「knowledge」の検索結果

Knowledge base from the FAA Requirements Engineering Management Handbook (DOT/FAA/AR-08/32). Use for writing requirements for real-time, embedded control systems — the avionics/medical slant — via the Handbook's eleven recommended practices: system overview and boundary (monitored/controlled variables), operational concepts (use cases), environmental assumptions, change-tolerant functional architecture (dependency diagrams), safety-driven architecture revision (FHA/PSSA/fault tree), system modes, detailed behavior and performance requirements (ideal value + tolerance + latency), the four-variable model bridge to software requirements (MON/CON/NAT/REQ, IN'/REQ'/OUT', DO-178B mapping), subsystem allocation, and rationale. Carries the Isolette Thermostat and Flight Control System worked examples. Slanted toward control systems with sensors and actuators; thin on information-system / enterprise / agile requirements, on requirements-management tooling, and on the formal-methods internals of SCR/RSML/SpecTRM (it names them, does not re-teach them). Does not reproduce the IEEE/DO-178B/ARP standards it cites.

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Knowledge base from NASA-STD-7009B (Standard for Models and Simulations) with implementation depth from NASA-HDBK-7009B. Use for M&S criticality and programmatics, development evidence and capability assessment, verification/validation domains, uncertainty and sensitivity, use-phase results assessment, risk-informed decision reporting, and handbook life-cycle products. Covers STD-7009B (2024-03-05) + HDBK-7009B (2026-02-03) synthesized notes only; does not replace program-specific TA direction, NPR 7150.2 software requirements detail, or full PRA methodology.

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Knowledge base from IS-GPS-200 Rev N (NAVSTAR GPS Space Segment / Navigation User Segment Interfaces). Use as a worked ICD/IS exemplar: what an interface specification is, DIST-A and IRN/CCB change control, interface definition vs identification, composite-signal criteria patterns, NAV data as payload families, and time/definition hygiene. Covers Rev N synthesized notes only; does not transcribe Apps II–IV bit fields or PRN/Gold-code tables, and does not replace faa-std-025 preparation rules.

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Knowledge base from DOE Systems Engineering Methodology (SEM) Version 3 — the DOE SDLC for IT investments. Use for staged systems lifecycle planning through maintenance, Stage Exits, Structured Walkthroughs, In-Stage Assessments, requirements traceability, and CMM-aligned process discipline. Covers SEM3 (2002) guidance only; does not replace modern DevSecOps tooling standards, DOE O 413.3B capital-asset directives, or current CIO policy supersessions.

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Knowledge base from the DoD Manufacturing Readiness Level (MRL) Deskbook (Version 2022, OSD ManTech / Joint Service–Industry MRL Working Group). Use for assessing and managing manufacturing maturity and producibility risk in defense acquisition: the 10-level MRL scale and its non-linear, target-not-grade nature; the five demonstration environments (laboratory → production-relevant → production-representative → pilot line → production line); the nine manufacturing-risk threads and sub-threads; how MRL targets map onto Adaptive Acquisition Framework milestones (MRL 4/6/7/8/9); conducting an MRL Assessment; building a Manufacturing Maturation Plan; putting MRL maturity into contract language (Section L/M, SOW, AS6500A, DIDs); tailoring criteria for S&T, single-system, ship, sustainment, and industry contexts; and screening operational-technology cybersecurity. Manufacturing-readiness focused — it measures producibility risk and frames mitigation; it is NOT a technology-readiness (TRL) guide, a full systems-engineering process model, a cost-estimating or schedule guide, or a deep cybersecurity audit method (see sibling packs for those).

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Psychoeducation on depression, anxiety disorders, PTSD, bipolar disorder, schizophrenia, ADHD, and borderline personality disorder. Knowledge sharing without diagnosis.

