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Found 1,646 Skills
Compress and simplify prompts to preserve meaning while reducing use of context
Create, improve, or optimize prompts using best practices
Spawn specialized sub-agents with context handoff for complex multi-phase tasks. Enables expertise delegation within a session with automatic context merging and depth limiting to prevent infinite loops.
Operate LM Studio's `lms` CLI and local/remote LM Studio servers for model discovery, server status checks, model loading, endpoint smoke tests, and downstream OpenAI-compatible wiring. Use when the user mentions LM Studio, `lms`, a local model server, `/v1/models`, a remote LM Studio host, or wants to connect another tool to LM Studio; even if they only ask to test a local OpenAI-compatible endpoint or choose the correct loaded-model identifier. Triggers on: lmstudio, lm studio, lms, local model server, LM Studio API, LM Studio endpoint, /v1/models, connect Strix to LM Studio, load model in LM Studio.
Generate a periodic knowledge digest — a human-readable newsletter-style summary of what was learned, updated, and connected in your wiki over a specified period (day/week/month). Use when the user says "what did I learn this week", "give me a digest", "weekly summary", "knowledge report", "what's new in my wiki", "/wiki-digest [period]", "summarize my recent learning", or wants a readable overview of recent wiki activity. Distinct from wiki-status (which reports ingestion delta of sources) — wiki-digest summarizes *knowledge*, not sources.
Deep architectural knowledge of AI Agent Harness design patterns, implementation strategies, and Claude Code internals for building production-grade AI agents
Expert guidance on AI Agent Harness architecture based on the comprehensive Claude Code analysis book
Expert knowledge of agentic AI design patterns for autonomous agent development
Integrate TileGym kernels into Hugging Face `transformers` models by replacing the library's submodule(s) and certain class(es)' implementations, and patching certain class(es)' init/forward/load weight methods prior to instantiating models. Used when the user requires integrating TileGym kernels into `transformers` models.
Deduplicate and synthesize raw concept stubs into a tiered intellectual map (T1 Canon to T4 Riff), tracing idea evolution across sources over time. Transforms thousands of raw concept pages into a curated intellectual fingerprint.
A-share multi-agent investment research framework with 7 AI analysts, bull/bear debate, and risk assessment adapted for Chinese stock market
Audits instrumentation health of existing Arize traces. Runs deterministic checks over a bounded span sample (orphaned/uncategorized/duplicate spans, flat structure, blank root I/O, unset status, missing token counts or children) and returns a ranked report. Use when the user asks why traces look empty/flat/broken, wants to verify instrumentation is healthy, find instrumentation issues, or why evals or token/cost dashboards show n/a or zero. To debug app behavior or errors, use arize-trace.