Audit websites for accessibility issues and WCAG compliance. Use when checking accessibility, fixing a11y issues, or ensuring WCAG compliance.
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Performs a deep code review of files, modules, a diff, or a branch - finding security vulnerabilities (mapped to OWASP Top 10:2025), correctness bugs, performance problems, and maintainability issues - and returns severity-ranked findings with evidence and concrete fixes. Use when the user asks to review, audit, or sanity-check code, asks "is this safe", "what's wrong with this", "find bugs", or wants a security or performance pass before shipping. For posting line comments on a GitHub pull request, use git-pr-reviewer.
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Review the instructions the agent will receive before you install.
Find the problems that matter, prove each one, and show the fix. A short list of real issues beats a long list of maybes.
Set the scope. Decide what is under review: a pasted snippet, specific files, the working-tree diff (git diff, git diff --staged), or a branch against its base (git diff main...HEAD). For a diff, review the changed lines but read enough surrounding code to know how they are called.
Learn the context before judging. Identify language, framework and version (check package.json, pyproject.toml, go.mod, and so on), how the code is reached (HTTP handler, job, CLI, library), what input is untrusted, and any repo conventions (linters, CLAUDE.md, existing patterns). A pattern that is a bug in one framework can be safe in another; for example, React escapes JSX text, so XSS lives in dangerouslySetInnerHTML, href values, and raw HTML sinks.
Review in priority order, using references/checklist.md:
Verify every finding before reporting it. For each candidate, trace the data flow: where does the input come from, can an attacker or real user control it, and does anything upstream already validate or escape it? Check whether a test covers it. If you can run code, reproduce the bug with a small test or script. Drop findings you cannot support; mark the rest with a confidence level.
Rank and write the report in the format below. Lead with the highest severity. Group repeated instances of one problem into a single finding with all locations.
# Code Review: [scope]
**Verdict**: [Ship / Ship after fixes / Do not ship] - [one sentence why]
**Findings**: [n] critical, [n] high, [n] medium, [n] low
## Critical
### 1. SQL injection in user search (`src/api/users.ts:42`)
**Category**: A05:2025 Injection | **Confidence**: High
**Evidence**: `q` comes from `req.query` and is interpolated into the SQL string; no validation upstream.
**Impact**: Any caller can read or modify arbitrary tables.
Current:
```ts
const rows = await db.query(`SELECT * FROM users WHERE name LIKE '%${q}%'`);
```
Fix:
```ts
const rows = await db.query("SELECT * FROM users WHERE name LIKE $1", [`%${q}%`]);
```
## High
...
## Medium
...
## Low
- `utils/date.ts:10` - [one line]
## What is solid
[Two or three specific things done well, so the author knows what to keep.]
## Not reviewed
[Files, paths, or concerns outside scope or that could not be verified.]
useMemo/useCallback advice changes when the React Compiler is enabled, and many Node APIs now ship built-ins (fetch, crypto.randomUUID, node:test).No reviews yet. Tell others what you thought.
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Audit websites for accessibility issues and WCAG compliance. Use when checking accessibility, fixing a11y issues, or ensuring WCAG compliance.
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Deploy a 2-layer parallel agent hierarchy for large, parallelizable work — big refactors, multi-file migrations, codebase-wide audits, bulk generation. A top-tier commander (Fable or Opus) orchestrates the swarms; the user picks a power level (Max Power / Heavy / Balanced / Economy) that sets the Opus/Sonnet/Haiku model mix per layer. Layer 1 is 3-50+ specialist agents, each with its own full context window; Layer 2 is 2+ sub-agents per member. Includes git safety, tiered sizing, a pre-deploy gate, phantom-completion checks, and multi-wave follow-up.
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Use to test whether an AI agent, prompt, or skill actually works before or after it ships — build a small eval set, score runs, and catch regressions when the prompt or model changes. Trigger when the user asks whether a prompt is good, wants to compare models or versions, sees inconsistent agent output, or is about to put an agent in front of customers.
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Deploys swarms of sub-agents for massive parallel data processing tasks. Unlike agent-army (which is for code changes), this is for DATA tasks -- processing 1000 documents, analyzing datasets, bulk content generation. Configurable swarm size, task distribution, result aggregation, progress tracking, and error recovery.
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Designs and deploys custom agent teams for specific business workflows. Interactive discovery of business processes, then generates complete team configurations with specialized agent roles, tool access, communication protocols, and handoff rules.
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Agent-to-Agent (A2A) communication protocol. Connect two or more Claude agents that pass messages, share context, delegate tasks, and collaborate. Implements structured handoffs, shared memory, and multi-agent conversations.
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