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概要と使いどころ
Especialista em Otimização para Mecanismos de Busca para estratégia de conteúdo, SEO técnico, pesquisa de palavras-chave e melhorias de ranqueamento. Use ao otimizar conteúdo do site, melhorar ranqueamentos de busca, realizar análise de palavras-chave ou implementar melhores práticas de SEO. Especialista em SEO on-page, meta tags, schema markup e Core Web Vitals.
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。
Fundamentos de SEO, E-E-A-T, Core Web Vitals e princípios do algoritmo do Google.
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。
Iteratively improves a PR (GitHub), MR (GitLab), or shelved changelist (Perforce) until it reaches the repository's configured Greptile merge threshold with zero unresolved actionable comments, or until the five-review maximum is reached. Triggers Greptile review, fixes all actionable comments, pushes/re-shelves, re-triggers review only for new head SHAs unless a 5/5 score is already published, and repeats. Use when the user wants to optimize a PR/MR/CL against Greptile's code review standards without creating runaway review loops.
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。
[Debugging] Use when analyzing or optimizing performance bottlenecks: database queries, N+1 fan-out, indexing, API latency, memory/GC, concurrency and pool saturation, algorithmic complexity (O(n²)), network/protocol round trips, frontend rendering and Core Web Vitals, caching, and distributed/resilience paths. Calibration constants and domain laws (latency ladder, Little's Law, utilization knee, CWV thresholds, symptom→cause triage) live in references/performance-knowledge.md.
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。
Ranks companies or accounts by strategic relevance and opportunity. Use when the user provides accounts and wants a priority score, tier, rationale, risks, and next action.
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。
Qualifies and scores B2B prospects against a supplied ideal customer profile. Use when the user provides a lead list, company records, prospect data, or qualification criteria and wants prioritization, fit analysis, or disqualification reasons.
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 anomalies in procurement data using Qdrant vector operations. Covers centroid-based outlier detection, near-duplicate finding via batch recommend, amount z-score analysis, and vendor variance scoring. Use when building anomaly detection, fraud detection, or data quality features.
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 generating a single dense technical architecture, reference poster, or system diagram image for a software project. Produces a source-grounded bitmap diagram with topology, primitives, ingest/process flow, core logic, state scopes, tiers/modules, surfaces, runtime cards, and strict semantic color 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。
Search the web with LLM-optimized results via the Tavily CLI. Use this skill when the user wants to search the web, find articles, look up information, get recent news, discover sources, or says "search for", "find me", "look up", "what's the latest on", "find articles about", or needs current information from the internet. Returns relevant results with content snippets, relevance scores, and metadata — optimized for LLM consumption. Supports domain filtering, time ranges, and multiple search depths.
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 audio already placed in a HyperFrames composition needs to be mixed: fade-in/fade-out, crossfade, track gain or volume, volume automation, ducking, a music bed that fights a voiceover (voiceover carve), effects on a track (EQ, compressor, limiter, gate, saturation, delay, reverb, chorus, phaser, bitcrush), automation envelopes drawn on a track's volume or any effect parameter, or one submix bus carrying a chain, a fader and an automation clock for several tracks at once (`<hf-audio-group>`). Don't use for sourcing or generating audio — finding BGM, SFX, or making a voiceover is `/media-use`. Don't use for clip timing or track layout, which is `/hyperframes-core`.
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。
Run evidence-disciplined Xiaohongshu social listening for foreign pharma employee pain points. Use when the user asks to collect and analyze notes/comments about pharma companies, aliases, roles, compliance, medical affairs, or AI topics, then produce a cited and scored sampled-insight report. Includes independent-evidence counting, MediaCrawler batch collection, and CAPTCHA/461 fallback to Agent Reach/OpenCLI.
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。
The HyperFrames composition contract — build one renderable project. Use for composition structure, the `data-*` timing attributes, `class="clip"`, tracks, sub-compositions, variables, framework-owned media playback, deterministic-render rules, and validation. Also covers Tailwind projects and the STORYBOARD.md / SCRIPT.md plan formats. Read before writing composition HTML.
