65 件 ・ 関連度順
概要と使いどころ
Optimize an article for Answer Engine Optimization (AEO) so AI engines like ChatGPT, Perplexity, and Claude can extract, quote, and cite it. Use when asked to AEO-optimize, make content AI-readable, improve AI citation chances, or adapt an article for answer engines. Produces an AEO-optimised rewrite with question headings, 50–80 word answer capsules, a paragraph-length audit, and flagged trust signals.
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。
Adversarial senior review of the spec before any code is written. Constructs a skeptical reviewer whose authority comes from the codebase, §R research, and live best-practice — then tries to REFUTE the spec, not rubber-stamp it. Every finding cites evidence (file:line or source); unverifiable ones are flagged. Survivors harden §V; the run ends in an explicit go / no-go gate. Triggers before building anything high-blast-radius, when the user says "review the spec", "red-team this", "is this plan sound", "senior review", or invokes /ck:review.
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。
Gather external knowledge the spec needs and distill it into §R — the durable research log — so build grounds in facts instead of hallucinating library behavior. Each finding cites a source; unsourced claims are flagged, never written as fact. Triggers when a spec decision hinges on a library/API/best practice the agent is unsure of, when the user says "research this", "what's the best lib for…", "check current best practice", or invokes /ck:research. Defers the §R write to the spec skill.
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。
Adjudicates whether a hit generated by sanctions, PEP, or adverse-media screening is a true positive, false positive, or requires human escalation. Use whenever a user presents a screening alert, a name match against a watchlist (OFAC SDN, EU consolidated list, UK OFSI, UN list, PEP list, adverse media hit, etc.), or asks to clear a screening hit / reduce false positives / determine whether a flagged name is actually the listed party. Use even when the user describes the task casually — "is this person actually on the sanctions list", "did we get a real match", "clear this alert", "I have a hit on X" — these are all screening-adjudication tasks. Produces a deterministic determination with full audit trail (structured JSON + human-readable narrative). Designed for use by compliance analysts and screening systems.
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。
Helps the user come up with free lead magnets or hooks to offer in cold emails. Asks about their business, what they can legally give away, and what would be genuinely valuable to their ICP. Returns 5-10 concrete offer ideas with example CTA phrasings. Use when the user says their campaign "needs a hook", "needs an offer", "what can I give away", or when ICP onboarding flagged a missing lead magnet.
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。
Write Reddit posts and comments that recommend affiliate products without getting banned or flagged as spam. Subreddit-native content that adds value first. Use this skill when the user asks about Reddit posts for affiliate marketing, writing Reddit comments that mention products, how to promote affiliate links on Reddit, or says "write a Reddit post for X", "how to mention affiliate on Reddit", "Reddit comment promoting product", "Reddit-friendly affiliate content", "post for r/[subreddit] about X", "share affiliate link on Reddit without getting banned", "genuine Reddit recommendation", "organic Reddit affiliate post", "Reddit thread idea for product".
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。
敏感词过滤器。过滤文本中包含敏感词/标记词比例超过指定阈值的样本。 当用户提到敏感词过滤、脏词过滤、违禁词过滤、内容审核、敏感词检测等需求时使用此skill。 即使用户没有明确说出"敏感词"，只要任务涉及过滤包含特定敏感词的文本内容， 就应该使用此skill。
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。
Sweep your folders and downloads for tax documents and build a CPA-ready package -- checklist by form type, everything renamed and organized, a missing-document chase list, and flagged items worth asking your accountant about. Organization, not tax advice.
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 manuscript or response-to-reviewers letter reads as AI-written. Scans for 27 AI writing patterns and rewrites flagged passages, preserving technical accuracy and bounding how much text changes. Not general copy-editing; that is /polish-language.
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。
Social Media Publisher. Takes approved content from outputs/ and schedules it across platforms via Blotato MCP. Generates infographic-style visuals (stat cards, framework diagrams, process graphics, quote graphics) for posts flagged by platform-specialist skills. Requires Blotato MCP to be configured. Run after /linkedin-writer, /threads-writer, /x-writer, or /caption-writer.
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。
Decide whether a preview the visual-diff bot flagged actually regressed or is simply nondeterministic, using a repeat-render oracle at a single commit. Use when a PR's preview diff reports a changed preview whose source the PR does not touch, or when a render, GIF or filmstrip is suspected of being unstable.
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。
Builds and audits a concept-ontology for a regelrecht corpus to decide and verify reference types — cross-law source-binding vs intra-law source-binding vs open-norm leaf vs external-fact leaf — and to catch concept conflation across laws. Core principle: a value's reference type is fixed by WHO derives it, and a binding is sound only when producer and consumer denote the SAME concept; the lexical surface (the word in the text) is not the concept. Detects false friends (one term, different concepts — e.g. a register-based vs a factual "naar de omstandigheden"-based residence/ingezetene concept), orphan plain-params (a value a regulation actually derives, modeled as a brute input), negation/complement re-entry (¬X modeled as an independent leaf next to X), and duplication (one concept re-entered as several independent leafs, losing the single-source-of-truth invariant). Produces a law-level glossary and a stelsel-level concept map with sound same-concept binding edges and flagged lexical-match-but-concept-mismatch edges. Use when deciding whether a value should be cross-law/intra-law/leaf, when the same term means different things across laws, or when a binding might silently conflate concepts.
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。
Triages an Aikido security findings CSV against a local codebase. For each finding, reads the flagged file, traces the code path, and verdicts it as KEEP OPEN or CLOSE with a specific reason. Outputs a reviewed CSV and a self-contained HTML evidence report. Run this at the end of a pentest when an Aikido CSV is available.
