280 件 ・ 関連度順
概要と使いどころ
Query openFDA REST API for adverse events (FAERS), labeling, product info, recalls, enforcement. Search by drug name, ingredient, MedDRA, or NDC. 1k req/day no key; 120k with free key. For trials use clinicaltrials-database-search; for structures use drugbank-database-access or chembl-database-bioactivity.
日本語の概要は準備中です。原文の説明を表示しています。
Provides chunking strategies for RAG systems. Generates chunk size recommendations (256-1024 tokens), overlap percentages (10-20%), and semantic boundary detection methods. Validates semantic coherence and evaluates retrieval precision/recall metrics. Use when building retrieval-augmented generation systems, vector databases, or processing large documents.
日本語の概要は準備中です。原文の説明を表示しています。
Searches and navigates stored knowledge in memory palaces. Use when looking for previously stored information or cross-referencing concepts across palaces.
日本語の概要は準備中です。原文の説明を表示しています。
Semantic memory and context - store and retrieve information with embeddings for similarity search. Use for long-term memory, context recall, and knowledge persistence.
日本語の概要は準備中です。原文の説明を表示しています。
Per-project persistent memory: save and recall durable facts across sessions as small frontmatter files, indexed in MEMORY.md.
日本語の概要は準備中です。原文の説明を表示しています。
Semantic + keyword search and connection-discovery across the user's own Apple Notes via the apple-notes MCP server. Use when the user wants to find, recall, or synthesize something from their notes, or surface non-obvious bridges/related notes. macOS, on-device.
日本語の概要は準備中です。原文の説明を表示しています。
Rules for when an agent should recall from long-term memory before acting and when it should save decisions, corrections and failures afterwards. Works with any memory backend.
日本語の概要は準備中です。原文の説明を表示しています。
Implement privacy-preserving record linkage across datasets using Bloom filter encoding, secure hash matching, threshold tuning for precision and recall, and false positive management. Enables entity resolution without exposing raw personally identifiable information between parties.
日本語の概要は準備中です。原文の説明を表示しています。
Learn daily from this machine's Claude Code, Codex, Hermes, OpenClaw and Cursor sessions. Review skill changes and lessons with accept/reject/tbd; recall lessons at session start.
日本語の概要は準備中です。原文の説明を表示しています。
Device-track catalyst analysis: 510(k), PMA, and De Novo decision events, CMS coverage and NTAP milestones, recalls and watch actions as risk signals, and post-market surveillance obligations — dated, verified, and sized per event for any medtech ticker or watchlist.
日本語の概要は準備中です。原文の説明を表示しています。
Provider-independent governance workflow for verifying and correcting human- or model-generated video descriptions. Use for pre-caption critique and post-caption revision, aspect-by-aspect factual review, critique precision/recall/constructiveness, second-stage peer review, calibration, appeals, versioned triplets, provenance, and acceptance reporting; not for pixel/audio QA, accessibility captioning, model training, or creative shot direction.
日本語の概要は準備中です。原文の説明を表示しています。
The only memory skill that watches on its own. No database. No vectors. No manual saves. Just an LLM observer that compresses your conversations into prioritised notes, consolidates when they grow, and recovers anything missed. Five layers of redundancy, zero maintenance. ~$0.00/month (using free-tier models). While other memory skills ask you to remember to remember, this one just pays attention.
日本語の概要は準備中です。原文の説明を表示しています。
Self-hosted semantic memory for AI agents via MCP. Save worklogs, decisions, and notes, then recall them across sessions by meaning, not keyword. Postgres + pgvector with auto-tagging.
日本語の概要は準備中です。原文の説明を表示しています。
Use trace-mcp through bb to search indexed code and recall project decisions with compact context.
