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概要と使いどころ
Queries a pinned Precision Medicine Knowledge Graph (PrimeKG) CSV for typed gene, drug, disease, and phenotype nodes, direct associations, disease context, and one- or two-hop paths. Use for PrimeKG reproducibility, biological association lookup, and hypothesis generation with relation and data provenance preserved.
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Queries documented public database APIs with explicit endpoints, filters, pagination, and provenance. Used when a scientific, regulatory, financial, or other database-backed fact must be retrieved reproducibly from a named source rather than inferred from general knowledge.
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Use when the user asks about GitNexus itself — available tools, how to query the knowledge graph, MCP resources, graph schema, or workflow reference. Examples: "What GitNexus tools are available?", "How do I use GitNexus?"
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Use when you need a deep, implementation-ready engineering plan for a code change — built from GitNexus graph intelligence, statement-level PDG analysis, and targeted source verification, compact enough that an implementation agent can start without re-investigating. Also strengthens existing plans via Deepen mode. Examples: "/gitnexus-plan Add retry support to the ingestion pipeline", "/gitnexus-plan deepen docs/plans/<plan>.md", "plan this change using the knowledge graph".
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Use when querying or extending GitNexus's PDG control/data-dependence surface (the `pdg_query` MCP tool, CDG/REACHING_DEF edges), or reasoning about "what controls X" / "where does Y flow" / guard clauses. Examples: "what guards this statement?", "trace this variable within the function", "why is the pdg_query result empty?", "add a CDG query".
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Capture surprises, workarounds, and rough edges hit while *consuming* the public surface of Prisma 8, Prisma Compute, or Prisma Postgres — anything a real user of these products would experience. Fires whenever an operator (or agent) writes a workaround, hits a surprising failure mode, or finds undocumented behaviour while using one of these three products from the outside (extension authoring, example apps, integration tests, customer reproductions, internal demos, manual repros). Does NOT fire on bugs in code the operator's own team maintains — those are normal product-backlog bugs. In product-team repos (e.g. `prisma-8-ws`), surfaces the gotcha and offers the operator three paths (capture-as-gotcha, file a normal bug, or note-and-move-on). Outside product-team repos (pet projects, hackathon entries, customer codebases), silent-captures into the project's gotchas log AND a Triage-state Linear ticket in the matching gotchas project — no operator escalation. Do not skip the recording step.
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Ask real executives and domain experts a question through Instant Expert for a written answer or short call: practitioner knowledge, customer discovery. Free test mode; live sends need approval.
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Companion to atlas-contract. Auto-invoked by its Final Audit on caught drift; also use after Post Reviews or user requests to record a mistake. Distills drift into WHEN/DON'T/INSTEAD clauses, writes to Atlas.md after confirmation.
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Capture and retrieve durable YYLO Ledger artifact Records with intentional profiles, payload modes, provenance, retention, and secret-safe immutable evidence.
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。
Semantic + keyword search and connection-discovery across the user's own Apple Notes via the apple-notes MCP server. Use when the user wants to find, recall, or synthesize something from their notes, or surface non-obvious bridges/related notes. macOS, on-device.
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Modern Angular (v20+) expert with deep knowledge of Signals, Standalone Components, Zoneless applications, SSR/Hydration, and reactive patterns.
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Announcement board: author, category, department, priority, audience, publish and expiry dates, status and acknowledgements, as CSV, SQL, JSON Schema or Notion on request. Use for internal notices.
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Compiled CLI covering all 52 endpoints of the Anytype local API — objects, properties, tags, search, chat, files — one binary, no MCP server needed.
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A hybrid memory system that provides persistent, searchable knowledge management for AI agents.
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 hybrid memory system that provides persistent, searchable knowledge management for AI agents (Architecture, Patterns, Decisions).
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。
Internal downstream skill for ctf-sandbox-orchestrator. CTF-sandbox workflow for cloud metadata services, instance identity, workload identity, link-local credential paths, role assumption, and metadata-to-privilege trust edges. Use when the user asks to inspect metadata-service access, instance credentials, pod or workload identity, link-local token paths, SSRF-to-metadata escalation, or explain how metadata-derived credentials turn into accepted cloud or control-plane privilege. Use only after `$ctf-sandbox-orchestrator` has already established sandbox assumptions and routed here.
日本語の概要は準備中です。原文の説明を表示しています。
Internal downstream skill for ctf-sandbox-orchestrator. CTF-sandbox workflow for Linux credential artifacts, service tokens, SSH material, cloud and container secrets, socket-level trust, and host-to-host pivot chains. Use when the user asks to trace Linux auth artifacts, accepted token or key replay, socket or service-account trust edges, sudo or capability abuse, or explain lateral movement across Linux challenge nodes. Use only after `$ctf-sandbox-orchestrator` has already established sandbox assumptions and routed here.
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Internal downstream skill for ctf-sandbox-orchestrator. CTF-sandbox workflow for SSRF reachability, internal route probing, metadata-service access, credential pivoting, and token-to-accepted-privilege chains. Use when the user asks to trace SSRF sources, internal hosts, metadata endpoints, link-local tokens, service-account credentials, or explain how a server-side fetch edge turns into accepted access. Use only after `$ctf-sandbox-orchestrator` has already established sandbox assumptions and routed here.
日本語の概要は準備中です。原文の説明を表示しています。
Internal downstream skill for ctf-sandbox-orchestrator. CTF-sandbox workflow for LSASS-resident secrets, Windows logon sessions, Kerberos ticket caches, DPAPI-backed material, SSP artifacts, and replayable credential extraction. Use when the user asks to inspect LSASS memory, recover tickets or logon sessions, trace DPAPI or SSP material, distinguish which credential artifacts are replayable, or connect host-resident credential material to an accepted pivot or privilege edge. Use only after `$ctf-sandbox-orchestrator` has already established sandbox assumptions and routed here.
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。
Internal downstream skill for ctf-sandbox-orchestrator. CTF-sandbox workflow for Kerberos, WinRM, SMB, RDP, Windows credential material, replayable tickets, delegation edges, and host-to-host pivot chains. Use when the user asks to replay Kerberos material, trace a WinRM, SMB, or RDP pivot, understand host-to-host privilege movement, or prove which Windows service accepted a credential or ticket. Use only after `$ctf-sandbox-orchestrator` has already established sandbox assumptions and routed here.
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, manage, and use DeepTutor through its CLI, including capabilities, knowledge bases, partners, memory, sessions, notebooks, providers, skills, and the server or Web app.
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Use when maintaining a durable Markdown knowledge store that agents compile from sources, when every number or quote in a wiki page must trace back to an immutable source, or when compiled pages need cheap drift detection against the code they describe. Teaches the raw/wiki/archive layout, per-claim provenance links, and git fingerprints for zero-token staleness checks.
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Master effective code review practices to provide constructive feedback, catch bugs early, and foster knowledge sharing while maintaining team morale. Use when reviewing pull requests, establishing review standards, or mentoring developers.
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 agent skills for Microsoft technologies using Learn MCP tools. Use when users want to create a skill that teaches agents about any Microsoft technology, library, framework, or service (Azure, .NET, M365, VS Code, Bicep, etc.). Investigates topics deeply, then generates a hybrid skill storing essential knowledge locally while enabling dynamic deeper investigation.
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。