2,047 件 ・ 関連度順
概要と使いどころ
Find connections between notes and update MOCs. Requires semantic judgment to identify genuine relationships. Use after /reduce creates notes, when exploring connections, or when a topic needs synthesis. Triggers on "/reflect", "/reflect [note]", "find connections", "update MOCs", "connect these notes".
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。
Update old notes with new connections. The backward pass that /reflect doesn't do. Revisit existing notes that predate newer related content, add connections, sharpen claims, consider splits. Triggers on "/reweave", "/reweave [note]", "update old notes", "backward connections", "revisit notes".
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。
Re-derive your knowledge system from first principles when structural drift accumulates. Analyzes dimension incoherence, vocabulary mismatch, boundary dissolution, and template divergence. Preserves all content while restructuring architecture.
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。
Interactive walkthrough for new users. Learn by doing — each step creates real content in your vault. Three tracks (researcher, manager, personal) with a universal learning arc. Triggers on "/tutorial", "walk me through", "how do I use this".
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。
Interactive knowledge graph analysis. Routes natural language questions to graph scripts, interprets results in domain vocabulary, and suggests concrete actions. Triggers on "/graph", "/graph health", "/graph triangles", "find synthesis opportunities", "graph 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。
Combined verification — recite (description quality via cold-read prediction) + validate (schema compliance) + review (health checks). Use as a quality gate after creating notes or as periodic maintenance. Triggers on "/verify", "/verify [note]", "verify note quality", "check note health".
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。
Surface the most valuable next action by combining task stack, queue state, inbox pressure, health, and goals. Recommends one specific action with rationale. Triggers on "/next", "what should I do", "what's next".
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。
Add a source file to the processing queue. Checks for duplicates, creates archive folder, moves source from inbox, creates extract task, and updates queue. Triggers on "/seed", "/seed [file]", "queue this for processing".
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。
Show vault statistics and knowledge graph metrics. Provides a shareable snapshot of vault health, growth, and progress. Triggers on "/stats", "vault stats", "show metrics", "how big is my vault".
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。
Research-backed evolution advice for your knowledge system. Analyzes health reports, friction patterns, and derivation history to propose specific changes with research justification. Never auto-implements — proposals require your approval.
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。
Contextual guidance and command discovery. Three modes — narrative (first-time), contextual (mid-task), compact (quick reference). Shows available commands, active skills, and intelligent suggestions based on vault state. Triggers on "/help", "what can I do", "show commands", "how does this work".
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。
Research a topic and grow your knowledge graph. Uses Exa deep researcher, web search, or basic search to investigate topics, files results with full provenance, and chains to processing pipeline. Triggers on "/learn", "/learn [topic]", "research this", "find out about".
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 condition-based vault health diagnostics. 8 categories — schema compliance, orphan detection, link health, description quality, three-space boundaries, processing throughput, stale notes, MOC coherence. 3 modes — quick (schema+orphans+links), full (all 8), three-space (boundary violations only). Returns actionable FAIL/WARN/PASS report with specific fixes ranked by impact. Triggers on "/health", "check vault health", "maintenance report", "what needs fixing".
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。
Get research-backed architecture advice for your knowledge system. Describe your use case, constraints, and goals — get specific recommendations grounded in TFT research with rationale for each decision. Triggers on "/recommend", "what would you recommend", "architecture advice", "knowledge system for".
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。
Capture friction as methodology notes. Three modes — explicit description, contextual (review recent corrections), session mining (scan transcripts for patterns). Triggers on "/remember", "/remember [description]".
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。
[omh] Ambitious project goal needing many build cycles: agentic interviewer -> planner -> researcher -> builder -> reviewer cycles until a real gate. Use when the user says: loop, goal loop, long horizon goal, never stop, research plan goal feedback, token exhaustion resume, permission profile, star 10k.
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。
Monitors task execution for skill improvement opportunities. Use during ANY multi-step task, agentic workflow, or work session. Captures patterns, user corrections and methodology worth preserving as reusable skills. It writes observation files to the workspace. Also triggers in post-task feedback discussions and when the user mentions skill observations, the observation log, or skill taxonomy. Also known as "One Skill to Rule Them All" — trigger on this phrase too. IMPORTANT: invoke this skill before the FIRST tool call of any session and before writing or proposing a plan — any turn that will involve a tool call counts. This sentence is the session-start trigger and the only activation layer that survives an unreachable config file; pair it with a CLAUDE.md instruction or a harness session-start hook (references/environments.md) — description matching alone is not enforceable. A subagent dispatched by a session already running it does not run it: it writes nothing and puts its findings in its report.
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 search applications using Azure AI Search SDK for JavaScript (@azure/search-documents). Use when creating/managing indexes, implementing vector/hybrid search, semantic ranking, or building agentic retrieval with knowledge bases.