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。
Generate a prioritized review queue of registry skills and catalog items that need attention. Use this skill whenever someone asks: "what needs review?", "what's overdue for audit?", "where are the weak spots in the registry?", "what should I audit next?", "run a health check on the registry", "show me flagged skills", "find problems in the registry", "what skills need fixing?", or "triage the registry". This is the triage layer — it surfaces candidates ranked P0–P4 so that focused work (via /gaia-audit) targets the right things first. Run this before any audit pass. Also invoke proactively when a PR changes registry/named/ at scale, or before a release, to catch regressions early.
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。
Skippable Phase 0 of the Gaia evidence verification pipeline. Use only on declared need for higher-quality Stage-2 evidence: a promotion candidate, or a skill that gaia-meta-sweep flagged as under-evidenced. Given a skill id, source repo/context, and needed evidence types (benchmark-result, arxiv, peer-review, social-signal), it runs Firecrawl searches, scrapes top hits to markdown, and appends discovered sources into Phase 1 inputs. Missing benchmark evidence is recorded as benchmark-source candidates first, not direct scoring rows. Trigger phrases: "discover evidence", "find new evidence", "Phase 0", "run ev-discovery", "search for benchmarks/papers", "under-evidenced", "promotion candidate needs stronger proof". This is the ONLY ev-* skill that searches the web for NEW evidence; other ev-* skills aggregate or verify known sources. Requires Firecrawl to be usable (`firecrawl --status` authenticated OR FIRECRAWL_API_KEY set); skips gracefully when neither holds.
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。
Detect AI-generated content using pure text analysis (no external API calls). Analyzes sentence uniformity, vocabulary diversity, repetition patterns, hedging language, paragraph uniformity, and personal experience signals. Returns a 0-100 confidence score plus flagged passages. Use when user says "AI content check", "AI detection", "AI written", "content authenticity", "AI content", "detect AI".
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 task needs the judgment of a Dragline and Excavating/Loading Machine Operator in surface mining — converting a rated bucket capacity into bank cubic yards moved per shift, deciding whether a swing angle or spoil-placement plan needs to be flagged to pit engineering, matching loading pass count and payload to a haul truck's rated capacity, checking ground bearing conditions before walking or tracking a machine onto fill or spoil, or reading a berm/highwall condition against MSHA thresholds.
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 task needs the judgment of a Camera Operator (television, video, or film) — deciding how to physically execute a director/DP's shot on a moving or stabilized platform, reframing live for unpredictable subject movement, protecting a frame for multi-format delivery, or diagnosing why a take got flagged for reshoot.
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。
Render the GAME-DESIGN document (the GDD) into a human-readable form — read-only Markdown chapters under docs/design/ (index, systems, flows, content, economy, glossary), with facts resolved inline from GD-IDS.yaml and drafts flagged 🚧. Optional --web also renders an HTML site from the unikit-docs template (falls back to Markdown-only if the template is absent). Use to export or publish the design for people to read, e.g. "render the GDD", "export the game design to docs", "generate readable design docs", "publish the GDD", "make a design doc site". Read-only — it never edits the GDD or registry, only renders it. This renders the game DESIGN — for the project's CODE documentation (README, docs/*, modules, architecture) use /unikit-docs.
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。
White-box security audit. Blue-teamer and lead red-teamer run in parallel isolation for an independent first pass — neither sees the other's output during reconnaissance. A synthesis step categorizes findings into four prescriptive buckets (anchoring-suppressed, convergent, blue-flagged-unverified, divergent), producing a unified target list. Focused red-teamers then deep-dive each target. Iterates when exploit chains are discovered. Heavy and thorough by design. Advisory only — produces findings and proposes tickets; does not implement remediation.
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。
One page, hands-on manual tier — drive a live page through the web accessibility (a11y) checks a rule engine can't decide: keyboard operation and focus order, screen-reader names, roles and states from the accessibility tree, reflow and zoom, reduced motion, form errors, and target size. Grades each finding by evidence basis (verified / confirm-with-a-human / human-required) and severity, and closes every criterion in a ledger: verified, flagged, not exercised, or N/A. Locates and assesses; does not fix (use `accessibility-fix`). Use it for keyboard testing, focus-order checks, screen-reader or a11y-tree review, reflow and zoom at 200%, or 'is this operable, not just lint-clean'. The automated tier is `accessibility-scan`; `accessibility-audit` runs both across a sampled site.
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。
Resolve CodeRabbit review feedback on a GitHub pull request. Collects every open CodeRabbit finding (inline threads, plus the nitpick and outside-diff findings packed into review bodies), judges and fixes each one against the real code with subagents, unifies fixes that share a pattern, commits locally, asks before pushing, then shows every reply draft linked to its finding and posts only on approval. Runs only on top-tier models. Use on "resolve the CodeRabbit feedback", "address coderabbit comments", "answer the rabbit", "fix what CodeRabbit flagged on
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。
Apply this opinionated workflow when modernizing legacy systems: strangler fig pattern, branch by abstraction, characterization tests, incremental monolith decomposition, framework upgrades, feature-flagged migration with rollback.
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。
Produces a Rule 506(d) bad actor disqualification review for private securities offerings, including a covered persons register, tailored questionnaires mapping to each disqualifying event category, a documented reasonable-care diligence record, and classification analysis of flagged events with remediation or disclosure recommendations. Use this skill when conducting Rule 506(d) diligence, identifying covered persons for a Regulation D offering, drafting bad actor questionnaires, or analyzing potential disqualifying events involving criminal convictions, SEC orders, SRO sanctions, or state regulatory actions. Also trigger when the user mentions bad actor screening, covered persons analysis, Rule 506(d) questionnaire, disqualification waiver, or reasonable care defense. Even if the user just says "bad actor check" or "506(d) diligence," use this skill.
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。