「knowledge base」の検索結果

Knowledge base from MIL-HDBK-61B, the DoD Configuration Management (CM) guidance handbook (7 April 2020, cataloged with Change 1, 2025). Use for defense CM across the acquisition and sustainment life cycle: the five CM functions (planning, identification, control/change management, status accounting, verification & audit), the three configuration baselines (FBL/ABL/PBL) and FCD→ACD→PCD documentation, configuration items (CIs/HWCI/CSCI), change instruments (ECP/NOR/RFV), the CCB and Class I/II classification, CDCA/AA authority, FCA/PCA audits, data management and data rights (CDRL/DID, DFARS rights ladder, master/authoritative source), and tailoring CM by phase (EIA-649-1 R/T/NR matrix, Appendix C templates), plus digital-era CM (digital twin, viewpoints, MOSA). NOTE: 61B is advisory guidance that adopts and points to the SAE EIA-649 / EIA-649-1 / GEIA-HB-649 suite for the authoritative CM requirements — this pack names and describes that suite but does not reproduce its copyrighted requirement text. Thin on: the EIA-649 requirement clauses themselves, form-level detail (DD-form field instructions, full DID content), and non-DoD/commercial CM frameworks.

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。

Knowledge base from the GAO Cost Estimating and Assessment Guide (GAO-20-195G). Use for program cost estimating: the four characteristics of a reliable estimate (comprehensive, well documented, accurate, credible) and the 18 best practices; the 12-step cost estimating process (purpose, plan, technical baseline, WBS, ground rules and assumptions, data, point estimate, sensitivity, risk/uncertainty, document, present, update); estimating methods (analogy, parametric, engineering build-up, learning curves); Monte Carlo risk/uncertainty analysis, confidence levels and contingency; auditing and validating an estimate against the characteristics; earned value management (EIA-748, BCWS/BCWP/ACWP, CPI/SPI/TCPI, EAC, PMB, IBR); and specialized techniques (software cost estimating, learning curves, Analysis of Alternatives, WBS templates, the Green Book internal-control framework). This is the cost-estimating companion to the GAO Schedule and Technology Readiness Assessment guides. Does not cover schedule estimating/risk in depth (see the GAO Schedule Assessment Guide), agency-specific cost models, or the full text of referenced standards (EIA-748, MIL-STD-881D, the Green Book) — these are named, not reproduced.

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。

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).

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 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".

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 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.

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。

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.

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。

Busabase-backed App-in-Skill dashboard for a shared LLM gateway's cost and model governance — daily spend trend, cost breakdown by service/model, a canary-rollout status board, and a deterministic cost/error anomaly list. Use when the user invokes $kelly-llm-gateway or /kelly-llm-gateway, or wants to review LLM gateway spend, model routing, canary rollouts, rollback readiness, or cost/error anomalies for services routed through one shared gateway to multiple LLM providers/models. Human actions (promote/rollback/hold a rollout, acknowledge an anomaly) write directly onto the route's own Busabase record — this skill never changes a real routing config.

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。

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.

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。

Personal App-in-Skill inbound-inquiry reception desk (Busabase App-in-Skill) for cross-border/foreign-trade sellers, aggregating WhatsApp, Instagram, Messenger, and email inquiries into a pipeline with a product knowledge base, quote worksheet, approval queue, and follow-up reminders. Use when the user invokes $kelly-inquiry or /kelly-inquiry, mentions 询盘, inquiry desk, WhatsApp leads, quote management, 报价, foreign trade sales, follow-up reminders, or a lead pipeline, wants inquiries triaged into new/replied/quoted/negotiating/won/lost stages, agent-drafted replies and quotes reviewed before sending, or stale deals surfaced.

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。

Convert external documents — specs, contracts, reports — to markdown for knowledge base ingestion.

