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概要と使いどころ
Search Cognee memory. Session memory is automatically searched on every prompt via hooks. Use this skill explicitly for permanent knowledge graph search, filtered category search, or when you need more results than the automatic lookup provides.
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 knowledge engine - a memory system in 6 lines of code. remember/recall/forget/improve loop, vector + graph search, OpenClaw plugin supported.
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 indexing a code repository into Cognee's code graph or querying it (callers, impact analysis, paths, endpoints) from Antigravity.
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。
Index a code repository into Cognee's code graph and query it deterministically (callers, impact analysis, paths, endpoints). Use for structural code questions, "index this repo", and checking what a re-index changed.
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。
Designs NUnit-based C# test suites for API, component, and integration coverage. Use when creating fixtures, wiring Testcontainers, or reducing flaky CI behavior.
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。
Implements OpenTelemetry logs/metrics/traces, SLI/SLO gates, burn-rate alerts, and APM integrations. Use when adding or validating observability.
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。
Designs coding-agent plugin systems. Use when adding plugin manifests, extension points, or reloadable integrations, or keeping plugin cache compatible across CLI upgrades.
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。
Executes an approved implementation plan through fresh implementer and reviewer agents for each task, with explicit handoffs, focused tests, and task-by-task integration. Use for multi-task work where independent agent context improves implementation and review quality. Not for a single small edit, an unresolved design, or a failure that still needs root-cause 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。
Closes a completed development branch by checking the final diff and proof, presenting merge, pull-request, keep, or discard options, and cleaning up only after the user or repository workflow chooses a path. Use when feature work is complete and the branch must be integrated or retired. Not for claiming a feature is complete before verification.
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 any time a spreadsheet file is the primary input or output. This means any task where the user wants to: open, read, edit, or fix an existing .xlsx, .xlsm, .xltx, .csv, or .tsv file (e.g., adding columns, computing formulas, formatting, charting, cleaning messy data); create a new spreadsheet from scratch or from other data sources; or convert between tabular file formats. Trigger especially when the user references a spreadsheet file by name or path — even casually (like "the xlsx in my downloads") — and wants something done to it or produced from it. Also trigger for cleaning or restructuring messy tabular data files (malformed rows, misplaced headers, junk data) into proper spreadsheets. The deliverable must be a spreadsheet file. Do NOT trigger when the primary deliverable is a Word document, HTML report, standalone Python script, database pipeline, or Google Sheets API integration, even if tabular data is involved.
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。
Stop and consult this skill whenever your response would include specific facts about Anthropic's products. Covers: Claude Code (how to install, Node.js requirements, platform/OS support, MCP server integration, configuration), Claude API (function calling/tool use, batch processing, SDK usage, rate limits, pricing, models, streaming), and Claude.ai (Pro vs Team vs Enterprise plans, feature limits). Trigger this even for coding tasks that use the Anthropic SDK, content creation mentioning Claude capabilities or pricing, or LLM provider comparisons. Any time you would otherwise rely on memory for Anthropic product details, verify here instead — your training data may be outdated or wrong.
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。
Implement and route HitKeep contributions through the repository-owned hk developer platform. Use for any HitKeep code, UI, API, database, analytics-feature, seed, screenshot, documentation, integration, or release-preparation change, as well as setup, local development, builds, smoke tests, and contributor workflow discovery.
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 QA test plan for a sprint or feature. Reads GDDs and story files, classifies stories by test type (Logic/Integration/Visual/UI), and produces a structured test plan covering automated tests required, manual test cases, smoke test scope, and playtest sign-off requirements. Run before sprint begins or when starting a major feature.
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。
Integrating messaging platforms and bots: channel adapters, webhook handlers, WebSocket servers, event-driven architecture, bot command frameworks. Use for Slack/Discord/Teams integration.
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。
Authoring Storybook stories, component catalogs, and Visual Regression integration (CSF 3.0/Factories, Storybook 10 ESM-only, React Cosmos). Use when building a component catalog.
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 cryptographic architecture: algorithm selection, key management, E2EE, KMS integration, signature verification, TLS. Use when designing crypto protocols or key rotation flows.
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 new skill agents via gap analysis, overlap detection, SKILL.md + reference generation, and Nexus integration. Not for task orchestration (Nexus) or format-only audits (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。
Implementing robust business logic, API integrations, data models, and reproducible AI image-generation code with type safety. Use for production implementation, Gemini image API pipelines, or interactive pair programming.
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 AI design contract (AI-SPEC.md) for phases that involve building AI systems — framework selection, implementation guidance from official docs, and evaluation 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。
Master advanced AgentDB features including QUIC synchronization, multi-database management, custom distance metrics, hybrid search, and distributed systems integration. Use when building distributed AI systems, multi-agent coordination, or advanced vector search applications.
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。
Verify mini-program implementations and fixes with risk-calibrated evidence across static checks, unit tests, integration tests, state matrices, simulators, real devices, cloud environments, and release artifacts. Use when users ask to test, validate, accept, regression-check, quality-check, confirm readiness, or determine whether a mini-program feature is actually complete. Binds results to a source and build fingerprint, records commands and observable evidence, separates passed, failed, blocked, and not-run layers, prioritizes the next highest-information check, and never converts local success into device, cloud, release, or formal acceptance claims.
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, preview, implement, and assess mini-program interfaces against an approved prototype, screenshot, visual reference, design system, or bounded UI request while preserving product semantics and existing accepted behavior. Use when users ask to reproduce a mini-program screen, refine visual hierarchy, adapt layouts across device sizes, handle safe areas or keyboards, inspect touch targets, scrolling, media, or gesture conflicts, compare an implementation with a reference, or prepare a device-verification matrix. Separates proposed visual previews, user approval, source integration, local checks, and real-device evidence; never invents product functionality for visual completeness or claims device validation without device-specific proof.
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。
Modify a lesson plan using SIOP sheltered instruction principles to support language learners across all four skills. Use when planning lessons for classes that include EAL students.
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 needs a comprehensive marketing plan for a client, a company they advise, or their own product. Also use when the user mentions "marketing plan," "growth plan," "GTM plan," "go-to-market plan," "AARRR plan," "90-day marketing plan," "12-month marketing roadmap," "fractional CMO plan," or "fCMO plan." Generates an exhaustive 13-section plan structured by AARRR (Acquisition, Activation, Retention, Referral, Revenue), customized to the client's current budget, team, and stage, mapped to future funding milestones, cross-referenced with the 139-idea marketing-ideas library and an embedded 17-section current-state audit rubric, with a full marketing operations stack showing which skills and MCP/API integrations execute each part. Outputs a Notion-paste-ready markdown document. For positioning and ICP context before planning, see product-marketing. For stage-specific deep work, see onboarding, signup, emails, referrals, 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。