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概要と使いどころ
Security engineering that protects applications, data, and users from real-world threatsUse when "security, authentication, authorization, encryption, OWASP, vulnerability, XSS, SQL injection, CSRF, secrets, password, JWT, OAuth, permissions, audit, compliance, security, authentication, authorization, encryption, vulnerabilities, OWASP, compliance, audit" mentioned.
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。
Full SEO toolkit for Claude Code, powered by the DataForSEO API. Replaces the data layer of an SEO agency: keyword research, competitor analysis, rank tracking, backlink audits, technical site crawls, content gap analysis, and client-ready PDF reports — all with real Google search data, not scraping. Use when the user says "seo", "keyword research", "rank check", "backlinks", "competitor analysis", "site audit", "content gap", "compare domains", or passes a domain / URL / keyword for SEO analysis.
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。
Draft a personalized cold email to a prospect based on the SEO audit findings. Reads the audit JSON for the given domain, picks the 2-3 most impactful issues, and writes an outreach email that references specific data (current ranking, traffic value, named issues) so it doesn't look like spam.
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。
Assesses production readiness or audits a codebase against its specs. Supports quick static mode, deep 17-dimension audit, or single-dimension focus. Use before a production launch, after a major refactor, when inheriting a codebase, or when someone asks how done the project is.
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。
Audit whether a paper's EVALUATION DESIGN actually measures what it claims and whether its reporting is complete — the validity layer family D (experiment-forensics) cannot reach. Three patterns: train/test leakage means the reported score may not measure generalization (HP-EVAL-LEAKAGE — adopts the Kapoor & Narayanan 8-type / 3-category leakage taxonomy; the illegitimate-proxy / sampling-bias / pretraining-contamination subtypes hand off as needs_external_check, naming but NEVER running Oren-2023 exchangeability / Shi-2023 Min-K% / Golchin-2023 Time-Travel / BIG-bench canary); a load-bearing LLM judge is conflicted (same model/family as a compared system) or unvalidated (no human-agreement, no bias control) (HP-JUDGE-VALIDITY); a declared condition/metric is dropped or switched to favor the method, or 'best' is chosen with no held-out set (HP-SELECTIVE-REPORTING). Verdict-bearing at L0/L1 from the DESCRIBED protocol — NOT repo-gated like experiment-forensics; L2 only CONFIRMS against split/preprocessing/result files. A fresh cross-model reviewer (gpt-5.6-sol xhigh, read-only, fresh thread per pass) PROPOSES findings, each span-anchored to a ledger claim_id; tools/adjudicate_findings.py DECIDES the verdict. Leakage and under-reporting are usually HONEST methodological errors — every finding describes a discrepancy to CHECK, never an accusation. An LLM generating GROUND-TRUTH labels is HP-FAKE-GT (experiment-forensics) — routed there, not here. Emits eval-design-forensics.findings.json; computes NO verdict. Detect-only. Triggers: "eval design audit", "evaluation validity", "train/test leakage", "data leakage", "is the score measuring generalization", "LLM judge bias", "is the judge validated", "selective reporting", "cherry-picked results", "评估设计审计", "评测有效性", "数据泄漏", "训练测试集泄漏", "裁判模型有没有验证", "选择性报告".
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。
Audit experiment integrity against the evidence ledger. At L2 (repo + result files present) a fresh cross-model reviewer reads the eval code line-by-line for fake/derived ground truth, score self-normalization, phantom results (a paper number with no backing file/key), dead/uncalled metric code, verified-scope inflation, method-described ≠ method-evaluated drift, synthesized-looking results, placeholder/fake data still wired into a released result, code-output ≠ reported-number mismatch, and missing reproducibility artifacts (an empirical/agent/LLM paper shipping neither code nor the prompts/configs its results need) — every finding span-anchored to a ledger claim_id. At L0/L1 (PDF / source only) the same patterns are surfaced as info-level 'could-not-verify' signals where the ledger gives an anchor (observability_level_required:2) — NEVER a fraud verdict from a PDF. The reviewer PROPOSES findings; tools/adjudicate_findings.py computes the verdict. Detect-only. Triggers: "experiment forensics", "audit the results", "check the eval code", "实验诚实度".
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 Context Builder Skill (Ultra-Granular Pure Context Mode) workflow skill. Use this skill when the user needs Enables ultra-granular, line-by-line code analysis to build deep architectural context before vulnerability or bug finding and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.
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。
Analytics Tracking & Measurement Strategy workflow skill. Use this skill when the user needs Design, audit, and improve analytics tracking systems that produce reliable, decision-ready data and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.
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。
Analytics Tracking & Measurement Strategy workflow skill. Use this skill when the user needs Design, audit, and improve analytics tracking systems that produce reliable, decision-ready data and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.
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。
Analytics Tracking & Measurement Strategy workflow skill. Use this skill when the user needs Design, audit, and improve analytics tracking systems that produce reliable, decision-ready data and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.
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。
Analytics Tracking & Measurement Strategy workflow skill. Use this skill when the user needs Design, audit, and improve analytics tracking systems that produce reliable, decision-ready data and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.
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。
Audit dependencies for licensing, security, and maintenance risk. Use when a senior developer needs risk assessment.
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。
Assemble the governance record for a range — commits, overrides, ADRs, sprint auto-decisions, open questions, checkpoint findings — into one dated audit packet. Read-only.
