'knowledge' 검색 결과

VRChat エコシステムツール（ModularAvatar / liltoon / VRCFury / AvatarOptimizer 等）の ナレッジを 4 フェーズで段階的に調査・検証し `knowledge/*.md` に再現可能な形で蓄積する。 ソースコード読解 → inspect 実測まで進めると confidence を `high` に昇格できる。 トリガー: 新しい VRChat コミュニティツールに初めて遭遇した、 既存ナレッジの confidence が low で作業に支障がある、 ユーザーから「このツールについて調べて」と依頼された場合。

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gnipbao/knowledge-cat-ppt-skillの方式で日本語スライドを制作・再現する。原典固有の生成器・テンプレート・実装と個別条件を保ち、実制作例を参考に日本語の資料を作るときに使う。

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Guides implementation of agent memory systems, compares production frameworks (Mem0, Zep/Graphiti, Letta, LangMem, Cognee), and designs persistence architectures for cross-session knowledge retention. Use when the user asks to "implement agent memory", "persist state across sessions", "build knowledge graph for agents", "track entities over time", "add long-term memory", "choose a memory framework", or mentions temporal knowledge graphs, vector stores, entity memory, adaptive memory, dynamic memory or memory benchmarks (LoCoMo, LongMemEval).

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生成スキルに knowledge/ を追加するとき読む。ナレッジ蓄積・検索・フィードバックループの設計を参照するとき読む。

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Analyze folders of Markdown/text research notes, Obsidian vaults, paper cards, invention notes, or memory exports as a practical knowledge map. Use when asked to find center topics, noisy or low-signal notes, possible contamination, overgrown topic groups, orphan notes, cleanup actions, or a useful public knowledge-hygiene workflow.

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Turns an engineer's notes, a ticket history or a chat thread into a draft step-by-step runbook with preconditions, stop conditions, numbered steps with checks, verification, rollback and escalation, for the team to validate before use. Commands verbatim, placeholders marked, every gap UNKNOWN; never executes a step or fills a gap from general knowledge. Use when the user asks to "write a runbook from these notes", "turn this resolved ticket into a runbook", "document the steps we ran" or "check this runbook for gaps". Do not use for a business process without commands, use sop-drafter instead; for a knowledge article or known-error record, use knowledge-article-drafter; for the post-incident review, use incident-postmortem-drafter. Drafts for human review; never approves, authorises or signs off.

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Knowledge base from the DoD Digital Engineering Strategy (2018, OUSD/ODASD(SE)). Use for the Department of Defense's five digital engineering goals — (1) formalize the development, integration, and use of models; (2) provide an enduring authoritative source of truth; (3) incorporate technological innovation; (4) establish supporting infrastructure and environments; (5) transform the culture and workforce — plus their focus areas, the document-to-model and design-build-test→model-analyze-build shifts, model formalisms/provenance, governance and access control of the authoritative source of truth, and the coordinate→plan→pilot→sustain rollout. A vision-and-policy strategy, deliberately non-prescriptive. Does NOT provide a how-to method, tool tutorials, MBSE/SysML mechanics, or implementation-plan content; thin on metrics, acquisition-policy detail, and step-by-step procedure. Excludes the CAC-gated Digital Engineering Body of Knowledge (DEBoK).

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Knowledge base from the DoD M&Q BoK (the Manufacturing and Quality Engineering Body of Knowledge), v3.0 of July 2025, published by OUSD(R&E). Use for the manufacturing-and-quality view of the defense acquisition life cycle: what M&Q engineers do across the Adaptive Acquisition Framework phases (Pre-MDD, Materiel Solution Analysis, Technology Maturation and Risk Reduction, Engineering and Manufacturing Development, Production and Deployment, Operations and Support); the twelve M&Q threads (A–L) built on the '5 Ms'; manufacturing feasibility and producibility; Manufacturing Readiness Levels and assessments; Key/Critical Characteristics and process capability (Cp/Cpk, SPC); the standards stack (AS6500, AS9100/ISO 9001, AS9103, AS9145); technical reviews (ASR/PDR/CDR/PRR) and milestone gates; DCMA surveillance; industrial base and supply chain; and sustainment (LCSP, IPS, ILA, ISR). Scope is DoD acquisition M&Q practice on the Major Capability Acquisition path — it is a best-practice compilation, not policy, and is thin on detailed shop-floor process engineering, contract law, and the full text of the cited industry standards (which it names but does not reproduce).

