Audit websites for accessibility issues and WCAG compliance. Use when checking accessibility, fixing a11y issues, or ensuring WCAG compliance.
日本語の概要は準備中です。原文の説明を表示しています。
Use to run real static analysis over a diff or repo before shipping — Semgrep, CodeQL, secret scanning, dependency CVEs — and triage the findings into what must be fixed now versus noise. Trigger before a release or PR merge, during a security review, or when the user asks to scan the code for vulnerabilities.
インストールする前に、エージェントに与えられる指示の中身を確認できます。
Reading code for bugs finds what you thought to look for. A scanner finds the rest. This skill runs the tools, then does the part the tools cannot: deciding which findings are real.
Scan the diff first, the repo second. Triage every finding. Never hand over a raw scanner dump — an unfiltered report is how real findings get ignored.
Semgrep — fast, pattern-based, good defaults, no build required.
semgrep --config=auto --error --quiet . # whole repo
semgrep --config=auto --quiet --baseline-commit=origin/main # diff only
Secret scanning — the highest-value scan per second spent.
gitleaks detect --no-banner --redact
git log -p -S'BEGIN PRIVATE KEY' --oneline | head
Dependency CVEs
npm audit --audit-level=high 2>&1 | tail -40
pip-audit 2>&1 | tail -40
CodeQL — deeper, needs a build, worth it on a release branch rather than every PR.
codeql database create db --language=javascript --overwrite
codeql database analyze db --format=sarif-latest --output=results.sarif
If a tool is not installed, say so and run what is available rather than silently skipping the whole sweep.
Every finding lands in one of four buckets:
Fix now — reachable from untrusted input and causes real damage: injection into a query or shell, authentication or authorization bypass, secrets in the repo, deserialization of user data, path traversal, SSRF against internal hosts.
Fix soon — real weakness, limited blast radius: missing rate limit, weak crypto choice, permissive CORS on a non-sensitive route, a dependency CVE with no exploit path.
Won't fix, explained — the pattern matched, the exploit does not exist here. Write the one-line reason. This is the bucket that earns trust in the report.
Noise — test fixtures, generated files, vendored code. Suppress with a targeted ignore, never by turning the rule off globally.
For each Fix-now item, prove reachability before claiming it: name the entry point, the path the data takes, and the sink. A finding you cannot trace is Fix-soon at best.
If a live credential is found in the working tree or in history, stop and tell the user immediately. Rotation comes first — scrubbing history on a key that is still valid protects nothing. Never print the secret back in full.
## Scan — <scope> — <date>
Tools: <which ran, which were unavailable>
### Fix now (n)
1. **<issue>** — `file:line`
Path: <entry point> → <sink>
Fix: <the concrete change>
### Fix soon (n)
- <issue> — `file:line` — <why it is limited>
### Reviewed, not an issue (n)
- <finding> — <why it does not apply here>
Ranked by severity, not by file order. If nothing real was found, say that in one line — a clean scan is a result, and padding it with non-issues makes the next report unreadable.
まだレビューはありません。使ってみた感想をお寄せください。
概要と使いどころ
Audit websites for accessibility issues and WCAG compliance. Use when checking accessibility, fixing a11y issues, or ensuring WCAG compliance.
日本語の概要は準備中です。原文の説明を表示しています。
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.
日本語の概要は準備中です。原文の説明を表示しています。
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.
日本語の概要は準備中です。原文の説明を表示しています。
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.
日本語の概要は準備中です。原文の説明を表示しています。
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.
日本語の概要は準備中です。原文の説明を表示しています。
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.
日本語の概要は準備中です。原文の説明を表示しています。