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概要と使いどころ
A dated chronology of one topic across the user's recorded coding-agent sessions — the lessons the graph learned about it and the prompts that named it, oldest first. Use only when the user explicitly asks for the history or timeline of a topic across their sessions ("how did X evolve", "when did we first discuss X") or asks for the `cognee-timeline` skill by name. Not for questions about code or the current task.
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。
A standup or status update built from the coding-agent sessions Cognee recorded — prompts, tool calls, edited files, what was left open — per project. Use only when the user explicitly asks for a standup, a status update, a catch-up on their own recent sessions ("what did I work on yesterday / this week"), or invokes /cognee-standup. Not for questions about code, files or the current task.
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 this skill when the user's prompt requires (1) researching a topic across multiple sources, comparing options or alternatives, analyzing trends or history, understanding markets or industries, or reviewing literature or studies and (2) synthesizing that research into a comprehensive, narrative report. If you're planning to search the web or internal knowledge bases, consider using this skill. This skill coordinates research subagents, so use it only when you have a tool for spawning subagents (the Agent or Task tool); otherwise, research the question directly.
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 this skill when the user wants intellectual understanding — learning how or why something works, not getting a task done or soliciting Claude's judgment. Trigger for: - Explicit learning requests: teach, explain, ELI5, walk me through, quiz me, flashcards, "I'm rusty on"; definitions ("what is X") - Terse concept names implying "help me understand this": "Galois theory," "transformers, from scratch" - Confusion signals: "won't stick," "keep mixing these up," "not getting it" - Learning-path questions: prerequisites, sequencing, what to study before X - Conceptual questions about mechanisms, causes, or dynamics Don't trigger for: - Tasks: coding, writing, calculation, translation, factual lookup, news updates - Personal troubleshooting; resource/textbook recommendations - Claude's evaluative verdict: opinion prompts ("do you think X", "settle this", "honest take", "is X dead / still taken seriously") and interpretive takes ("was X really as harsh as people say")
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。
Test interactive CLI/TUI applications using @microsoft/tui-test. Use when you need to test CLI tools with interactive prompts, TUI rendering, arrow key navigation, or any command that requires a TTY. Triggers include "test CLI", "test TUI", "run interactively", "automate terminal input", "simulate user input in terminal".
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。
End-of-story completion review. Reads the story file, verifies each acceptance criterion against the implementation, checks for GDD/ADR deviations, prompts code review, updates story status to Complete, and surfaces the next ready story from the sprint.
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 per-asset visual specifications and AI generation prompts from GDDs, level docs, or character profiles. Produces structured spec files and updates the master asset manifest. Run after art bible and GDD/level design are approved, before production begins.
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。
Designing LLM-optimized folder structures: audits and restructures directories for context efficiency, progressive disclosure, and prompt cache performance. Not for general repo structure (Grove).
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。
Generating AI image-generation code using the Gemini API. Handles text-to-image generation, image editing, and prompt optimization. Use when image generation code is 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。
Designing and evaluating AI/ML systems: prompt engineering, RAG design, LLM application patterns, AI safety, evaluation frameworks, MLOps, cost optimization. Use for AI pipelines or eval harnesses.
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。
Designing and auditing repository structure for humans and LLM agents: layouts, monorepos, docs/tests/scripts, progressive disclosure, prompt-cache topology, and safe migrations.
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。
Converting a supplied prompt into an executable specification: detects vague quality/quantity/explanation/style/design/technical/judgment wording, role and persona theater, and self-contradiction, then replaces each with a numeric bound, an observable behavior, or a scorable criterion — with a per-term ledger of what changed and what stayed open. Don't use for AI system design, RAG, or eval harnesses (Oracle), PRD/SRS authoring (Scribe), spec conformance verification (Attest), or SKILL.md normalization (Gauge).
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。
Author or improve a Skill (an executable, reusable capability with a ``SKILL.md`` plus optional ``scripts/``, ``references/``, and assets). Trigger this skill whenever the user wants to: - Capture a workflow they keep repeating into a fresh Skill - Polish an existing Skill's prompt, description, or examples - Tighten when-to-trigger wording so the model invokes the Skill more reliably - Discuss the Skill format, frontmatter, or how progressive disclosure works Do not wait for the user to say "skill-creator" verbatim — phrases like "turn this into a skill", "let's make a reusable thing for this", "improve my skill", or "tweak the description for this skill" should all reach 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。
Build, test, inspect, install, and deploy MCP servers with FastMCP in Python. Use when creating a new MCP server, wrapping an API or database as MCP tools, exposing resources or prompts, or preparing a FastMCP server for Claude Code, Cursor, or HTTP deployment.