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。
Comprehensive SEO analysis for any website or business type. Full site audits, single-page analysis, technical SEO (crawlability, indexability, Core Web Vitals with INP), schema markup, content quality (E-E-A-T), image optimization, sitemap analysis, and GEO for AI Overviews/ChatGPT/Perplexity. Industry detection for SaaS, e-commerce, local, publishers, agencies. Triggers on: SEO, audit, schema, Core Web Vitals, sitemap, E-E-A-T, AI Overviews, GEO, technical SEO, content quality, page speed, structured data.
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。
Maps intelligence for local SEO — geo-grid rank tracking, GBP profile auditing via API, review intelligence across Google/Tripadvisor/Trustpilot, cross-platform NAP verification (Google/Bing/Apple/OSM), competitor radius mapping, and LocalBusiness schema generation from API data. Three-tier capability: free (Overpass + Geoapify), DataForSEO (full intelligence), DataForSEO + Google (maximum coverage). Use when user says "maps", "geo-grid", "rank tracking", "GBP audit", "review velocity", "competitor radius", "maps analysis", "local rank tracking", "Share of Local Voice", or "SoLV".
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。
When the user wants to create SEO-driven pages at scale using templates and data. Also use when the user mentions "programmatic SEO," "template pages," "pages at scale," "directory pages," "location pages," "[keyword] + [city] pages," "comparison pages," "integration pages," "building many pages for SEO," "pSEO," "generate 100 pages," "data-driven pages," or "templated landing pages." Use this whenever someone wants to create many similar pages targeting different keywords or locations. For auditing existing SEO issues, see seo-audit. For content strategy planning, see content-strategy.
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。
Audit design token usage across a product for consistency and coverage.
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。
Optimize existing blog posts and web content for GEO (Generative Engine Optimization) — AI search visibility on ChatGPT, Perplexity, Claude, and Google AI Overviews. Use whenever a user provides a blog URL or pastes existing content and asks to optimize it for AI search, rewrite for LLM visibility, improve GEO score, make content answer-first, add FAQ sections, restructure headings for AI retrieval, improve citation potential, or audit content for AI readiness. Also triggers on "GEO optimize this", "make this AI-search ready", "rewrite for Perplexity", "optimize for ChatGPT", "optimize for Google AI Overviews", "improve AI visibility", "reformat for AI search", "make this content citation-worthy", "content isn't showing up in AI search", "why isn't my blog cited by AI", "GEO audit", "answer-first rewrite", "restructure for AI", "make my blog AI-friendly", "improve LLM citability". Do NOT trigger for writing new content from scratch — use geo-content 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。
Audit and optimize content for SEO, GEO (generative engine optimization), and AEO (answer engine optimization). Use when user asks to "audit SEO", "check search visibility", "optimize for AI search", "generate schema markup", "validate metadata", "extract keywords", "optimize for Perplexity/ChatGPT/Gemini", "check Open Graph tags", "submit to IndexNow", or mentions SEO, GEO, AEO, or search optimization. Do NOT use for creating new content from scratch or image optimization.
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。
Full-lifecycle blog engine with 30 sub-skills, 12 content templates, 5-category 100-point scoring, and 5 specialized agents. Routes user requests to the right sub-skill: writing, rewriting, analysis, outlines, audits, schema, charts, images, repurposing, AI-citation optimization, FLOW framework prompts, topic-cluster execution, and multilingual publishing. Optimized for Google rankings (December 2025 Core Update, E-E-A-T) and AI citations (GEO/AEO). Supports any platform (WordPress, Next.js MDX, Hugo, Ghost, Astro, Jekyll, 11ty, Gatsby, HTML). Use when user says "blog", "write a blog", "blog post", "blog strategy", "content brief", "editorial calendar", "blog audit", "blog optimization", "topic cluster", "multilingual blog", "FLOW framework", or any /blog subcommand. Sub-skill descriptions cover narrower triggers.
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。
Post-writing SEO validation with pass/fail checklist covering title tag length and keyword placement, meta description quality, heading hierarchy and keyword density, internal/external link audit with anchor text analysis, canonical URL verification, Open Graph meta tags (og:title, og:description, og:image), Twitter Card validation, URL structure optimization, and image alt text presence. Produces prioritized fix list with specific recommendations. Use when user says "seo check", "check seo", "validate seo", "blog seo", "seo validation", "on-page seo", "title tag check", "meta description check", "heading check", "link audit".
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。
Audit and score blog posts on a 5-category 100-point scoring system covering content quality, SEO optimization, E-E-A-T signals, technical elements, and AI citation readiness. Includes AI content detection (burstiness, phrase flagging, vocabulary diversity). Supports export formats (markdown, JSON, table) and batch analysis with sorting. Generates prioritized recommendations (Critical/High/Medium/Low) with specific fixes. Works with any format (MDX, markdown, HTML, URL). Use when user says "analyze blog", "audit blog", "blog score", "check blog quality", "blog review", "rate this blog", "blog health check".
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。
Conduct a comprehensive accessibility audit against WCAG guidelines with severity ratings and remediation steps.
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。
Audits any domain's AI-readiness by using curl to directly probe robots.txt, llms.txt, and llms-full.txt, then scores each file against a structured checklist and delivers a formatted report with pass/warn/fail findings and actionable fixes. Use this skill whenever a user provides a domain or URL and wants to know if llms.txt or llms-full.txt is available, discoverable, or properly structured. Trigger on phrases like "check llms.txt for", "does this site have llms.txt", "find llms.txt", "check llms for this url", "audit llms.txt", "is llms-full.txt available", or any time a user shares a domain/docs URL and wants AI-readiness checked. Also trigger when the user wants to verify GEO/AEO readiness of a documentation 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。