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概要と使いどころ
QA test a live website with Firecrawl browser and scrape evidence. Use when the user wants exploratory QA, form testing, navigation/link checks, responsive checks, performance observations, bug reports, or a pre-launch quality review.
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Produce an intensive, cited analytical report: executive summary, multi-angle findings, contrarian views, open questions, and full sources. Use only when the user needs rigorous synthesis of a complex topic (scientific, technical, policy, or market-analytical) that cannot be answered with a short search, and wants a formal written report, not a recommendation list. Do not use for product picks, top-N lists, quick lookups, or routine "find out about X" tasks. If the request does not clearly need this kind of report, do not use this skill. Do not use for a literature review over published papers. This skill collects evidence from the open web. A request for the literature on a biomedical, clinical, life-science, or other scientific topic — papers, studies, trials, preprints — belongs to firecrawl-research-papers, which queries Firecrawl's paper index (PubMed, bioRxiv, medRxiv, arXiv) instead of searching websites.
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。
Search an index of public repositories, GitHub issues, merged pull requests, repository READMEs, and curated documentation sites. Use when a programming question needs external documentation or upstream evidence.
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。
Perform QA testing on running web applications using cmux_browser. Covers functional testing, accessibility auditing, edge case testing, and structured reporting with evidence. **Triggers — use this skill when:** - User says "test this", "QA this", "QA this PR", "check the app" - User says "run QA", "verify the build", "test the UI" - User asks to "check if it works", "validate the feature" - User says "acceptance testing", "smoke test", "regression test" - User asks to "find bugs", "test for bugs", "break it" - User wants to "check accessibility", "run axe", "a11y audit" **Covers:** Any web application accessible via HTTP. Uses cmux_browser (Playwright under the hood) for all browser interactions.
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 when the user wants to know if something works — the answer requires running code, not analyzing it. Output is a verdict backed by evidence: passed, failed, or broken. Primary triggers: 'run the tests', 'does X still work after my change?', 'did the merge break anything?', 'verify the fix worked', 'check if the endpoint returns X', 'confirm nothing regressed', 'run tests/unit/test_foo.py', 'let me know the results', 'make sure my changes didn't break anything'. Hard stops — do NOT use for: reviewing test code for quality/coverage gaps, debugging why test infrastructure/databases/seed scripts are misbehaving, writing or fixing tests, diagnosing root causes of unexpected behavior. The deciding question: is the user asking for the result of executing something, or asking for help understanding/analyzing/improving something? If it's the latter, use diagnose or review-code instead.
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。
Fix bugs and broken behavior when there is enough evidence to act on a repair path. Use for errors, crashes, incorrect results, API failures (500, 404, 403), CORS problems, database exceptions, broken rendering, duplicated or wrong data, off-by-one mistakes, timezone/date bugs, broken forms, config-caused runtime failures, and regressions. Trigger when the user wants the bug repaired and the conversation already contains a clear failing area, a reproducible failing test, a concrete error path, or a prior diagnosis to implement. Do NOT use for new features, pure explanation, architecture discussion, broad research, or bug reports where the main need is figuring out why the behavior happens — use diagnose for that.
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。
Verify before claiming completion. No claims without fresh verification evidence. Use before marking work done.
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。
Select one target opportunity from an OST using evidence-based weighted scoring and tie-breakers.
