「knowledge」の検索結果

Test Retrieval-Augmented Generation (RAG) systems for data poisoning, prompt injection via retrieved documents, and data exfiltration through manipulated context windows. Use this skill when assessing RAG-based chatbots, knowledge bases, enterprise AI assistants, or any system that augments LLM responses with external document retrieval. Covers document injection, embedding manipulation, knowledge base poisoning, and cross-document inference attacks.

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Study unfamiliar codebases and produce evidence-backed knowledge artifacts. Use for repository orientation, architecture mapping, subsystem tracing, onboarding, or codebase documentation.

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Create and maintain the shared knowledge/ context files that every other marketing skill reads - brand voice, ICP personas, positioning, KPIs, and the learnings log. Run this once per company or client before using any other marketing skill. Use when the user says "set up brand context", "onboard my brand", "set up the marketing knowledge base", "the skills don't know my brand", "update our positioning", "our voice has changed", "add a new persona", or when any marketing skill reports that a knowledge/ file is missing. Also use when output sounds generic or off-brand. For writing a positioning statement as a standalone document, see positioning-doc. For deep persona research from raw sources, see customer-research.

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Searches, retrieves, and synthesizes official Google developer documentation across Google Cloud, AI/Gemini, Android, Chrome, Web, Flutter, Go, Firebase, and other Google developer platforms. Integrates with the Developer Knowledge MCP server (search_documents, get_documents, answer_query) or the Developer Knowledge REST API fallback. Use when searching for gcloud CLI commands, API syntax, IAM permissions, official documentation, architectural comparisons, or product choice overviews. Don't use for local filesystem lookups or non-Google documentation.

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Discovers requirements and designs an end-to-end governed agentic analytics solution using Knowledge Catalog and Managed Service for Apache Spark (Lightning Engine). Use when designing data science and analytics workflows across structured and unstructured distributed data (including in S3, Azure Blob, AlloyDB, and Iceberg), establishing metadata governance with Knowledge Catalog aspect types, or grounding agentic IDEs (VS Code, Antigravity) by using the Google Cloud Data Agent Kit. Don't use for provisioning borderless data lakehouse infrastructure (use google-cloud-solution-agentic-ai-borderless-data-lakehouse instead).

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Use this skill when designing the UX and content strategy for a Salesforce-powered self-service portal or help center — pre-deflection article surfacing, Help Center search UX, friction-calibrated case submission forms, community peer support layers, and deflection measurement. Trigger keywords: design self-service portal, case deflection strategy, help center UX design, reduce support volume with self-service, knowledge base search experience. NOT for picking which contact reasons to deflect and through which channel — use architect/case-deflection-strategy. NOT for building the Experience Cloud site itself — use admin/experience-cloud-site-setup. More trigger keywords: article visibility matrix IsVisibleInCsp IsVisibleInPkb, which articles show in the customer portal, case visibility for portal users, CaseComment IsPublished customer visible comment, self-registration profile and account for a portal, Help Center template does not support login, deflection metrics SOQL KnowledgeArticleViewStat, self-service journey map, moderation policy for a support community.

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Use when planning, sequencing, or troubleshooting an org-wide migration from Salesforce Classic to Lightning Experience. Covers the LEX Transition Assistant Readiness Check, asset triage matrix (Visualforce, JavaScript buttons, page layouts, Knowledge, email templates, list views, AppExchange), pilot/wave rollout sequencing, end-user adoption telemetry, and cutover criteria. Triggers: 'lightning experience transition', 'classic to lightning migration plan', 'LEX readiness check', 'why are some users still on Classic', 'turning on Lightning for everyone'. NOT for individual asset migrations like a single VF page (use lwc/visualforce-to-lwc-migration), a single JavaScript button (use admin/custom-button-to-action-migration), or Knowledge article migration (use admin/knowledge-classic-to-lightning) — this skill orchestrates the program. NOT for Lightning App Builder page design (use admin/lightning-app-builder-advanced).

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Capture and organize knowledge in Notion—research findings, best practices, reference materials. Build searchable knowledge base for your team and future self.

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Search, read, create, edit, and maintain knowledge in a Memex KB via the `mx` CLI. Use when the user asks to search the KB, find documentation, check project or organizational docs, add or update knowledge-base entries, audit KB quality, or when reusable knowledge should be captured for future agents.

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Render a wiki into ONE self-contained HTML snapshot (pages + provenance) that opens offline — no CDN, no remote assets, page content HTML-escaped. Use to share or review accumulated wiki knowledge as a static page. NOT for capturing/approving knowledge (see mk:wiki); NOT for generic code/diagram visuals (see mk:preview).

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Interactive Archon integration for knowledge base and project management via REST API. On first use, asks for Archon host URL. Use when searching documentation, managing projects/tasks, or querying indexed knowledge. Provides RAG-powered semantic search, website crawling, document upload, hierarchical project/task management, and document versioning. Always try Archon first for external documentation and knowledge retrieval before using other sources.

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Deep analysis of knowledge distillation techniques for compressing large models into smaller efficient ones

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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。

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。

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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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 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 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".

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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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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.

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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.

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。