日本語の概要は準備中です。原文の説明を表示しています。
音乐制作 / 混音 (音乐制作 / 混音 (Music Production & Mixing) — 录音棚与卧室工作室里把一首歌从编曲/录音/编程做到混音/母带成品的工程+艺术认知操作系统，面向制作人(producer)、混音师(mixing engineer)、母带工程师(mastering engineer)、beatmaker、唱作人/bedroom producer，以及想入行/服务这行(录音棚、plugin 厂商、sample 公司)的视角。覆盖: (a) 第一性张力 — **服务歌曲/情绪 (serve the song / emotion-first: 技术只为打动人，'if it sounds good it is good'，用耳朵不用眼睛) ⇄ 技术正确/工程纪律 (technical correctness: gain staging 增益结构、headroom 余量、相位 phase、监听校准、LUFS 响度规范)**; **黄金耳朵/直觉混音 (mix by ears, reference-driven, 不看分析仪) ⇄ 看表/分析仪/规则 (mix by metrics: EQ 曲线、频谱仪 spectrum analyzer、相关计 correlation meter)**; **少即是多/减法混音 (subtractive, 'less is more', 录好源头 = 混音成功一半) ⇄ 重度处理/'we'll fix it in the mix' (heavy processing, 后期补救)**; **In-the-box 全数字 (ITB: DAW + 插件，召回方便，bedroom 可达专业) ⇄ 模拟硬件/outboard (analog console + 硬件，'mojo'/谐波染色 vs 召回噩梦)**; **人性化/groove/不完美 (humanize, push/pull, 'the feel') ⇄ 量化/网格/修音 (quantize to grid, pitch-correct, 'in the pocket' 靠编辑)**; (b) 核心工作流 / pipeline (最标准、最易蒸高质量+CLI 化) — 编曲制作 arrangement/production → 录音/编程 tracking/programming → 编辑 editing (comping 选段/quantize/pitch-correct/time-align) → 混音 mixing (gain staging → balance 平衡 → EQ → compression → 空间 reverb/delay → 自动化 automation → 总线/母线处理 bus/mix-bus) → 母带 mastering (响度/EQ/限制 limiting/dither/排序+间隔) → 交付 delivery (DDP/streaming spec/stems); (c) 工具栈 — DAW (Pro Tools 录音棚标准/Ableton Live 电子+现场/Logic Pro Mac 性价比/FL Studio beatmaker/Cubase/Studio One/Reaper); 插件 (EQ: FabFilter Pro-Q/SSL/API; 压缩: 1176/LA-2A/SSL bus comp 及其复刻 Waves/UAD/Plugin Alliance; 混响: Valhalla/Lexicon/Bricasti; 调音: Auto-Tune/Melodyne; 限制器 limiter: Pro-L 2/Ozone); 硬件 (监听音箱 monitors: Yamaha NS-10/Genelec; 声学处理 acoustic treatment; 转换器 converters: Apollo/RME; 话筒 mics: U47/SM7B/57); 采样/音色 (Splice/sample packs/Kontakt/Serum 合成器); (d) 知识正典 — Mixing Secrets for the Small Studio (Mike Senior)、The Mixing Engineer's Handbook / Recording Engineer's Handbook (Bobby Owsinski)、Zen and the Art of Mixing (Mixerman)、The Art of Mixing (David Gibson 可视化)、Modern Recording Techniques、Sound on Sound 杂志、Mix With The Masters 大师课、Pensado's Place; (e) figures/流派 — 混音大师 (Chris Lord-Alge CLA 摇滚/Andrew Scheps 'Stadium' 3-bus/Tony Maserati/Manny Marroquin/Serban Ghenea 流行隐形混音/Michael Brauer multing); 制作人 (Rick Rubin 减法/氛围、Quincy Jones、Dr. Dre 完美主义、Max Martin 流行结构、Jack Antonoff、Finneas bedroom→Grammy、Metro Boomin/trap、Timbaland、Pharrell); 母带 (Bob Katz K-system/响度战争批判、Bob Ludwig、Greg Calbi); 教育者 (Mike Senior、Pensado、Warren Huart Produce Like A Pro、Dan Worrall 技术深挖); (f) 行业话术/黑话 — gain staging/headroom/LUFS/true peak/comping/bouncing/printing/stems/DI/reamp/sidechain/parallel compression 并行压缩/mid-side/bus/aux/send-return/dry-wet/transient/sibilance/de-ess/pumping/mud 浑浊/harsh 刺耳/boxy/muddy/in the pocket/the pocket/quantize/swing/groove/vibe/mojo/reference track/A/B/recall/print/glue 胶水; (g) 争议/批判 — 响度战争 loudness war (Bob Katz/Loudness Penalty/streaming 归一化 LUFS 终结了吗)、修音/Auto-Tune 之争 (artistic vs cheating)、ITB vs 模拟玄学 (硬件 'warmth' 是否安慰剂、null test 盲测)、preset/AI 混音 (iZotope/LANDR/sample-based 是否毁了 craft)、bedroom producer 民主化 vs 录音棚消亡、'loudness'/over-compression、plugin 复刻 vs 真硬件、Spotify normalization 对母带的影响、过度依赖 reference/抄袭; (h) 创意 vs 技术张力是本行业 deep tacit 知识的核心 — 大量隐性 '听感' 知识难以言传，靠 reference/学徒/千小时积累。