136건 · 관련도순
This skill should be used when the user asks to "compress context", "summarize conversation history", "implement compaction", "reduce token usage", or mentions context compression, structured summarization, tokens-per-task optimization, or long-running agent sessions exceeding context limits.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
This skill should be used when the user asks to "share memory between agents", "KV cache compaction for multi-agent", "orchestrator worker context", "latent briefing", "reduce worker tokens", "cross-agent memory without summarization", or discusses Attention Matching compaction, recursive language models with workers, or token explosion in hierarchical agents.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Process and generate multimedia content using Google Gemini API. Capabilities include analyze audio files (transcription with timestamps, summarization, speech understanding, music/sound analysis up to 9.5 hours), understand images (captioning, object detection, OCR, visual Q&A, segmentation), pr...
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
AI-powered presentation generation via the 2slides API — create slides from text, match a reference image style, summarize documents into decks, add AI voice narration, and export pages/audio. Use for any "make slides", "create a deck", or "slides from this document" request.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Use Transformers.js to run state-of-the-art machine learning models directly in JavaScript/TypeScript. Supports NLP (text classification, translation, summarization), computer vision (image classification, object detection), audio (speech recognition, audio classification), and multimodal tasks. Works in browsers and server-side runtimes (Node.js, Bun, Deno) with WebGPU/WASM using pre-trained models from Hugging Face Hub.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Hostile self-review of recently written AI workflow code that then applies fixes (agents, tools, chat context, summarization, tokens, attachments). Use after generating AI systems code, or when asked to self-review and harden it. Not for PR peer review (code-review) or no-edit devil's-advocate attacks (adversarial-challenge). Prefer: adversarial-challenge → decide → this skill.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Devil's-advocate attack on AI workflow designs — threat model, assumption challenges, code critiques, and hypothetical redesigns with no file edits. Use for "attack this design", devil's advocate, or hostile challenge on agents/tools/summarization/tokens. Not for PR peer review (code-review) or self-review that applies fixes (ai-systems-review). Prefer: this skill → decide → ai-systems-review.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Strategies for managing LLM context windows including summarization, trimming, routing, and avoiding context rot Use when: context window, token limit, context management, context engineering, long context.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
AI-powered presentation generation via the 2slides API — create slides from text, match a reference image style, summarize documents into decks, add AI voice narration, and export pages/audio. Use for any "make slides", "create a deck", or "slides from this document" request.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Agent context budget guard and artifact compression workflow. USE FOR: image-heavy QA, compare board review, screenshot batches, large keep/todo/task manifests, long notes handoff, and md/json diff summarization. Trigger this before reading or forwarding heavy artifacts when token growth is a risk.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Data structures and algorithms for AI agent episodic memory. Covers vector stores (HNSW, IVF, PQ), temporal indexing, knowledge graphs with triple stores, hierarchical summarization, forgetting curves, working/long-term/ procedural memory, and memory consolidation. Deep analysis of MemGPT/Letta, Zep/Graphiti, Mem0, and the Stanford generative agents memory architecture. Teaches the CS fundamentals behind how agents remember, retrieve, and forget. Activate on: "agent memory", "episodic memory", "vector search algorithm", "HNSW", "memory retrieval", "forgetting curve", "knowledge graph memory", "MemGPT", "Letta", "Zep", "Mem0", "memory consolidation", "temporal retrieval", "agent long-term memory", "memory layer". NOT for: conversation protocol design (use agent-conversation-protocols), agent infrastructure selection (use agentic-infrastructure-2026), building RAG pipelines (use ai-engineer).
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Treat tokens as a coding swarm's cost-of-goods-sold and legibility engine while accounting for finite subscription allowance when per-call price is unknown. Covers provider windows, remaining-usage evidence, burn forecasts, preemptive checkpoint/model switching, per-agent budgets, compaction, shared digests, context degradation, and spend metering. Activate on: "token budget", "context budget", "compaction strategy", "briefing as compression", "context rot", "summarization collapse", "COGS for agents", "subscription usage remaining", "five-hour or weekly limit", "burn forecast", "model switch before limit", or "/context-economics-for-agent-swarms". NOT for: memory architecture (use always-on-agent-architecture), single-prompt wording (use prompt-engineer), mechanism-design proofs (use nisan-et-al-2007-algorithmic-game-theory), physical containment (use sandboxed-adversarial-test-harness), or production rebodiment and fencing (use agent-resurrection-and-body-continuity).
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Automatically fetch YouTube transcripts, generate structured summaries, and send full transcripts to messaging platforms. Use when a request includes YouTube URLs and needs metadata, key insights, or downloadable transcripts.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Intelligent pattern selection for Fabric CLI. Automatically selects the right pattern from 242+ specialized prompts based on your intent - threat modeling, analysis, summarization, content creation, extraction, and more. USE WHEN processing content, analyzing data, creating summaries, threat modeling, or transforming text.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Work with state-of-the-art machine learning models for NLP, computer vision, audio, and multimodal tasks using HuggingFace Transformers. This skill should be used when fine-tuning pre-trained models, performing inference with pipelines, generating text, training sequence models, or working with BERT, GPT, T5, ViT, and other transformer architectures. Covers model loading, tokenization, training with Trainer API, text generation strategies, and task-specific patterns for classification, NER, QA, summarization, translation, and image tasks. (plugin:scientific-packages@claude-scientific-skills)
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Strategies for managing LLM context windows including summarization, trimming, routing, and avoiding context rot Use when: context window, token limit, context management, context engineering, long context.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.