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Found 177 Skills
LLM Wiki — persistent markdown knowledge base that compounds across sessions (Karpathy model)
Store and retrieve agent memories across jobs. Enables long-term context, learning from past interactions, and building agent knowledge bases. Based on OpenClaw's memory-core architecture.
Answer questions against the knowledge base wiki. Use when the user asks a question about their collected knowledge, wants to explore connections between topics, says "what do I know about X", or wants to search their wiki, second brain, or knowledge base.
Document finalized technology selections, architecture decisions, long-term constraints, and coding conventions in the project into searchable permanent documents. No one will remember why X was chosen six months later, but with decision documents, at least the background can be understood before making changes next time. Four types: tech-stack (which tools/libraries/frameworks to use), architecture (how the system is organized), constraint (what is not allowed), convention (what is uniformly done). Trigger scenarios: Proactively push when important choices are made after feature-design or issue-analyze, or when the user says "record decision", "archive technology selection", "ADR", "record this constraint", "write down the convention". Only archive finalized decisions; do not archive under-discussion solutions.
Display the current state of the FPF knowledge base
Interact with Heptabase using the CLI to create, read, and edit notes, journals, tags, and cards, and to browse AI Tutor goals, courses, and lessons. Use when the user asks to manage their Heptabase knowledge base, search cards, work with journals or tags, or read AI Tutor content.
Health check and maintenance of the wiki. Activates when the user asks to audit, verify, clean up, or organize the knowledge base.
Use when generating, updating, or organizing documentation (component/API docs, project indexes, diagrams, tutorials, learning paths) - provides structured workflows and references for docs generation, indexing, diagrams, and teaching.
Build Retrieval-Augmented Generation (RAG) Q&A systems with Claude or OpenAI. Use for creating AI assistants that answer questions from document collections, technical libraries, or knowledge bases.
Extract valuable legal question-answer pairs from lawyer-client communication records and generate structured knowledge base content. This skill should be used when users need to organize client consultation records, extract reusable legal knowledge from conversations, create Q&A knowledge bases, or prepare content marketing materials. Strict client information desensitization is supported.
Interact with the Denser Retriever API to build and query knowledge bases. Use this skill whenever the user wants to create a knowledge base, upload documents (files or URLs), search/query a knowledge base, list or delete knowledge bases or documents, check document processing status, or check account usage/balance. Also trigger when the user mentions 'denser retriever', 'knowledge base', 'document search', 'semantic search', 'RAG pipeline', or wants to index and search their files.
Enables autonomous pattern recognition, storage, and retrieval at project level with self-learning capabilities for continuous improvement