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。
Edit PDF text/typos/titles via nano-pdf CLI (NL prompts).
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。
Initiate natural language Q&A through Amazon's storefront Alexa shopping assistant to get shopping guidance answers, recommended product groups, ASIN lists, and follow-up questions. Each call supports only 1 prompt; for follow-ups, the agent must summarize context and concatenate a new question for a new request. A url can be used to supplement Amazon page context. Trigger when the user mentions Amazon Alexa, Alexa shopping assistant, Amazon smart assistant, AI shopping guide, conversational product selection, natural language shopping, Amazon chat Q&A, Amazon Alexa shopping, conversational shopping, AI shopping assistant, follow-up questions, product recommendation conversation, context follow-ups. Even if the user does not explicitly mention "Alexa", if their need is to "ask for product recommendations on Amazon using natural language", this skill should also be triggered.
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 elaborative interrogation prompts that deepen encoding through targeted why and how questions. Use when students memorise without understanding or need deeper processing of content.
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。
Execute a plan or concrete work prompt end-to-end. Use when implementing from docs/plans, a spec path, or a clear build request; use ce-debug for open-ended bugs.
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 or optimize in-app paywalls, upgrade screens, upsell modals, or feature gates. Also use when the user mentions "paywall," "upgrade screen," "upgrade modal," "upsell," "feature gate," "convert free to paid," "freemium conversion," "trial expiration screen," "limit reached screen," "plan upgrade prompt," "in-app pricing," "free users won't upgrade," "trial to paid conversion," or "how do I get users to pay." Use this for any in-product moment where you're asking users to upgrade. Distinct from public pricing pages (see cro) — this focuses on in-product upgrade moments where the user has already experienced value. For pricing decisions, see pricing.
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 metric-driven optimization loops. Use when improving measurable outcomes such as search relevance, clustering quality, build performance, prompt quality, or scored behavior through experiments.
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。
Smithy the Toolsmith builds, converts, reviews, packages, validates and migrates BMad skills, agents and modules. Use when the user wants a skill, agent, persona, command or module made, changed, reviewed or packaged; a prompt or rule from another tool turned into a skill; a skill mined from session logs; an old module migrated; or asks for Smithy. Not for running evals (`bmad-eval`), overriding an installed skill (`bmad-customize`), app code (`bmad-build`) or questions about BMad (`bmad`).
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。
Scans a codebase and generates a self-contained HTML service-design journey map — NN/g combined customer journey + service blueprint format. Use when the user wants to map user journeys, create journey maps, build service blueprints, or understand user flows from code. Prompts for codebase path if not supplied. Works with any tech stack.
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 Muted Zine Poster v0.1 poetic paper-poster prompts and the matching generated image. Use when the user gives a theme, sentence, object, mood, article idea, photo, or content brief and wants a quiet Japanese/Korean zine-like editorial poster with large negative space, aged paper texture, experimental typography, and a restrained muted palette without high-chroma color blocks. This is the low-saturation sibling of gc-minimal-zine-poster-v0-1.
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。
管家阿福(Afu)——把 Obsidian 收件箱变成活的 Wiki,再变成本周待办卡和周历:Inbox -> Wiki -> Todo Card -> Calendar 一条线。本地优先,Markdown 是唯一真源,收件箱原文不改写,导入/排期/作废/编译全部写操作日志可回滚。 触发词包括但不限于:整理我的 Obsidian 收件箱、每天提醒我处理 Obsidian 收件箱、inbox 清零、把这批资料编译成 Wiki、从笔记生成本周待办、待办卡排进周历、阿福帮我理一下 vault。 即使用户只是说"我的 Obsidian 堆了一堆没处理的东西",也应该触发。 不要用于:不涉及 Obsidian/Markdown vault 的普通待办管理(用一般 todo 工具);改写、润色、总结笔记原文(阿福只整理位置,不动原文);Notion 等非 Markdown 知识库。
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。