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概要と使いどころ
Ingest a CVE writeup, blog post, advisory, or GitHub repo into the wiki - fetch, dedup via sources:, update the right technique/tool page(s), re-index. Generic knowledge only; never client 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。
Drive msfconsole across the workflow - DB-backed recon (db_nmap, auxiliary scanners), version->exploit search/check/run, multi/handler reverse shells (meterpreter-first, plain shell_reverse_tcp backup for Windows/EDR), sessions + local_exploit_suggester + post modules, and autoroute/portfwd/socks pivoting. Points to the metasploit cheatsheet for syntax. Use for "metasploit", "msfconsole", "msfvenom", "meterpreter", "multi/handler", or driving an exploit/reverse-shell through msf.
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。
Exposed-secrets hunting - .git/ dir + history mining, exposed .env/config files, hardcoded keys in JS bundles + source maps, S3/blob exposure, public-repo secret search, CI/CD leakage. Live-validation mandatory. Wiki-first, FIND schema output.
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。
Drive responsible disclosure of a proven finding to a CVE. Package the report, find the vendor contact, report privately, coordinate a timeline, request the CVE (vendor CNA / GitHub / MITRE), and publish an advisory. Closes the research loop. Triggers - "disclose", "request a cve", "report this to the vendor".
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 oip-visual-v2 for traceable prompt-image pairs and open local result galleries. Use when finding, inspecting, comparing, browsing, or copying real image-generation references or exact source prompts. After references are selected, adapt them into a derived bilingual prompt when requested; use img-gen-taste first when the visual direction is still unclear.
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 polishing, diagnosing, tailoring, or exporting resumes for LLM, RAG, Agent, Agentic RL, post-training, pretraining, AIGC, search/ranking, multimodal, AI backend, or LLM algorithm internships from raw resume text, a materials folder, and/or a target job description. Audits evidence, maps JD fit, enforces truth boundaries, writes polished and targeted resumes, generates interviewer-style grilling questions, answer cards, evidence-upgrade plans, and optional open-source project recommendations without fabricating experience.
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。
Score a website's AI answer-engine visibility 0–100 against the open AIV rubric, and, with the user's own API keys, check and track through the OpenAI, Perplexity, Gemini and Anthropic APIs whether AI engines actually cite it. Use when the user wants to know whether AI engines can find, parse, trust and cite their site, or whether they do. Triggers on: 'does ChatGPT cite us', 'AI citation rate', 'share of voice in AI answers', 'track AI visibility weekly', 'AIV', 'AI visibility', 'GEO audit', 'generative engine optimization', 'AEO', 'llms.txt', 'will AI cite my site', 'AI search ranking', 'get cited by ChatGPT'.
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。
Before showing the user any substantive GTM deliverable (positioning, value prop, homepage, launch post, pricing, sales script, the brief, the market research, or the roadmap), stress-test it against the standard as an independent critic, because the agent that wrote it is the worst judge of whether it is good. Use as a gate right before presenting work, or when the user asks whether something is actually strong.
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。
After the founder brief and the market research, turn them into an honest diagnosis and a prioritized, stage-aware GTM roadmap saved as docs/gtm-cofounder/gtm-roadmap.md. This is the hub the user returns to every session to see where they are and the single next move. Use right after start-here, whenever the user doesn't know what to work on next, wants a plan instead of a one-off task, or is drowning in disconnected tactics.
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。
Design and build AI agents for any domain. Use when users: (1) ask to "create an agent", "build an assistant", or "design an AI system" (2) want to understand agent architecture, agentic patterns, or autonomous AI (3) need help with capabilities, subagents, planning, or skill mechanisms (4) ask about AI coding agents, agent runtimes, or similar internals (5) want to build agents for business, research, creative, or operational tasks Keywords: agent, assistant, autonomous, workflow, tool use, multi-step, orchestration
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。
Reconstruct and report long-running or multi-turn research, architecture questions, reviews, decisions, completion results, and status as a clear, self-contained brief. Use when a reader must re-enter earlier context, understand the real question and its relationships, distinguish verified facts from judgment and open evidence, or make a decision without reconstructing the work log. Quote the relevant original user wording before substantive analysis, keep one coherent main line, use spatial diagrams when relationships matter, and finish with an explicit decision boundary and practical next step.
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。
Recover and review local human-AI conversations from Claude Code, Codex, opencode, Grok Build, and Cursor. Use when the user wants to revisit, search, extract, summarize, or compare work across Agent session stores, time ranges, projects, or named conversations. Follow the user's requested output; question clustering, problem-space mapping, artifact review, drift analysis, and insights are optional lenses.
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。
Guides DPIA for health and medical data processing covering Art. 9(2)(h)-(j) exemptions, HIPAA crosswalk for transatlantic operations, clinical trial data protection under EU CTR 536/2014, and genetic data specifics under Art. 9(1). Activate for healthcare systems, clinical research, health apps, or medical device data. Keywords: health data, DPIA, Art. 9, clinical trial, genetic data, HIPAA, medical records, special category.
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。
Configure AWS MCP servers for documentation search and API access. Use when setting up AWS MCP, configuring AWS documentation tools, troubleshooting MCP connectivity, or when user mentions aws-mcp, awsdocs, uvx setup, or MCP server configuration.
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。
Analyze market conditions, geographic opportunities, pricing, consumer behavior, and product validation across Google Maps, Facebook, Instagram, Booking.com, and TripAdvisor.
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。
Complete App Store Optimization (ASO) toolkit for researching, optimizing, and tracking mobile app performance on Apple App Store and Google Play Store
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。
Automate Asana tasks via Rube MCP (Composio): tasks, projects, sections, teams, workspaces. Always search tools first for current schemas.
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。
Astropy is the core Python package for astronomy, providing essential functionality for astronomical research and data 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。
Compiled CLI covering all 52 endpoints of the Anytype local API — objects, properties, tags, search, chat, files — one binary, no MCP server needed.
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。
Automate Amplitude tasks via Rube MCP (Composio): events, user activity, cohorts, user identification. Always search tools first for current schemas.
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。
Create "[Competitor] alternative" and comparison pages for developer tools. Build honest, high-converting comparison content that ranks for competitive search terms.
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。
Automate Airtable tasks via Rube MCP (Composio): records, bases, tables, fields, views. Always search tools first for current schemas.
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。
Skill for discovering and researching autonomous AI agents, tools, and ecosystems using the AgentFolio directory.
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 content for AI search and LLM citations across AI Overviews, ChatGPT, Perplexity, Claude, Gemini, and similar systems. Use when improving AI visibility, answer engine optimization, or citation readiness.
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。