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。

This skill adds data(like resources) to OpenViking Context Database (aka. ov). Use when an agent needs to add files, data from URLs, or external knowledge during interactions. Trigger this tool when 1. is explicitly requested adding files or knowledge; 2. identifies valuable resources worth importing; 3. the user mentioned adding to OV/OpenViking/Context Database. This skill helps how to use CLI like `ov add-resource`, `ov add-skill` and `ov add-memory` to add resource data, skill files, memory files to OpenViking.

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。

SiYuan Note API for searching, reading, creating, and managing blocks and documents in a self-hosted knowledge base via curl.

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。

Search personal knowledge bases, notes, docs, and meeting transcripts locally using qmd — a hybrid retrieval engine with BM25, vector search, and LLM reranking. Supports CLI and MCP 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。

Search local documents, files, notes, and knowledge bases. Index directories, search with BM25/vector/hybrid, get AI answers with citations. Use when user wants to search files, find documents, query notes, look up information in local folders, index a directory, set up document search, build a knowledge base, needs RAG/semantic search, or wants to start a local web UI for their docs.

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 and query AI-powered knowledge bases from claude-mem observations. Use when users want to create focused "brains" from their observation history, ask questions about past work patterns, or compile expertise on specific topics.

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。

Extract business domain knowledge from a codebase and generate an interactive domain flow graph. Works standalone (lightweight scan) or derives from an existing /understand knowledge graph.

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。

Navigate and understand codebase structure using the knowledge graph

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。

Deep-research a topic end to end and produce a permanent, reusable knowledge asset: archive every primary source verbatim (gated by the user's privacy/retention posture), write one 1:1 summary per source, then synthesize a single self-contained compendium page. Depth is a dial (base synthesis → grounded primaries → books + counter-canon → saturation), each level an idempotent superset of the one below. Distinct from data-research (structured trackers) and perplexity-research (web deltas): this produces prose knowledge synthesis backed by an archived source corpus.

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。

Reference skill for Zoom Virtual Agent. Use after routing to a virtual-agent workflow when implementing web embeds, Android or iOS wrapper integrations, knowledge-base sync, lifecycle handling, or troubleshooting.

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。

Two-tier memory system that makes Claude a true workplace collaborator. Decodes shorthand, acronyms, nicknames, and internal language so Claude understands requests like a colleague would. CLAUDE.md for working memory, memory/ directory for the full knowledge base.

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。

Draft a knowledge base article from a resolved issue or common question. Use when a ticket resolution is worth documenting for self-service, the same question keeps coming up, a workaround needs to be published, or a known issue should be communicated to customers.

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。

Generate or improve a company-specific data analysis skill by extracting tribal knowledge from analysts. BOOTSTRAP MODE - Triggers: "Create a data context skill", "Set up data analysis for our warehouse", "Help me create a skill for our database", "Generate a data skill for [company]" → Discovers schemas, asks key questions, generates initial skill with reference files ITERATION MODE - Triggers: "Add context about [domain]", "The skill needs more info about [topic]", "Update the data skill with [metrics/tables/terminology]", "Improve the [domain] reference" → Loads existing skill, asks targeted questions, appends/updates reference files Use when data analysts want Claude to understand their company's specific data warehouse, terminology, metrics definitions, and common query 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。

Combines search results from multiple sources into coherent, deduplicated answers with source attribution. Handles confidence scoring based on freshness and authority, and summarizes large result sets effectively.

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。

Analyze indexed codebases via graph database (neug) and vector index (zvec). Covers call graphs, dependencies, dead code, hotspots, module coupling, architecture reports, semantic search, impact analysis, bug root cause from GitHub issues, class diagrams (UML), and PR review (risk scoring, conflict detection, auto-merge candidates, labeling). Also covers creating, inspecting, and repairing a CodeScope index. Use for: code structure, who calls what, why something changed, similar functions, module boundaries, bug tracing, class relationships, PR risk/conflicts, or any question benefiting from a code knowledge graph. Applies when a `.codegraph` index exists in the workspace, or when the user wants to create one.

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。