「discover」の検索結果

Drafts code-compliant California responses to Requests for Production (CCP § 2031.010 et seq.) for Superior Court. Use when the user mentions California RFP responses, CCP 2031, discovery objections, compliance statements, inability to comply, privilege objections, Bates numbering, verification requirements, or responding to document requests in California state court. California Superior Court only — not for federal court without substantial modification.

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。

Generate llms.txt files from project documentation following the llmstxt.org spec. Use when asked to create AI-friendly documentation indexes, generate llms.txt, or make a project discoverable by AI assistants.

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。

JIRA custom field discovery + configuration via the jira-as wrapper. Triggers: 'field ID for X', 'list custom fields', 'check fields for project PROJ', 'configure agile fields for board'. Read ops are open; create/configure-agile require admin. NOT for setting per-issue field values (mk:jira-issue update).

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。

Target a specific Google Chrome user profile for browser automation via CDP. Discovers installed profiles from the Chrome Local State file, launches Chrome with the requested profile attached to a debug port, and connects agent-browser via `agent-browser connect <port>`. Use when "automate using my Work/Personal Chrome", "open site with my saved session", or "use Chrome profile named <name>". NOT for headless scraping without an existing user profile (use mk:agent-browser directly); NOT for importing cookies only (use agent-browser --auto-connect state save).

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 and review durable REST, GraphQL, RPC, or event-facing API contracts. Use for public or shared API contracts, endpoint/schema consistency, compatibility, pagination, error semantics, and API documentation. Discovers existing consumers and conventions before recommending a style.

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。

Workflow navigation assistant. Scans project state (plans, reviews, tests, git) and recommends the next step in the 7-phase pipeline. Use when asked "what should I do next?", "where am I?", "help", or at session start. NOT for domain complexity routing (see mk:scale-routing); NOT for skill discovery (descriptions handle that automatically).

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。

Central catalog of available skills with metadata, capabilities, and performance history. Provides skill discovery and lookup services. Activate on 'skill registry', 'list skills', 'skill catalog', 'available skills', 'skill metadata'. NOT for matching skills to tasks (use dag-semantic-matcher) or ranking (use dag-capability-ranker).

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。

Trains and applies base-resolution deep learning models on ChIP-seq / ChIP-nexus / CUT&RUN data. Uses BPNet (Avsec 2021 Nat Genet 53:354; soft motif syntax from ChIP-nexus), chromBPNet (Pampari A et al 2024 bioRxiv; bias-factorized base-resolution profiles), EnFormer (Avsec 2021 Nat Methods 18:1196; 196 kb input, ~100 kb effective receptive field), DeepSEA (Zhou 2015; multi-task CNN), and JASPAR 2026 deep-learning collection (1259 BPNet ChIP models). Performs in silico mutagenesis for variant-effect prediction, DeepLIFT/Grad attribution, and TF-MoDISco motif discovery from attribution scores. Use when predicting variant effects on TF binding, discovering soft motif syntax / cooperativity, integrating ChIP-seq with sequence-only predictions, or applying precomputed JASPAR Deep Learning models to new variants.

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。

Find web sources with query-relevant page excerpts and optional full-page content, and discover workflows, data APIs, and indexes. Use for web research or finding structured records, listings, transcripts, and datasets. Supports semantic tool discovery, domain matching, and progressive catalogue browsing.

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。

Discover prospect companies from a structured OpenClaw search brief, including public search and user-authorized data sources, then output a conservative candidate list ready for lead screening.

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 original reddapi.dev Reddit search skill (vector search, semantic search, trends, subreddit discovery), no Reddit OAuth or app registration needed. This is the same engine now packaged as reddit-research with added market-research playbooks and a fuller pitch on semantic vs keyword search; reddapi is kept live under its original name for existing installs and works standalone. Use when the user says 'reddapi' by name, or wants a minimal drop-in Reddit search skill without the extra research-workflow guidance. For the expanded research-oriented version with query playbooks, see reddit-research. For B2B lead scoring, see reddit-leads. For a bare API reference, see reddit-search-api.

