `lodash 4.17.20 → 4.17.21`: patch for CVE-2021-23337, no
  API change.

**Not covered**
- The React changes under `web/` were not reviewed for XSS in depth; the diff
  touches 40 components.

Stating what you reviewed and found clean is as useful as the findings — it tells the next reviewer where not to spend time, and it makes the coverage of the review honest.

Rationalizations to Reject

  • "The diff looks fine." You reviewed the added lines. Read the removed ones and the callers.
  • "CI passed." CI runs the tests that exist for the behaviour someone anticipated.
  • "It's a small change." One deleted decorator is a small change.
  • "The author is senior." Review the code.
  • "It's just a refactor." Refactors move security-relevant code across trust boundaries more often than features do.
  • "That's pre-existing." True, and worth saying — but confirm it is pre-existing rather than assuming, and file it rather than dropping it.
  • "I'd need to understand the whole system to be sure." Then say what you could not determine. An honest uncertainty is a reviewable statement; silence reads as approval.

References

  • auditing-code-for-vulnerabilities — full audit methodology and bug classes
  • auditing-supply-chain — dependency and workflow changes in the diff
  • securing-ai-systems — reviewing changes to LLM/agent features
  • reporting-security-findings — severity and write-up when a finding leaves the PR

Frequently asked questions

What does the Reviewing Code Changes AI skill do?

Perform a security review of a diff, branch, or pull request — assessing what the change introduces, weakens, or exposes, with a triage-first workflow and false-positive discipline. Use when asked to security review a PR or branch, check a diff before merge or commit, or assess whether a change is safe to ship.

Why use Reviewing Code Changes on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/trilwu/secskills/tree/main/secskills-core/skills/reviewing-code-changes. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Reviewing Code Changes?

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 Reviewing Code Changes?

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

Is the Reviewing Code Changes AI skill free?

Yes. It is published on GitHub by trilwu 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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