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概要と使いどころ
React composition patterns that scale. Use when refactoring components with boolean prop proliferation, building flexible component libraries, or designing reusable APIs. Triggers on tasks involving compound components, render props, context providers, or component architecture. Includes React 19 API changes.
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 the user wants to connect cognee to external services — switching LLM or embedding providers (OpenAI, Azure, Gemini, Anthropic, Ollama, OpenRouter), changing databases (Postgres, PGVector, Neo4j, Neptune, Turso), S3 storage, or the MCP server for IDE 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。
Install or refresh OneCLI as NanoClaw's gateway provider. Use when setup selects OneCLI, an existing OneCLI-backed install is migrated to the gateway seam, or its runtime, approval bridge, setup, and agent guidance must be restored from the in-tree package.
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。
Migrate legacy NanoClaw and Claude-native memory into the shared memory tree and provider-neutral standing instructions. Run after an update reports the shared-memory breaking change, or when a group still has .seed.md, legacy CLAUDE.md/CLAUDE.local.md, Claude auto-memory, or an unindexed imported-agent-memory.md. Triggers on "migrate memory", "legacy memory", "the agent forgot everything after the switch".
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The meta skill. Turn any raw feature into a properly-skilled, tested, resolvable unit of agent capability. Idempotent: running on an existing skill improves it (bug fix, new input, quality pass) instead of starting from scratch. Every skill declares an EVAL CONTRACT (its goal + skill-specific dimensions + hard-fails) so the cross-modal eval judges THIS skill's real purpose, not generic slop. Cross-modal eval runs BEFORE tests: 3 frontier models from different providers critique the output against the contract, you iterate to quality, THEN write/update tests that lock in the proven-good behavior. NO-REGRESSION LAW: any edit to a skill must score >= the previous iteration's eval — forward only, never back. For skills that back a scheduled job, an edit MUST re-run a representative task and eval it before shipping.
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 an eval for an existing skill from its REAL usage history, not its spec. Mine invocations from the brain's conversation archive (conversations/) and per-harness session transcripts — a user correction after an invocation is the gold signal — then synthesize an eval_contract plus 4-8 replayable cases with honesty labels (SPEC-DERIVED vs HISTORY-IMPLIED) and stage the result at skills/<name>/eval/autobench-<date>.md as PENDING-HUMAN-APPROVAL. Never rewrites SKILL.md. Ships two guard companions: panel integrity (multi-model judging must prove each provider actually responded) and the fail-improve taxonomy (logged LLM-fallback cases convert to deterministic code over time).
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。
Entry point for 9Router — local/remote AI gateway with OpenAI-compatible REST for chat, image, TTS, embeddings, web search, web fetch. Use when the user mentions 9Router, NINEROUTER_URL, or wants AI without writing provider boilerplate. This skill covers setup + indexes capability skills; fetch the relevant capability SKILL.md from the URLs below when 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。
Select and run Python SDK verification with nox, Makefile targets, Ruff, mypy, pytest markers, sanity tests, type inference checks, and build checks. Use when adding Python tests, diagnosing Python CI, or validating Python SDK/provider changes. Do not use for TypeScript-only checks.
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 the user needs help configuring Crush — writing crushrc (the Bash config format) or crush.json, setting up providers, models, LSPs, MCP servers, hooks, skills, permissions, or changing Crush 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。
EU AI Act (Regulation (EU) 2024/1689) operational compliance for compliance teams. Three Article-level decisions: (1) What's the risk tier of this AI system — prohibited (Art. 5), high-risk (Art. 6 + Annex III), limited-risk (Art. 50), or minimal-risk? (2) For high-risk systems, what's the Article 43 conformity assessment route (Module A internal control vs Module H full QMS + notified body) and what goes in the Annex IV technical documentation? (3) Per organizational role (provider / deployer / importer / distributor / authorized representative), what are the active obligations and deadlines? Use during AI system intake review, when planning conformity assessment, or when scoping deployer obligations. Cites Articles + Annexes for every output. NOT executive AI strategy (see chief-ai-officer-advisor). NOT a legal substitute.
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 adding, retiring, or auditing feature flags. Triggers on "add a flag", "ship behind a flag", "rollout plan", "kill switch", "stale flags", "flag debt", "LaunchDarkly", "GrowthBook", "Statsig", "Unleash", "Flipt", or any progressive-delivery question. Ships flag debt scanner, rollout planner, and kill-switch auditor (all stdlib Python), 4 references on flag taxonomy + provider trade-offs + rollout strategies + lifecycle, plus a /flag-cleanup slash command.
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。
Production-grade Stripe integrations: subscriptions with trials and proration, one-time payments, usage-based billing, checkout sessions, idempotent webhook handlers, customer portal, and invoicing. Covers Next.js, Express, and Django patterns. Use when integrating Stripe for the first time, debugging webhook reliability issues, migrating from a different payment provider, or adding usage-based billing to an existing subscription product.