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Drafts a blameless incident postmortem (summary, impact, timeline, detection and response, contributing factors as candidates for review, action items) from an incident channel export, ticket notes and any alert or change records, with UNKNOWN for every fact the inputs do not contain. Never assigns blame, names a root cause or re-rates severity. Use when the user asks to "write the postmortem for this incident", "build the incident timeline from the chat export", "draft the post-incident review" or "turn this ticket and channel log into an incident report". Do not use for a knowledge article from the resolved ticket, use knowledge-article-drafter instead; for project retrospectives, use lessons-learned-synthesis; for data-breach impact and notification questions, use data-incident-impact-brief. Drafts for human review; never approves, authorises or signs off.

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Sweeps a SharePoint library listing (an export or a listing the agent can reach, with file name, link, review-date column and owner column) for documents past their review date and returns a dated DRAFT review sweep report (Overdue, Due soon, No review date, Summary) plus one ready-to-paste reminder per document owner for the user to send. Never changes metadata, sends a message or invents a document, date or owner. Use when the user asks to "find the documents past their review date", "run the review sweep on the policies library", "which documents are overdue for review", "audit the review dates in this library export" or "draft reminders to owners about stale documents". Do not use for duplicate, conflict and ownership checks across knowledge files, use knowledge-base-hygiene-review instead; for gaps in an engineering deliverables register, use master-document-register-check. Drafts for human review; never approves, authorises or signs off.

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Sync CRM knowledge notes in a vault with the live CRM code repositories: pull the read-only repos, detect which changed, and refresh only the affected reverse-engineering notes and the keep/drop/decide note. Triggers: "/crm-sync", "refresh CRM knowledge", "what changed in the CRM code".

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リファクタリング（構造変更）と機能変更（behavior change）が同一 diff に混在していないかを識別し、structural change に対する external behavior preservation の証拠（テスト・型・静的解析）が揃っているかを diff-time で確認する。証拠不足のときは Characterization Test 追加を促すか question とする。DTO/公開 API の破壊的変更検出は api-compatibility、完了主張の反証や抽出リファクタの性能特性退行は refactor-claim-audit、新知識の命名・責務・境界への反映は knowledge-to-code-alignment、投機的抽象化・スコープ逸脱は altitude-generalization / fix-scope-integrity へ委譲する

Build Retrieval-Augmented Generation (RAG) systems for LLM applications with vector databases and semantic search. Use when implementing knowledge-grounded AI, building document Q&A systems, or integrating LLMs with external knowledge bases.

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Understand how ideas evolved over time to find old solutions for new problems and avoid repeating past failures

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Set up and run knowledge distillation (on-policy, off-policy, or multi-teacher) from a teacher model to a student model using the Tinker API. Use when the user wants to distill knowledge, compress models, or train a student from a teacher.

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Zero-Knowledge Proofs (ZKPs) allow a prover to demonstrate Bilgi: a secret (such as a password or private key) without revealing the secret itself. bu skill implements the Schnorr identificati

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Search the documents uploaded for the CURRENT task/row (e.g. an Agentic Table row's attached files) via the `unique-cli uploaded-search "<query>"` command, with the same per-turn citation tracking as `unique-cli search` so cited facts render as `<sup>N</sup>` footnotes and clickable reference chips on the Unique platform. ALWAYS use this skill when the user refers to documents they uploaded/attached to this task, or when you need facts from the task's own attached files. These uploaded files are scoped to the chat, NOT to a knowledge-base folder, so they will NEVER appear in `unique-cli search` results no matter the folder or metadata filters. Use `unique-cli search` for the knowledge base and this command for the task's uploaded documents; the two are complementary and citation numbering is shared across them within a turn.