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 audio already placed in a HyperFrames composition needs to be mixed: fade-in/fade-out, crossfade, track gain or volume, volume automation, ducking, a music bed that fights a voiceover (voiceover carve), effects on a track (EQ, compressor, limiter, gate, saturation, delay, reverb, chorus, phaser, bitcrush), automation envelopes drawn on a track's volume or any effect parameter, or one submix bus carrying a chain, a fader and an automation clock for several tracks at once (the `hf-audio-group` element). Don't use for sourcing or generating audio — finding BGM, SFX, or making a voiceover is `/media-use`. Don't use for clip timing or track layout, which is `/hyperframes-core`.
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, build, evaluate, and improve systems using decision models. Use for bounded classification, routing, ranking, Choice/Score/Noul, decision-model composition, evaluation harnesses, prompt/program hill climbing, and Software 3.0 workflows across software, business, organizations, and life. Separate models, code, and human judgment. TypeSafe Jev is the default hosted exemplar. Not for straightforward arithmetic, prose rewriting, or provider setup alone.
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。
Brand mention and authority scanner for AI visibility. Analyzes brand presence across platforms that AI models rely on for entity recognition and citation decisions. Produces a Brand Authority Score (0-100) with platform-specific recommendations.
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。
Monthly delta tracking and progress reporting for GEO clients. Compares two GEO audits (baseline vs. current), calculates score improvements across all categories, tracks action item completion, and generates a "here's your progress" client report. Use when user says "compare", "delta", "monthly report", "progress", "confronta", "progressi", "report mensile", or when running a monthly client check-in.
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。
AI citability scoring and optimization. Analyzes web page content to determine how likely AI systems (ChatGPT, Claude, Perplexity, Gemini) are to cite or quote passages from the page. Provides a citability score (0-100) with specific rewrite suggestions.
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 professional PDF report from a GEO audit using pandoc plus a Chrome/Chromium-compatible headless browser. Converts GEO-AUDIT-REPORT.md into a styled, client-ready PDF with a cover page, color-coded score tables, severity-tagged findings, and a 90-day roadmap.
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 website GEO+SEO audit with parallel agents first and skills fallback. Orchestrates a comprehensive Generative Engine Optimization audit across AI citability, platform analysis, technical infrastructure, content quality, and schema markup. Produces a composite GEO Score (0-100) with prioritized action plan.
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 professional, client-facing GEO report combining all audit results into a single deliverable with scores, findings, and prioritized actions
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。
从复盘评论数据派生 / 刷新账号的受众画像，写入 audience.md。这是和 rubric 平行的第二个派生物——rubric 答"怎么打分"，persona 答"谁在看"。cheat-seed 选题 / 写稿时读它。**audience.md 含实绩信号，cheat-score-blind 硬禁读**。触发词："构造受众画像"/"更新 persona"/"我的观众是谁"/"build persona"/"刷新受众画像"/"看看我的受众画像"。
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。
为最终稿创建不可变盲预测日志，并在已有预测时追加新版本。通过 cheat-score-blind 做隔离评分，再由主窗口复核落盘。触发词包括启动预测、start prediction、打分并预测、写预测日志。
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 deep, honest peer-review of how someone uses Claude Code by statistically analyzing their local session data (~/.claude/usage-data/ and ~/.claude/projects/). Trigger whenever the user asks to review, analyze, audit, or critique their own Claude Code usage, workflow, habits, or skill level — including phrases like "analyze my cc usage", "review my cc sessions", "peer review my cc workflow", "deeper than /insights", or any ask for an honest audit of their AI workflow. Also covers portfolio/hiring-manager framings (e.g. "portfolio for Anthropic/OpenAI/xAI"), but ALWAYS ask the user in Step 0 which version to build (self / hr / both) before running — never silently produce an HR report, because that version goes to outsiders and needs explicit privacy setup. The HTML report is laid out ledger-first for SELF (opening band, output/team/leak/trend ledgers, then peer review, 9-dim scoring, try-this-week, case study, claim-indexed evidence). The HR variant is a recruiter version v1: identity card, earned badges (threshold-based, earned-only), allowlist-filtered output ledger, one case study, and a methodology & scope disclosure — no scores, no peer-review memo, no charts.
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 the local C++ coding conventions of the EMULATORS framework. Use when Codex adds, edits, reviews, or scaffolds C++ code in this repository, especially new classes, headers, cpp files, CORE/COMMODORE module code, emulator components, registers, chips, memory, sound, IO, commands, or InfoClass-derived objects.
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。