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Knowledge base from the GAO Technology Readiness Assessment Guide (GAO-20-48G, 2020). Use for evaluating technology maturity in acquisition programs and projects: Technology Readiness Levels (TRL 1–9) and related scales (MRL, IRL), identifying Critical Technology Elements (CTEs), the five-step process for a high-quality TRA, the four characteristics of a reliable TRA (comprehensive, well-documented, credible, defensible), Technology Maturation Plans (TMPs), and using TRA results in knowledge-based, milestone decisions. Assessment/maturity oriented: it measures readiness and frames risk — it is not a full SE process model, cost-estimating, or schedule guide (see sibling packs for those).

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Use when the user wants to update, refresh, or reinstall the CopilotKit agent SKILLS (the SKILL.md files that teach this agent about CopilotKit). NOT for updating the CopilotKit codebase or project — this is specifically about refreshing the skills/knowledge this agent has loaded. Triggers on "update copilotkit skills", "update skills", "refresh skills", "skills are stale", "skills are outdated", "get latest skills", "my copilotkit knowledge is wrong", "copilotkit APIs changed", "skills seem old", "wrong API names", "reinstall skills", "skills not working right", "update your copilotkit knowledge".

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Extract structured domain knowledge from AI models in-session or from local open-source models via Ollama. No API key needed.

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Compress large language models using knowledge distillation from teacher to student models. Use when deploying smaller models with retained performance, transferring GPT-4 capabilities to open-source models, or reducing inference costs. Covers temperature scaling, soft targets, reverse KLD, logit distillation, and MiniLLM training strategies.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Build AI knowledge base creation systems. TRIGGERS - Use when user needs help with ai-knowledge-base-builder related tasks.

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Design AI knowledge management systems. TRIGGERS - Use when user needs help with ai-knowledge-management related tasks.

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Draft and quality-check pages for the IPA Knowledge Hub in the site's house style. Use when creating a new .qmd Hub page from source material, adapting outside content into a Hub page, or reviewing an existing page for structure, tone, frontmatter, references, and Diátaxis fit. Replicates the quality-control workflow of IPA's Knowledge Hub Generator assistant.

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週ごとに Notion「ナレッジカード」DB に集まったカードを読み、問いをまたいで親和図にまとめ、正（worked）と反（failed）の対立から合（提案）を出し、次の問いを決めて、「合成テーマ」DB と週の行の配下の「まとめ」ページに書き込む。「今週のカードを合成して」「W43 のまとめを作って」「ナレッジカードの合成」などの依頼で使う。カードの提出は knowledge-cards、個人の採点は evaluate-session の領域。個人別の件数ランキングや「誰が何を失敗した」は出さない。

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Extract structured domain knowledge from AI models in-session or from local open-source models via Ollama. No API key needed.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Build, update, and query a persistent project knowledge graph from skills, memory, docs, and code structure — stdlib Python only, no external tools. Dual-mode: skill-library (agent-loom) or application (any consumer repo). Load when the user asks for a knowledge graph, project map, skill relationships, query the graph, update the graph, or trace how components connect. Auto-runs on memory-handoff and project-setup bootstrap. Also triggers on "build the graph", "what connects to X", "map this project".

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Extract structured domain knowledge from AI models in-session or from local open-source models via Ollama. No API key needed.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Resolve and enrich Knowledge people, companies, projects, and concepts during an authorized ingestion or entity update, with sourced State, timelines, and back-links. Read-only identity questions do not authorize writes.

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Find and answer from saved Knowledge vault pages with source citations. Use for questions about stored documents, meetings, people, or projects; not for recalling conversation history or searching the web.

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Incrementally ingest enumerable external sources into Knowledge with canonical source identity, retained snapshot manifests, overlap-aware diffing, and reconciliation of changed or deleted items. Includes a generic manifest tool and a read-only Discrawl adapter.

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Create scheduled Knowledge ingestion automations with a verified worker, private-vault authority, and an end-to-end scheduled run. Use when asked to schedule or repair recurring ingestion from any source.

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Ingest meeting transcripts from any recorder into Knowledge with attendee enrichment, timeline merge, supported decisions and commitments, and verification of substance and sequence. Summary-only input is not a verified meeting ingestion.

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