'memory' 검색 결과

Align preprocessed CLIP-seq reads (eCLIP, iCLIP, iCLIP2, PAR-CLIP) to genome with STAR or bowtie2 using crosslink-preserving parameters, choosing between unique-mapper-only and multi-mapper-aware alignment for repeat-binding RBPs, deciding STAR vs HISAT2 memory trade-offs, and applying ENCODE-compatible filters. Use when turning preprocessed CLIP FASTQ into a deduplicated, MAPQ-filtered BAM ready for peak calling or crosslink-site detection.

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Provides integration patterns for LangChain4j with Spring Boot. Configures AI model beans, sets up chat memory with Spring context, integrates RAG pipelines with Spring Data, and handles auto-configuration, dependency injection, and Spring ecosystem integration. Use when embedding LangChain4j into Spring Boot applications, building Java LLM applications with @Bean configuration, or setting up Spring AI patterns.

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Expert in designing and building autonomous AI agents. Masters tool use, memory systems, planning strategies, and multi-agent orchestration.

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Tag a thesis's implicit macro bets — the macro variables its bull case is secretly levered to (AI capex, rates, FX, China decoupling, memory/HBM cycle, energy, regulation) with direction and magnitude. Use when user says "macro exposure", "what macro is [TICKER] betting on", "macro bets", or "rate exposure for [TICKER]".

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Parameter-efficient fine-tuning for LLMs using LoRA, QLoRA, and 25+ methods. Use when fine-tuning large models (7B-70B) with limited GPU memory, when you need to train <1% of parameters with minimal accuracy loss, or for multi-adapter serving. HuggingFace's official library integrated with transformers ecosystem.

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Expert guidance for fast fine-tuning with Unsloth - 2-5x faster training, 50-80% less memory, LoRA/QLoRA optimization

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Serves LLMs with high throughput using vLLM's PagedAttention and continuous batching. Use when deploying production LLM APIs, optimizing inference latency/throughput, or serving models with limited GPU memory. Supports OpenAI-compatible endpoints, quantization (GPTQ/AWQ/FP8), and tensor parallelism.

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Runs LLM inference on CPU, Apple Silicon, and consumer GPUs without NVIDIA hardware. Use for edge deployment, M1/M2/M3 Macs, AMD/Intel GPUs, or when CUDA is unavailable. Supports GGUF quantization (1.5-8 bit) for reduced memory and 4-10× speedup vs PyTorch on CPU.

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Autonomous rule adherence checker. Scans the codebase for rule violations, fixes the highest-impact ones in an isolated worktree, runs full validation, creates a PR. Uses memory to track progress across runs.

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Automated coordination, formatting, and learning from Claude Code operations using intelligent hooks with MCP integration. Includes pre/post task hooks, session management, Git integration, memory coordination, and neural pattern training for enhanced development workflows.

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Advanced Hive Mind collective intelligence system for queen-led multi-agent coordination with consensus mechanisms and persistent memory

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Implement ReasoningBank adaptive learning with AgentDB's 150x faster vector database. Includes trajectory tracking, verdict judgment, memory distillation, and pattern recognition. Use when building self-learning agents, optimizing decision-making, or implementing experience replay systems.

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Interact with DeerFlow AI agent platform via its HTTP API. Use this skill when the user wants to send messages or questions to DeerFlow for research/analysis, start a DeerFlow conversation thread, check DeerFlow status or health, list available models/skills/agents in DeerFlow, manage DeerFlow memory, upload files to DeerFlow threads, or delegate complex research tasks to DeerFlow. Also use when the user mentions deerflow, deer flow, or wants to run a deep research task that DeerFlow can handle.

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Achieve aggressive v3 performance targets: 2.49x-7.47x Flash Attention speedup, 150x-12,500x search improvements, 50-75% memory reduction. Comprehensive benchmarking and optimization suite.

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Apply systematic root cause analysis and debugging methodologies to diagnose and fix bugs, test failures, and unexpected behavior. Use when encountering production issues, investigating test failures, diagnosing performance problems, tracing error sources through call stacks, analyzing logs and stack traces, reproducing inconsistent bugs, debugging race conditions, investigating memory leaks, or applying scientific method to problem-solving before proposing fixes.

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50 core Rust coding conventions covering naming, data types, strings, error handling, memory, concurrency, async, and modern crate recommendations. Use when reviewing code style or setting up project conventions.

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Orchestrate continual learning by delegating transcript mining and AGENTS.md updates to `agents-memory-updater`.

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Audit and improve SwiftUI runtime performance from code review and architecture. Use for requests to diagnose slow rendering, janky scrolling, high CPU/memory usage, excessive view updates, or layout thrash in SwiftUI apps, and to provide guidance for user-run Instruments profiling when code review alone is insufficient.

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Use when the user asks to switch AI role/mode/persona or uses role-trigger words such as Captain, Coordinator, 寫文章, 寫文章模式, 文章模式, 技術文章, 部落格, 出版, 預覽. Routes the request to Project Captain, Publishing Director, or Article Writing Mode, loads the matching keep memory when available, and applies the role's workflow without excessive roleplay.

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Ground framework-specific implementation in official documentation — detect stack versions, fetch authoritative docs, implement cited patterns, cite sources. Load when building with a library or framework where API correctness matters, when the user wants verified or documented code, or before writing framework code from memory. Also triggers on "source driven development", "cite the docs", "check official documentation", "verify against docs", "don't hallucinate APIs". Not for pure logic or renames. Pairs with feature-spec and test-driven-development.

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Bootstrap full agent infrastructure for an existing, already-coded project without modifying a single line of source code. Surveys the repo, infers conventions from manifests, README, git history, and source samples, then generates the missing AGENTS.md, docs/architecture.md, docs/product-soul.md, ADR backfill, and seeded docs/memory/ files. Asks the user only about gaps the repo cannot reveal. Load when the user asks to "retroactive project setup", "backfill agent infrastructure", "bootstrap agents for this existing repo", "onboard agents to a legacy codebase", "set up agents without touching code", or "fill in missing agent context for this project".

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Record durable project decisions with rationale, alternatives, assumptions, status, and revisit triggers. Load when the user says record this decision, we decided, decision log, why did we choose, revisit this later, or capture architectural/process rationale.

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Write concise next-agent handoff summaries across sessions, tools, and coding agents. Load when the user says handoff, next agent should know, save context, summarize where we are, switching agents, before ending a meaningful session, or when the user asks to commit, push, commit and push, create a git commit, push to origin, or publish commits — commit/push requests MUST run this skill first to prepare handoff docs, then proceed with git operations.

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Debug LangChain and LangGraph agents by fetching execution traces from LangSmith Studio. Use when debugging agent behavior, investigating errors, analyzing tool calls, checking memory operations, or examining agent performance. Automatically fetches recent traces and analyzes execution patterns. Requires langsmith-fetch CLI installed.

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