「traction」の検索結果

Guide a person through a meditation session from preparation through closing. AI coaches posture selection, breath anchoring, working with distractions, shamatha (calm abiding), vipassana (insight), and session integration. Use when a person wants to begin or deepen a meditation practice, when someone needs to prepare their mind for focused work, when grounding is needed before or after energy healing work, when managing stress or anxiety, or for integration after intense experiences.

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Alluvial gold recovery using panning, sluicing, and classification. Covers site reading (geological indicators, stream dynamics, pay streak location), panning technique, sluice box operation, and responsible extraction practices. Use when prospecting in an area with known or suspected alluvial gold deposits, sampling a stream to test for gold presence, maximizing recovery during recreational panning, or assessing a site's gold potential before investing more effort.

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Perform controlled dismantling of rigid system structures while preserving essential capabilities (imaginal discs). Covers rigidity mapping, dissolution sequencing, knowledge extraction, interface archaeology, and safe decomposition of technical debt and organizational calcification. Use when assess-form classified the system as PREPARE or CRITICAL, when a system is so calcified that incremental change is impossible, when technical debt blocks all forward progress, or before adapt-architecture when the current form must be softened before it can be reshaped.

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Extract maximum value from existing code — performance optimization, API surface refinement, and dead weight elimination. The art of turning base code into gold through systematic identification and amplification of value-bearing patterns. Use when optimizing a working but sluggish codebase, refining an API surface that has accumulated cruft, reducing bundle size or memory footprint, or preparing code for open-source release — when code works correctly but doesn't shine and needs polish rather than a full rewrite.

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Foundation obedience training for dogs — sit, stay, come, heel, and down using positive reinforcement and marker training. Covers timing, reward hierarchy, session structure, distraction proofing, and common handler errors. Use when a new puppy (8+ weeks) is ready for foundation training, an adult dog lacks reliable basic commands, a rescue dog needs to learn the household's command vocabulary, before advancing to complex behaviors or off-leash work, or when existing commands have degraded and need re-establishing.

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Use when the user asks to simplify, clean, or refine code. Not for new abstractions or whole-codebase refactors.

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。

Fix silent failures when parsing Grafema semantic IDs that are in URI format instead of legacy arrow format. Use when: (1) code splits semantic IDs by "->" but gets the whole string back because IDs are grafema:// URIs, (2) file path extraction from semantic IDs returns empty or wrong values, (3) derived edges (DEPENDS_ON, etc.) produce 0 results despite source edges existing, (4) any code that processes semantic IDs after the analysis pipeline's to_uri_format() has run. The grafema:// URI format uses # fragments with percent-encoded characters instead of -> separators.

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Fires after fixing any non-trivial bug or regression. Asks: could the graph have caught this as a guarantee? Pairs with reflection-in-and-on-action — picks up after "earliest catchable signal" and asks the next question: was that signal expressible in graph? Triggers: (1) after any non-trivial bug fix is verified working, (2) after a regression report (something used to work, broke), (3) during step 6 (knowledge extraction) of the workflow, (4) when reflection-on-action surfaces a "would have been catchable" signal. Outcome is a triage decision (graph-reachable? rule expressible? rule sound?) and either a draft Linear issue + guarantee proposal, or a recorded "graph capability gap" note. Never auto-creates guarantees.

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End-to-end system for creating supervised fine-tuning datasets from books and training style-transfer models. Covers text extraction, intelligent segmentation, synthetic instruction generation, Tinker-compatible output, LoRA training, and validation.

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Reverse-engineer any person's writing voice from their content. Paste in posts, articles, tweets, or essays and this skill launches 10 parallel Sonnet subagents to analyze every dimension of the voice, then synthesizes into a voice file. Use when someone says 'match this voice,' 'analyze this writing,' 'extract their voice,' 'make me sound like this,' 'study this person's writing,' or pastes in content they want to learn from.

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Web scraping, search, crawling, and browser automation via the Firecrawl CLI. Use this skill whenever the user wants to search the web, find articles, research a topic, look something up online, scrape a webpage, grab content from a URL, extract data from a website, crawl documentation, download a site, or interact with pages that need clicks or logins. Also use when they say "fetch this page", "pull the content from", "get the page at https://", or reference scraping external websites. This provides real-time web search with full page content extraction and cloud browser automation — capabilities beyond what Claude can do natively with built-in tools. Do NOT trigger for local file operations, git commands, deployments, or code editing tasks.

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Call Exa Search (POST /search) for semantic web retrieval with ranked results, filters, freshness, highlights/text, structured output, or streaming. Use when the agent needs open-ended web search, competitor discovery, news, people, or research papers and does not already have URLs. Prefer Exa MCP web_search_exa / web_search_advanced_exa when available; otherwise raw HTTP with EXA_API_KEY. NOT for known-URL extraction (use exa-contents or firecrawl) or multi-step list-building (use company-research / lead-generation / Agent API).

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Call Exa Contents (POST /contents) for LLM-ready extraction from known URLs: text, highlights, summaries, links, image links, subpages, freshness-controlled crawl. Use when the agent already has URLs. Prefer Exa MCP web_fetch_exa when available; otherwise raw HTTP with EXA_API_KEY. NOT for open-ended discovery (use exa-search) or auth-walled X/Twitter (use mktg-x). For full-site crawl of a known domain, firecrawl may be better.