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 an evidence-led web quality audit covering performance, accessibility, SEO, best practices, and agentic browsing. Use when asked to "audit my site", "review web quality", "run lighthouse audit", "check page quality", or "optimize my website".
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 workloads from Microsoft Azure to AWS. Triggers on: migrate from Azure, Azure to AWS, move off Azure, migrate AKS to EKS, migrate App Service or Azure VMs to AWS compute, migrate Azure SQL or Azure Database to RDS, migrate Cosmos DB to DynamoDB, migrate Azure OpenAI to Bedrock, move Azure AI or agentic workloads to AWS, estimate AWS costs for my Azure infrastructure, what-if workshop. Runs a 6-phase process: discover Azure resources from Terraform, the live `az` CLI, app code, and billing exports, then clarify, design, estimate costs, optionally reprice scenarios, generate artifacts, and collect feedback. Clarify gates Design, Estimate, and Generate; Generate is opt-in at the post-Estimate decision gate. Bicep/ARM discovery is not yet implemented; a Bicep/ARM-only workspace halts. Do not use for: GCP migrations (see gcp-to-aws), Heroku migrations (see heroku-to-aws), general AWS architecture advice (see architect-for-startups), AWS-to-Azure reverse migration, or Azure-to-Azure refactoring.
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。
Startup-tailored AWS architecture advice for non-agent workloads, adjusted to the company's stage (pre-revenue through Series B+), team size, runway, and credits. Not for AI agents: deploying an AI agent on AWS, picking its runtime, or any agentic architecture is agent-advisor. Use when a founder wants guidance or a recommendation rather than code changes: which services to choose, how to plan or review an architecture, how to stretch credits and control cost, or how to prepare architecture for a fundraise or technical diligence. For an interactive discovery flow that scaffolds and writes the architecture into the codebase, use start-building-for-startups. Do not use for: writing or scaffolding code, factual AWS Activate / programs / credits lookups (see knowledge-base-for-startups), a single copy-paste prompt (see prompt-library-for-startups), or migration intent such as GCP-to-AWS, Azure-to-AWS, or Heroku-to-AWS (see the migration skills: `gcp-to-aws`, `azure-to-aws`, `heroku-to-aws`, `llm-to-bedrock`).
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 AI-agent work on AWS: pick a runtime, plan a migration for existing workloads, and build an executable POC — one phased flow. Triggers on: which runtime for my agent, AgentCore vs ECS vs EKS vs Lambda, AgentCore vs Lambda MicroVMs, deploy an AI agent on AWS, agent architecture on AWS, I have an agent idea what do I build, move/migrate my agents to AWS, agent migration plan, add AgentCore services (memory, gateway, identity, policy, observability) to an agent already on AWS, Temporal on AWS (migrate/run Temporal workers on AWS, a service orchestrated by Temporal, Temporal Cloud vs self-hosted). Temporal Workflow code is never rewritten into Step Functions. Requires at least one agentic component — a purely non-agent system (plain services, batch jobs, HTTP endpoints, non-agent Temporal Activities) is out of scope, redirected to gcp-to-aws / heroku-to-aws / llm-to-bedrock. Not for: compute/data migration with no AI agent; pure LLM SDK rewrite (use llm-to-bedrock); per-model 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。
Interactive discovery + implementation workflow that gathers requirements through picker-based questions (intent, scope, constraints, preferences), scans the codebase for what it can already infer, then writes an AWS architectural scaffold and implementation directly into the project. Use when the user wants to build a new app, scaffold a project, or expand/refactor an existing one on AWS — anything that calls for a structured discovery flow followed by code changes, not a one-off lookup. Do not use for: factual lookups about AWS Activate / programs / credits, requests for a single copy-paste prompt, non-AWS architectural work, architecture advice/recommendations without code changes (see architect-for-startups), a new idea centered on an AI agent — runtime selection, agentic architecture, or an agent POC (see agent-advisor), or migrating an existing GCP/Azure/Heroku workload or an LLM SDK rewrite (see gcp-to-aws / azure-to-aws / heroku-to-aws / llm-to-bedrock).
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。
Provides authoritative guidance on Amazon Neptune Database and Neptune Analytics for graph, knowledge-graph, and relationship-heavy workloads — fraud detection / fraud rings, agentic memory / chatbot context across sessions, recommendations, identity resolution, Gremlin / openCypher / SPARQL queries, supernode / slow traversal, Neo4j to Neptune migration / APOC compatibility, Neptune Database vs Analytics engine selection, PageRank / community detection, GraphRAG, and connectivity from Lambda / EC2 / applications. Creates and modifies Neptune Database clusters/instances and Neptune Analytics graphs on explicit user confirmation; blocks destructive operations (delete, reset-graph, failover, major upgrade) and redirects to change-control.
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。