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Found 215 Skills
Search group Yuque knowledge bases with natural language queries and provide summarized answers with key points and source links. For group use — searches within team/group repositories. Requires group Token.
Instructs an AI assistant to navigate a company knowledge base — searching docs, synthesizing answers, citing sources, and flagging gaps or outdated information.
Full optimization workflow, sub-agent launch templates, agent communication contracts, default configurations, tuning strategy, and knowledge base update protocol. Use when: (1) starting an optimization cycle, (2) launching a Profiler or Designer sub-agent, (3) interpreting or formatting agent communication, (4) updating the knowledge base after a profiling or implementation iteration, (5) deciding default configurations or tuning strategy for a kernel.
Use when building or maintaining a persistent personal knowledge base (second brain) in Obsidian where an LLM incrementally ingests sources, updates entity/concept pages, maintains cross-references, and keeps a synthesis current. Triggers include "second brain", "Obsidian wiki", "personal knowledge management", "ingest this paper/article/book", "build a research wiki", "compound knowledge", "Memex", or whenever the user wants knowledge to accumulate across sessions instead of being re-derived by RAG on every query.
Automatically detects story settings (genres, time periods, themes) based on keywords and activates corresponding knowledge bases - works silently in the background to provide relevant writing guidance without user intervention
Set up the Claude Brain Logseq graph (first-time) or add a new project page. Triggers: "init brain", "setup brain", "init brain project <name>", "add project to brain". Don't fire for loads, saves, or status checks — those are handled by brain-load, brain-save, and brain-status.
Health check and maintenance of the wiki. Activates when the user asks to audit, verify, clean up, or organize the knowledge base.
Compile durable knowledge into interlinked-markdown stores the "karpathy method" way — atomic files, [[wiki-links]], a maintained index. Use after producing research, intel, a digest, a learned non-obvious fact, or finishing any knowledge-shaped task, BEFORE you close it. Also when asked to "save this", "write this to the wiki", "update the wiki/memory", "log this finding", "structure this knowledge", or "follow the karpathy method".
Run a health check on existing memory (the wiki substrate) and goal artifacts. Use this when the user wants to lint the wiki, health-check the knowledge base, find orphan pages, spot broken or missing cross-links, clean up stale claims and unresolved wikilinks with safe local fixes, consolidate a legacy root `overview.md` into `index.md`, or health-check goal artifacts. Not for adding new material; use /loam::adding-to-memory or /loam::learning-from-session for that.
Inspect an existing memory corpus (wiki substrate) and align it to this repo's Obsidian-friendly note-graph conventions. Use this when the user wants to import, normalize, retrofit, or clean up existing memory, notes folder, vault, docs tree, or mixed markdown knowledge base. In monorepos, also use it to align relevant AGENTS.md and CLAUDE.md files. Excludes goals/ from normalization. Not for routine wiki maintenance; use /loam::linting-memory for that.
Read a local source file or synthesize conversation context, then integrate admitted content directly into topic, entity, concept, and analysis pages in existing memory (the wiki substrate). Use this when the user wants to add a source to the wiki, add a document, ingest a local note, transcript, article, report, or PDF, or explicitly preserve the current conversation as a topic note. For session-learning routing across wiki, guidance, checkpoint, task annotation, or discard, use /loam::learning-from-session. Must not ingest a goal wholesale; admit only independently reusable findings.
Synthesizes trust boundaries, attack surfaces, and attacker profiles into a living threat model. Use as Stage B of the Knowledge Base generation process, reading architecture and entity definitions from the KB. Don't use for analyzing source code or extracting raw learnings from JSONL files.