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Director-level AI art prompt creation for Eastern beauty series, including Eastern fantasy / gu feng Chinese beauty character art, classical Eastern beauty, realistic and modern Eastern beauty, Cosplay展会摄影 / official convention cosplay photography with AAA game character campaign and Vogue editorial quality, 古风闺蜜 / AncientFemaleCompanionship two-person ancient Chinese female companionship photography, 东方美学图鉴 / 小红书图鉴 cover and content systems for 四大美人、四大才女、十二花神、东方神女、敦煌飞天、朝代服饰、东方器物、东方神话, 甜系纯欲生活写真 / SweetHomeGirl lifestyle portraits built around realism, feminine charm, romantic feeling, story moment, and natural attraction, new-Chinese-style fashion editorials, magazine portraits, worldbuilding, mythic role design, costume/styling systems, cinematic composition, lighting, negative prompts, and model-specific prompt variants. Use when the user asks for 东方美人, 东方审美, 东方美学图鉴, 小红书图鉴, 图鉴封面, 图鉴正文, 四大美人, 四大才女, 十二花神, 东方神女, 敦煌飞天, 朝代服饰, 东方器物, 东方神话, Cosplay展会摄影, coser, Coser, 漫展摄影, 展会摄影, ChinaJoy, Bilibili World, Tokyo Game Show, 游戏展台, 官方展会摄影, AAA游戏角色Cosplay, 古风闺蜜, AncientFemaleCompanionship, Ancient Female Companionship, 闺阁夜话, 姐妹古风写真, 古代闺阁生活, 灯下梳发, 共读, 夏夜纳凉, 守夜, 江南同行, 湖亭共伞, 山寺同行, 灯会夜游, 柔美, 妩媚, 成熟, 优雅, 高贵, 清冷, 温婉, 英气, 神性, 慵懒, 东方幻想, 古风美女, 古典东方美人, 宋韵, 唐宫, 江南, 洛神, 青瓷, 昆曲, 国风人物, 汉服女子, 写实东方美人, 现代东方审美, 新中式, 东方高级感, SweetHomeGirl, 甜系纯欲生活写真, 甜系纯欲, 甜美女友感, 真实女生, 自然抓拍, 故事感, 女友视角, lifestyle girlfriend portrait, 仙侠/武侠/宫廷/江南/敦煌风 AI 绘画提示词, image prompt optimization, style word expansion, character series concepts, or prompt packs for Midjourney, Stable Diffusion, ComfyUI, DALL-E, Gemini, Seedream, or other image generation tools.

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当用户需要找出产品/服务的独特竞争优势来写定位文案或广告主轴时调用。 适用于：产品同质化严重不知怎么差异化、新产品定位、重新审视现有产品的竞争优势。 核心信号：用户在问"我的产品和别人有什么不同""怎么让客户选我而不是对手"。 不适用于：已有明确USP只需要执行、不需要竞争分析的产品（如垄断市场）。

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。

Invoke only when the user explicitly asks to review code through the "single level of abstraction / layered error handling" lens — a function does only its own layer's business logic while errors are handled above or below. The agent reports detections, raw-count measurements, and move directions. Apply only when the user explicitly requests this lens.

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。

Design document processing pipelines for extraction and routing. TRIGGERS - Use when user needs help with ai-document-processor related tasks.

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。

Build entity extraction systems for structured data from text. TRIGGERS - Use when user needs help with ai-entity-extractor related tasks.

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。

Evaluate architecture and module boundaries for volatility-based decomposition using Juval Lowy's framework (from "Righting Software", building on Parnas 1972). Use when reviewing module splits, service boundaries, new abstractions, or any decomposition decision. Trigger on phrases like "where should this boundary be", "how to split this", "module boundaries", "encapsulate change", "volatility", or references to Lowy, Parnas, or "Righting Software". Complements /hickey (interleaved concerns) with a different lens (change encapsulation).

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小凯 · 抖音视频提取工程师。当被主理人·阿爆调度执行 Phase 1 视频获取任务时激活。 负责接收抖音链接，通过三层降级策略获取无水印视频，提取音频并通过硅基流动 语音识别获取完整口播文案。 不直接面向用户，只响应主理人的调度指令。 Use when: dispatched by team-lead for video extraction subtask.

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。

Bulk extract content from an entire website or site section. Use this skill when the user wants to crawl a site, extract all pages from a docs section, bulk-scrape multiple pages following links, or says "crawl", "get all the pages", "extract everything under /docs", "bulk extract", or needs content from many pages on the same site. Handles depth limits, path filtering, and concurrent extraction.

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。

Web search with full page content extraction. Use this skill whenever the user asks to search the web, find articles, research a topic, look something up, find recent news, discover sources, or says "search for", "find me", "look up", "what are people saying about", or "find articles about". Returns real search results with optional full-page markdown — not just snippets. Provides capabilities beyond default agent web search tools.

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。

Extract clean markdown from any URL, including JavaScript-rendered SPAs. Use this skill whenever the user provides a URL and wants its content, says "scrape", "grab", "fetch", "pull", "get the page", "extract from this URL", or "read this webpage". Handles JS-rendered pages, multiple concurrent URLs, and returns LLM-optimized markdown. Use this instead of WebFetch for any webpage content extraction.

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。

Peer review checklist and quality standards for intelligence products, plus schema validation for graded evidence items and claim tables from /quality-of-information-check and /claim-extraction. Rejects outputs that break the evidence schema or the grading caps. Loaded by the quality-reviewer agent.

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。