不含: 现场扩声 live sound/PA、音乐理论/作曲 composition (虽相关但聚焦录音棚后期制作)、配乐/film scoring (虽相关但聚焦歌曲制作)、纯硬件电子工程/合成器 DIY、音乐商业/版权/发行 (虽相关但聚焦制作技艺本身)。) Master OS — automated mastery of 音乐制作 / 混音 (Music Production & Mixing) — 录音棚与卧室工作室里把一首歌从编曲/录音/编程做到混音/母带成品的工程+艺术认知操作系统，面向制作人(producer)、混音师(mixing engineer)、母带工程师(mastering engineer)、beatmaker、唱作人/bedroom producer，以及想入行/服务这行(录音棚、plugin 厂商、sample 公司)的视角。覆盖: (a) 第一性张力 — **服务歌曲/情绪 (serve the song / emotion-first: 技术只为打动人，'if it sounds good it is good'，用耳朵不用眼睛) ⇄ 技术正确/工程纪律 (technical correctness: gain staging 增益结构、headroom 余量、相位 phase、监听校准、LUFS 响度规范)**; **黄金耳朵/直觉混音 (mix by ears, reference-driven, 不看分析仪) ⇄ 看表/分析仪/规则 (mix by metrics: EQ 曲线、频谱仪 spectrum analyzer、相关计 correlation meter)**; **少即是多/减法混音 (subtractive, 'less is more', 录好源头 = 混音成功一半) ⇄ 重度处理/'we'll fix it i
日本語の概要は準備中です。原文の説明を表示しています。
Manage persistent memory across sessions. Use when asked to remember, recall, forget, or review what was stored. Covers long-term facts (MEMORY.md), daily session logs, search, and memory housekeeping.
日本語の概要は準備中です。原文の説明を表示しています。
Persistent memory across AI sessions using ChromaDB vector database. Stores and retrieves context from past conversations, decisions, and code. Use when user asks to remember something, search past conversations, recall what was done before, save context for later, or find information from previous sessions. Do NOT use for git history or file-based notes.
日本語の概要は準備中です。原文の説明を表示しています。
Skill to query and update a mistake notebook to memorize mistake. Use this skill when: 1. Encountered a severe problem, needs to recall for historical problem solving experience 2. After fixed a severe mistake with a clear solution valuable to be recorded 3. User requested 'Do not make this mistake again'
日本語の概要は準備中です。原文の説明を表示しています。
AI knowledge engine - a memory system in 6 lines of code. remember/recall/forget/improve loop, vector + graph search, OpenClaw plugin supported.
日本語の概要は準備中です。原文の説明を表示しています。
Use when Codex should remember, recall, search, improve, or forget information using Cognee.
日本語の概要は準備中です。原文の説明を表示しています。
Switch this session to another Cognee dataset. Lists the datasets you can write to, lets you pick one, syncs the current session into its dataset, then starts a fresh Cognee session bound to the chosen dataset. All later saves and recalls target the new dataset and the status line shows it.
日本語の概要は準備中です。原文の説明を表示しています。
Use when Antigravity should remember, recall, search, improve, or forget information using Cognee.
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Retrieve relevant memories for the current task from AgentDB. Use at the start of a task to load prior knowledge, when stuck to surface what worked before, or when the user asks "what do we know about X" / "have we done this before?"
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Close the learning loop — record reward signal for an action AgentDB suggested. Use after using anything from agentdb_pattern_search / reflexion_recall / skill_search / learning_route. The bandit needs the signal to improve.
日本語の概要は準備中です。原文の説明を表示しています。