Total 55,344 skills, AI & Machine Learning has 9197 skills
Showing 12 of 9197 skills
ASI skill integrating polynomial functors, free monad/cofree comonad module action, operadic decomposition, and open games for compositional intelligence.
Debug and trace C/C++/Rust programs with the GNU Debugger (GDB) without blocking the agent. Use when you need to set tracepoints, inspect variables, or monitor a running process while staying responsive to the user.
Clean code patterns for Azure AI Search Python SDK (azure-search-documents). Use when building search applications, creating/managing indexes, implementing agentic retrieval with knowledge bases, or working with vector/hybrid search. Covers SearchClient, SearchIndexClient, SearchIndexerClient, and KnowledgeBaseRetrievalClient.
Market intelligence, competitive analysis, technical evaluations, and technology decisions. Use when researching companies, analyzing competitors, evaluating frameworks, or making tech stack decisions.
This skill should be used when the user asks to "humanize text", "make this sound more human", "detect AI writing", "fix AI-sounding content", "copy edit for naturalness", "rewrite to sound less robotic", "check if this sounds AI-generated", or needs guidance on making written content feel authentically human while preserving its original tone.
LLM Tuning Patterns
Structured memory creation workflow. Converts messy notes, conversations, and unstructured thoughts into well-typed, tagged, confidence-scored memories. Uses 1-question-at-a-time clarification to avoid cognitive overload.
FORGE Vector Memory — Diagnostic tool for the vector memory index. Operations: sync, search, status, reset. Usage: /forge-memory sync | /forge-memory search "query" | /forge-memory status | /forge-memory reset
Tech Spec을 분석하여 Epic/Story 구조를 생성하고, Story 5개 이상일 때 병렬로 Story 문서를 작성한다. Distribute 조율 패턴.
Build a structured taxonomy of failure modes from open-coded trace annotations. Use this skill whenever the user has freeform annotations from reviewing LLM traces and wants to cluster them into a coherent, non-overlapping set of binary failure categories (axial coding). Also use when the user mentions "failure modes", "error taxonomy", "axial coding", "cluster annotations", "categorize errors", "failure analysis", or wants to go from raw observation notes to structured evaluation criteria. This skill covers the full pipeline: grouping open codes, defining failure modes, re-labeling traces, and quantifying error rates.
AI Native Camp Day 5 콘텐츠 소화 스킬 만들기. fetch-tweet, fetch-youtube, content-digest 3개 스킬을 직접 만들고 활용한다. "5일차", "Day 5", "fetch", "콘텐츠 스킬", "트윗 스킬", "유튜브 스킬", "다이제스트 스킬" 요청에 사용.
Comprehensive antibody engineering and optimization for therapeutic development. Covers humanization, affinity maturation, developability assessment, and immunogenicity prediction. Use when asked to optimize antibodies, humanize sequences, or engineer therapeutic antibodies from lead to clinical candidate.