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作業パターンを skill 化すべきか判定する共通スキル。org-retro と org-curate から呼ばれ、 「skill 化推奨 / 候補止まり / curated ノートのまま」の 3 値と根拠を返す。 自動 skill 化はせず、推奨は machine-local な knowledge/skill-candidates.local.md に追記し、 窓口が候補キューが溜まった時点でバッチで人間に問い合わせる二段構え。

蓄積された生の学び（knowledge/raw/）を整理・統合する。 ディスパッチャーが worker クローズ時の閾値チェック (tools/check_curate_threshold.py) 超過でオンデマンド起動した キュレーターから 1 回だけ呼び出される（常駐 /loop は廃止）。 手動で「知見を整理して」と言われたときにも使う。

skill の棚卸し（廃止候補 / 重複統合 / owner 明記チェック）。 状態ベースで発火する: 候補キュー knowledge/skill-candidates.md の pending が 5 件以上、 または .claude/skills/ 配下の work-skill 数（org-* を除く）が 20 以上になった場合のみ実行。 時間ベースの /loop では起動しない（変化の無い日に raw ログを汚す副作用を避けるため）。

Personal App-in-Skill customer-support desk (help desk) that triages incoming support tickets from email, WhatsApp, web chat, contact forms, and WeChat, drafts KB-grounded replies, and proposes actions, then holds every send behind a human approval queue with a pre-send quality gate (support-qa) that outputs SHIP/FIX/BLOCK, plus SLA and CSAT tracking. Use when the user invokes $kelly-support or /kelly-support, mentions 客服 / 工单 / 支持台, a support desk, help desk, customer service, support tickets, ticket triage, a knowledge base or canned macros, SLA / first-response time, CSAT, escalations, or refund requests, wants incoming tickets triaged into a needs_review → approved → done pipeline, agent-drafted replies grounded in a knowledge base and reviewed before sending, refunds and commitments gated behind human approval, or breaching-SLA tickets surfaced. This is the OPERATOR desk (agent + human), distinct from a visitor-facing chat bubble.

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SEO + GEO (AI-search) + brand-entity desk (Busabase App-in-Skill) — wired to Google Search Console for a dashboard over search analytics and an agent-prepared SEO opportunities review queue, plus an AI-visibility tracker (are we cited in ChatGPT / Perplexity / Gemini / Claude / Copilot answers), a GEO content-optimization queue gated by geo-qa, and an entity / knowledge-panel readiness checklist. Use when the user invokes $kelly-seo or /kelly-seo, mentions SEO, Google Search Console, GSC, search analytics, search performance, queries, keywords, rankings, positions, impressions, clicks, CTR, striking-distance queries, title/meta rewrites, content briefs, internal links, SEO opportunities review, GEO, generative engine optimization, AI search, AI visibility, being cited by AI answer engines, ChatGPT/Perplexity/Gemini/Claude/Copilot citations, brand entity, knowledge panel, Wikidata, schema.org, or entity readiness.

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Drive the AgentWiki knowledge platform via its CLI (`@aiagentwiki/cli`, command `agentwiki`) and MCP server (`https://api.agentwiki.cc/mcp`). Use this skill whenever the user mentions AgentWiki, agentwiki.cc, app.agentwiki.cc, the `agentwiki` CLI, the `@aiagentwiki/cli` npm package, the AgentWiki MCP server, or asks to create/list/search/share/publish documents, manage folders or tags, upload files or images, deploy static sites, traverse a knowledge graph, or query their AgentWiki workspace from the terminal or as an AI agent. Triggers include phrases like "agentwiki login", "aw_… API key", "create a doc on agentwiki", "search my wiki", "publish to agentwiki", "agentwiki MCP", or any task that would otherwise require opening app.agentwiki.cc manually.

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Transforms conversations and discussions into structured documentation pages in Notion. Captures insights, decisions, and knowledge from chat context, formats appropriately, and saves to wikis or databases with proper organization and linking for easy discovery.

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Data framework for building LLM applications with RAG. Specializes in document ingestion (300+ connectors), indexing, and querying. Features vector indices, query engines, agents, and multi-modal support. Use for document Q&A, chatbots, knowledge retrieval, or building RAG pipelines. Best for data-centric LLM applications.

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Biological knowledge: query curated assertions and reference databases for genes, variants, pathways, regulation, cell types, reference atlases, perturbation-derived relationships, phenotypes, therapeutic targets, drugs, interactions, annotations, signatures, compounds, and predicted structures. Use for identifiers, marker sets, gene sets, associations, ontologies, database facts, and cross-database coverage.

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