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。

Discover B2B leads from Reddit using AI-powered lead scoring via reddapi.dev Leads API. Finds high-intent signals, scores them 0-100, and classifies by lead type (pain_point, solution_request, complaint, feature_request, comparison). Perfect for competitor poaching, pain point discovery, and sales prospecting.

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。

Raw discovery primitives — project discovery, version resolution, docs loading, architecture mapping, execution flow. Called by `universal-project-analysis`. Single-pass scan → `project-analyzer`.

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。

When running discovery interviews — question-bank build, bias audit, insight extraction. Triggers on 'audit my guide', 'extract insights from transcript', 'is my hypothesis falsifiable'.

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, content extraction, crawling, and deep research via the Tavily CLI. Use this skill whenever the user wants to search the web, find articles, research a topic, look something up online, extract content from a URL, grab text from a webpage, crawl documentation, download a site's pages, discover URLs on a domain, or conduct in-depth research with citations. Also use when they say "fetch this page", "pull the content from", "get the page at https://", "find me articles about", or reference extracting data from external websites. This provides LLM-optimized web search, content extraction, site crawling, URL discovery, and AI-powered deep research — capabilities beyond what agents can do natively. Do NOT trigger for local file operations, git commands, deployments, or code editing 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。

Discover ordinary public webpages with LLM-optimized Tavily search results. Use when no specific URL is known and the user needs a quick public-web lookup, articles, recent news, sources, or domain-filtered results. This is the default Tavily leaf for simple public-web discovery. Do not use when the user already supplied the target URL (use tavily-extract), wants URLs under one known site (tavily-map), wants many pages' content (tavily-crawl), needs a full cited synthesis (tavily-research), or needs named platforms, login state, platform-native content, or cross-platform collection (Agent Reach).

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。

Produce comprehensive synthesis with explicit citations from multiple ordinary public-web sources via the Tavily CLI. Use for detailed reports, comparisons, market or literature research, and questions whose answer requires multi-source web evidence rather than a quick lookup. Do not use for simple discovery (tavily-search), one known URL (tavily-extract), one-site URL discovery or bulk extraction (tavily-map/crawl), or research centered on named social, community, career, code, media, authenticated, or cross-platform sources (Agent Reach). Takes 30-120 seconds.

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。

Phase 2 of disease-market-sizing-orchestration. Discovers disease-specific stratification dimensions by citing guideline sections from sources/guidelines/. ALWAYS uses cite-based discovery — NEVER built-in stratification templates. Output: .cache/<slug>/staging.json. Use AFTER contract-elicitor and AFTER A1' guidelines fetch.

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。

Install, discover, and wire registry blocks and components into HyperFrames compositions. Use when running hyperframes add or hyperframes catalog, installing one item or every block matching a tag, wiring an installed item into index.html, or working with hyperframes.json. Covers discovery, install locations, block sub-composition wiring, component snippet merging, and authoring a new block or component to contribute upstream (idea → scaffold → validate → PR).

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。

AID Phase 0 - Research & discovery. Use for validating problem spaces, identifying stakeholders, defining success metrics, deciding whether to proceed.

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。

プラグイン構想から index+13 フェーズファイル+component-inventory.json とタスク粒度の型付き依存グラフ (task-graph) を第3の射影として生成したいとき、後段のrun-skill-createへ渡す前段の開発計画を立て build 中の discovered-task を --mode update の外ループで graph へ還流したいときに使う。

Discover, compare, and compose existing open-source GitHub repositories for a user's goal, using GitHub MCP for live discovery and verification plus Context7 semantic retrieval over repository code and official documentation. Use when a user asks for GitHub repo recommendations, open-source alternatives, a multi-repository solution stack, project comparison, or help choosing an implementation to adopt.

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-neutral planner for synchronizing MCP configuration, skills and rule files across agent providers and app classes. It discovers evidenced local options and lets the user choose truth, targets, direction and conflicts.

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-neutral entry point for discovering, planning and synchronizing MCP configuration between user-selected providers and app classes. The user selects truth, targets and scope; no provider is an implicit hub.

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。