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 when a design task asks you to compare a built screen with its approved design - screenshots at 1440, 768 and 375 attached to the task, Blocker/High/Medium/Nitpick triage, evidence per finding, problems not prescriptions
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 when you want deterministic evidence for a review - which already-installed scanners may be run against the checkout, their exact read-only invocations, how to triage their output, and what must never be run or installed
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 when your context carries "The approved design this task builds" - comparing the running UI with the approved hand-off and mockup at each width and state, evidence screenshots attached to the task, Blocker/High/Medium/Nitpick findings and which of them fail the round
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 when a release reaches awaiting_verdict (or you are woken for an incident) - how to read runtime logs, error groups, health and smoke evidence for the release's component and decide finish, re-check, roll back or hand to a human
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 when the task touches a backend API, database or worker - booting it from the task branch, driving real requests and verifying side effects (DB rows, outbound calls, logs) as evidence
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 before any review_criterion approval or column move - the evidence each verdict needs, how to treat skipped, blocked and intermittent cases, and which exit a blocker takes
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。
Evidence rules for completion claims. Use before ticking an acceptance criterion, ending an implementation run, or writing anything that says something works, passes or is fixed.
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。
Find, rank, and safely remove accidental codebase complexity by proving real consumers, dynamic entrypoints, compatibility obligations, duplicate representations, speculative surfaces, and lifecycle ownership. Use when asked to simplify or clean up a repository, reclaim code entropy, reduce over-engineering or redundancy, find deletion candidates, collapse duplicate state/APIs, remove dead or added-then-abandoned code beyond static-tool output, or implement an evidence-backed simplification pass in any language or stack. Also trigger for 代码化简、熵回收、删代码、清理冗余、收敛抽象、去除过度设计. Do not use as a performance audit unless simplification is the stated goal.
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。
Phase E of a WinForms-to-Avalonia migration: verify behavioral parity against Phase A contracts, record intentional differences, validate supported environments and packaging, and make evidence-based legacy cutover decisions. Use only for an explicit migration when comparing the legacy and Avalonia implementations or when the orchestrator routes here. Do not delete legacy without the cutover checklist and explicit authorization.
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。
Phase A of a WinForms-to-Avalonia migration: inventory the legacy application, split responsibilities, capture command and state contracts, build a coverage matrix, and define acceptance evidence without implementing Avalonia UI. Use only in an explicit WinForms-to- Avalonia migration when behavior is undocumented or the orchestrator routes here. Generated artifacts must use the current repository's real paths, terminology, and capabilities.
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。
Phase F of a WinForms-to-Avalonia migration: evolve the Avalonia trunk after cutover without reintroducing WinForms coupling or bypassing established layers. Use only when an explicit migration has an active Avalonia trunk and a concrete product backlog, or when the orchestrator routes here. Select work from repository evidence; do not assume particular entry points, integrations, settings, or extension mechanisms exist.
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 when asked to prove, disprove, investigate, or find evidence for a claim about people, concepts, ideas, or their relationships. Also use when asked to explore connections, find who influenced what, trace how ideas developed, or answer questions that require reasoning over multiple linked notes.
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。
Deep research with web search, source verification, and fact-checking. Runs before humanize + kami in the document pipeline. Use when user asks to research a topic, verify facts, gather sources, or do deep web investigation. Covers source validation, citation, and evidence hierarchy.
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 when diagnosing Superloopy doctor, install, wrapper, plugin cache, hook bootstrap, bundled agents, marketplace, Codex, Claude Code, stale-version, evidence-floor, or host-wiring health problems.
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 Superloopy's lightweight strict-evidence loop for Codex tasks that need durable progress, criteria, and artifact-backed completion.
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。
Korean prose humanizer for Codex and Superloopy that rewrites Korean text so it sounds naturally human while preserving meaning, register, facts, protected tokens, and evidence. Use when the user asks to remove Korean AI tells, make Korean copy sound human, fix 번역투, remove ChatGPT/Claude/Gemini tone, polish Korean without changing meaning, or says "AI 티 없애줘", "AI 윤문", "번역투 고쳐", "사람이 쓴 것처럼", "humanize Korean", "한글 AI 티 제거", "글 자연스럽게 다듬어줘". Inspired by Korean AI-tell ideas from https://github.com/epoko77-ai/im-not-ai. Handles Korean rewriting only; not for translation, fact expansion, SEO rewriting, legal drafting, or generic proofreading.
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。