Results for “owl”

Use this skill when setting up, configuring, or managing Salesforce Lightning Knowledge — enabling the feature, designing record types on Knowledge__kav, configuring Data Categories for organization and visibility control, setting up publishing workflows, and layering approval processes. Trigger keywords: Lightning Knowledge setup, Knowledge article record types, Data Category visibility, Knowledge publishing workflow, Knowledge__kav configuration. NOT for converting Classic Article Types to Lightning — use admin/knowledge-classic-to-lightning. NOT for bulk-loading articles from an external help center — use data/knowledge-article-import. NOT for sizing the taxonomy itself (how many groups, how deep) — use architect/knowledge-taxonomy-design.

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Organize knowledge notes with links that explain their relationships, search saved information, review conflicting claims, and prepare weekly digests. Retrieve earlier work sessions and conversations by topic or date to recover context before continuing a task.

• Find saved knowledge on a topic
• Add source material and update related notes
• Review conflicting claims across notes

Design AI agent analytics workflows across distributed tables and documents. Combine Knowledge Catalog for metadata, meaning information about data and its usage rules, with managed Spark processing to produce architecture diagrams, deployment guidance, and validation plans.

• Design analytics across data stored in multiple clouds
• Define metadata and governance with Knowledge Catalog
• Plan Data Agent Kit use in VS Code or Antigravity

Create, validate, and enrich Open Knowledge Format (OKF) bundles — the open spec for representing organizational knowledge as markdown files with YAML frontmatter. Use when the user mentions 'OKF', 'Open Knowledge Format', 'knowledge bundle', 'OKF bundle', 'create a knowledge base for agents', 'validate OKF', 'convert to OKF', 'enrich knowledge docs', 'agent-readable knowledge', 'LLM wiki', 'knowledge catalog', 'kcmd', or wants to structure knowledge as markdown files for AI agent consumption. Also use when the user has a directory of markdown files and wants to make them interoperable or conformant with the OKF standard. Even for simple requests like 'make this folder OKF conformant' — the skill has critical structural rules the agent needs.

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Organize articles, documents, and insights from conversations into a personal Markdown wiki. Maintain source references, links between related topics, an index, and an activity log, then answer questions using the knowledge already collected.

• Save key points from articles and documents
• Preserve insights from conversations
• Find answers in accumulated knowledge

Migrating Classic Knowledge (KnowledgeArticleVersion / Article Types) to Lightning Knowledge (Knowledge__kav with record types): article-type-to-record-type mapping, multi-language translation preservation, data category re-architecture, file attachment porting, version and publication-state retention, channel visibility translation, and downstream Case Feed / Community / Bot rewiring. NOT for new Lightning Knowledge setup or editorial publishing workflow design (use admin/knowledge-base-administration).

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Read when the user asks what OpenKnowledge is, wants to install it on a repository, wants to open or preview a single markdown file that is not part of an OpenKnowledge project, wants to share an OpenKnowledge project with collaborators, asks whether OpenKnowledge supports a particular capability, or asks how `ok init` / `ok cowork` / OK Desktop set up a project. Do NOT load to perform OpenKnowledge reads/writes — the runtime guidance for editing markdown inside an initialized OK project ships as a separate project-local skill installed into each detected agent's skills dir (for example `.claude/skills/open-knowledge/`) whenever `ok init` runs.

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Save, organize, synchronize, and retrieve documents, conversations, and project information across local files, memory tools, knowledge repositories, and databases. Check for duplicates before storing information and choose a suitable home for each type of knowledge.

• Saving documents and conversations to a knowledge base
• Consolidating duplicate notes
• Synchronizing knowledge across storage systems

Turn a founder’s business vision into linked Markdown files through a phased interview covering strategy, finance, sales, operations, and governance. Build a company knowledge bundle that people and AI agents can read, with progress tracking and decision history.

• Structure a new business through phased interviews
• Build a company knowledge base for AI agents
• Document decisions and track planning progress

"Apply the Knowledge-Based View (Grant, 1996) and Nonaka and Takeuchi's SECI model to analyze how organizations create, transfer, and integrate knowledge for competitive advantage. Use this skill when the user needs to design knowledge management systems, understand why knowledge transfer fails across teams, evaluate knowledge creation processes, or when they ask 'how do we capture tacit knowledge', 'why does knowledge stay siloed', or 'how can we turn individual expertise into organizational capability'.".

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Build focused knowledge bases from claude-mem observations, filtered by topic, project, date, or file. Load selected records into an AI conversation to ask contextual follow-up questions about past decisions, fixes, and work patterns.