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 a user asks to debug or fix failing GitHub PR checks that run in GitHub Actions; use `gh` to inspect checks and logs, summarize failure context, draft a fix plan, and implement only after explicit approval. Treat external providers (for example Buildkite) as out of scope and report only the details URL.
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。
Guide the user through an independent iFixAi audit of their own agent, checking whether it does the job it is supposed to do given their business rules and org structure. Prefer pointing it at the user's REAL deployed agent over its HTTP endpoint (its actual tools, retrieval, and governance) with `--provider http --endpoint <url>`; only when no endpoint is reachable, fall back to replicating the model beneath as a bare stand-in (Anthropic, OpenAI, Gemini, Azure, Bedrock, etc.). Graded by the judge(s) of their choice (the same model, one independent judge, or a cross-vendor panel). You are the operator who walks them through it and explains the scorecard, running the SAME `ifixai run` engine as the guided CLI. Use when the user asks to run iFixAi or to audit an agent.
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。
Reproduce a known chat state in the running desktop on demand (model provider errors and retries, Code Mode and Den script failures, parallel tool steps, app creation, sequential commands) and check how it renders. Use when fixing or designing chat error, retry, tool-step or app-card UI.
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。
CodexBar read. Provider usage, limits, credits, config health. JSON. No writes.
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。
CodexBar live QA/e2e testing: run provider usage matrix checks, validate real app config, use Peekaboo for menu proof, use Browser Use/official docs for API spec or logged-in dashboard checks, and handle 1Password credentials safely.
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。
Google platform decision and setup guidance, loaded on demand from Google's skill catalog. Use when a developer is choosing or setting up part of their stack, such as where to run a service, a database, storage, messaging, authentication, analytics, ads, or AI model serving, and a Google product is a reasonable candidate - whether or not a vendor is named - or when a request names a Google product or API. Brings in the matching Google skill so the answer can weigh Google options, their trade-offs, and when they are not the right fit. Skip when the stack is already settled on another provider and no Google product is named, or the task involves no platform choice.
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。
Develop AI-powered applications using Genkit in Go. Use when the user asks to build AI features, agents, flows, or tools in Go using Genkit, or when working with Genkit Go code involving generation, prompts, streaming, tool calling, or model providers.
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, configures, and hardens workload-level security controls for Google Kubernetes Engine (GKE) applications and namespaces. Covers running security audits (`audit_cluster.sh`), enforcing Network Policies (default-deny and Dataplane V2 logging), isolating high-risk pods inside GKE Sandbox (`gVisor`), enforcing Pod Security Standards (`restricted` labeling) and pod securityContext, and mounting Secret Manager secrets via CSI (`SecretProviderClass`). Use when auditing workload security posture, isolating namespaces, applying pod security standards, or configuring network policies and secret volume mounts. Don't use for Workload Identity (use gke-workload-identity), cluster-wide control plane security, RBAC hardening, Binary Authorization, Shielded Nodes, or enabling platform-level GKE add-ons (use gke-platform-security instead).
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。
Plans, configures, and hardens platform-level Google Kubernetes Engine (GKE) cluster security. Covers cluster add-ons (Secret Manager enablement), RBAC hardening (disabling insecure bindings, audit tools), Binary Authorization, Secrets Encryption (--database-encryption-key), Security Posture (--security-posture), enabling Shielded Nodes, GKE Sandbox cluster enablement, GKE IAM roles, and cross-service authentication IAM patterns. Use when securing cluster control planes, hardening GKE RBAC, enabling Shielded Nodes, enabling GKE Sandbox runtime, enabling cluster-wide security add-ons, or managing GKE IAM roles. Don't use for Workload Identity (use gke-workload-identity) or workload-level security (SecretProviderClass, PSS, NetPol, gVisor pod runtimeClassName; use gke-workload-security).
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。
Answer questions about the Paseo product and app, including setup, configuration, connectivity, providers, workspaces, updates, logs, and troubleshooting. Use when a user inside Paseo asks how Paseo works, how to configure it, or why something is broken; use the paseo skill instead to operate agents and workspaces through MCP or the CLI.
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。
Instrument an AI application with DeepEval's native tracing so its behavior is visible in Confident AI. TRIGGER when the user wants to add DeepEval tracing or @observe to an LLM app, agent, RAG pipeline, or chatbot; wire a framework, model-provider, or vector-database integration (LangGraph, LangChain, OpenAI Agents, LlamaIndex, Pydantic AI, CrewAI, and others); choose between a native integration and manual instrumentation; set span types, tags, or metadata; or send DeepEval-SDK traces to Confident AI's Observatory. DO NOT TRIGGER for building DeepEval pytest eval suites, datasets, goldens, metrics, or deepeval test run (use the `deepeval` skill), or for raw OpenTelemetry / OTLP export without the deepeval package (use the `deepeval-otel` skill). This skill is purely DeepEval-SDK instrumentation — producing well-formed traces, not running evals.
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。
SolidJS renderer for json-render. Use when building @json-render/solid catalogs/registries, wiring Renderer providers, implementing bindings/actions, or troubleshooting Solid-specific reactivity patterns.
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。