• Compiling knowledge about a specific technical topic
• Reviewing decisions made during a chosen period
• Asking follow-up questions about a service's past bug fixes

Turn a structured LLM wiki into an interactive knowledge graph. It maps articles, topics, and sources, then adds entities and inferred relationships to help readers explore connections beyond the wiki’s explicit links.

• Explore connections across wiki articles
• Find relationships beyond explicit links
• View articles and sources by topic

Synthesize OpenViking knowledge bases or document collections into topic-based Markdown pages that explain trends, changes, comparisons, and implications. Keep conclusions traceable to source evidence, distinguish facts from inferences, and preserve disagreements and uncertainty.

• Comparing changes across financial reports
• Finding shared trends across knowledge bases
• Organizing sourced conclusions by topic

Search shared organizational knowledge to answer questions about people, projects, decisions, and history. Save lasting conclusions as pages, update existing knowledge, and link related topics within the areas your account is allowed to access.

• Find the reasons behind a past decision
• Look up knowledge about people and projects
• Save conclusions for use beyond the conversation

Find and synthesize official Google developer documentation for platforms including Google Cloud, Gemini, Android, and Firebase. Retrieve command syntax, API details, permissions, and product comparisons through Developer Knowledge, then turn the findings into practical technical answers.

• Checking gcloud commands and flags
• Looking up API syntax and required permissions
• Comparing Google products and architectures

How to work in a Knowledge Base project (the `knowledge-base` starter pack). Read when the project has the three-layer source-grounded layout — `external-sources/` → `research/` → `articles/` — or when asked how this project is organized. Carries the layer model, per-folder rules, status flows, and log discipline so this guidance does NOT live inside template bodies or log.md. The three procedures live elsewhere: ingest in the platform `open-knowledge` skill, research and consolidate as their own sibling skills in this pack. Complements the platform `open-knowledge` skill; does not replace it.

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Authoritative agent-runtime contract for working inside an OpenKnowledge project — a markdown-CRDT knowledge base exposed over MCP. Use whenever reading, listing, searching, editing, or linting any `.md` or `.mdx` file in the project, and before any `mcp__open-knowledge*` tool call (`exec`, `search`, `write`, `edit`, `lint`, and the rest). Installed by `ok init`, so its presence means this is an OpenKnowledge project and it governs every markdown file here. Covers the read/write tool surface, grounding and linking rules, folder/template conventions, the live browser preview, and the rule that OK's MCP tools — never native file tools — handle in-scope markdown.

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Use when the user asks to "optimize entity presence", "build knowledge graph", "improve knowledge panel", "entity audit", "establish brand entity", "Google does not know my brand", "no knowledge panel", or "establish my brand as an entity". Works standalone with public search and AI query testing; supercharged when you connect ~~knowledge graph + ~~SEO tool + ~~AI monitor for automated entity analysis. For structured data implementation, see schema-markup-generator. For content-level AI optimization, see geo-content-optimizer.

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Turn documents into a growing Markdown wiki and a knowledge graph, a map of entities and their relationships. Preserve source text, update linked pages as new material arrives, and answer questions with supporting evidence.

• Build a reusable knowledge base from documents
• Trace relationships between terms and topics
• Answer questions with source evidence

Audit AWS S3 buckets for public exposure, excessive permissions, and gaps in encryption or versioning settings. Use AWS CLI, Prowler, and access analysis to compile findings, prioritize issues, and plan or apply configuration fixes.

• Reviewing S3 settings in a newly onboarded AWS account
• Investigating alerts about publicly exposed buckets
• Preparing periodic access control audit reports

Audit AWS, Azure, and GCP configurations against CIS Benchmarks, security configuration guidelines. Use automated scan results to identify failed checks, prioritize fixes, report compliance by area, and establish ongoing monitoring for configuration changes.

• Running an initial cloud security configuration audit
• Preparing CIS-related evidence for SOC 2 or ISO 27001 audits
• Prioritizing fixes for failed security checks

Train smaller language models using knowledge distillation, where a larger model supplies examples or prediction targets. This skill covers training losses, generated training data, parameter choices, and output comparisons to support model compression and specialized student models.

• Training a smaller model from a larger teacher
• Fine-tuning with teacher-generated responses
• Distilling domain knowledge into a specialized model

Guide knowledge distillation, where a smaller language model learns from a larger teacher. Covers training from teacher responses or prediction scores, adjusting training settings, and comparing outputs to support model compression and specialized training.

• Compressing a large language model into a smaller model
• Training a specialized model from teacher-generated examples
• Comparing distillation methods and training settings

Build and maintain a Markdown knowledge base from source materials, with linked pages and traceable evidence. Use accumulated knowledge to answer questions, preserve reusable research, and check for conflicting claims, outdated information, broken links, or missing index entries.

• Add source materials to a linked knowledge base
• Answer questions using accumulated research
• Check conflicting or outdated claims