1,445 件 ・ 関連度順
概要と使いどころ
Transforms police body camera transcripts into structured 10-section legal summaries with timestamp-cited verbatim statements, constitutional issue spotting, evidence inventory, and probable cause articulation. Maintains strict neutrality for all parties. Use when analyzing BWC footage for discovery, pre-trial prep, or trial. Trigger: body camera, BWC, body worn camera, officer footage, use of force review, incident summary.
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。
Validates and summarizes the four required authorization forms in a personal injury matter (retainer, HIPAA release, insurance authorization, employment record release). Confirms signatures, dates, scope, and completeness to determine readiness for the investigation phase. Use when reviewing client onboarding packets, verifying authorization forms before discovery, or checking HIPAA and record-release compliance.
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。
Drafts U.S. commercial real estate access and indemnity (right-of-entry) agreements for pre-closing due diligence. Covers license grants, non-invasive vs invasive testing gates, insurance/endorsement requirements, indemnity with discovery carve-outs, restoration and lien remedies, confidentiality, and anti-indemnity guardrails. Trigger: access agreement, right of entry, due diligence access, Phase I/II, invasive testing, pre-PSA site inspection.
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。
Deep multi-source technical research with citations — tech evaluation, ecosystem/standards comparison. NOT for API docs (mk:docs-finder), codebase discovery (mk:scout), or project Q&A (mk:ask-me).
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 jira-as. Create/configure-agile need admin. NOT 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。
JIRA-side cache + project-context discovery via jira-as, diagnostic-only, no Atlassian state changes. NOT issue CRUD or project admin (mk:jira-admin).
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 or compare multiple technical approaches for one validated decision. NOT for product discovery or refining an existing plan.
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 a decision needs deep multi-source technical research with cited evidence — technology evaluation, ecosystem comparison, standards/spec fact-finding, "how do others solve X". Triggers on "research X", "deep dive on X", "evaluate X vs Y", "find best practices for X". NOT for library/API doc lookup (see mk:docs-finder); NOT for one-shot URL fetch (see mk:web-to-markdown); NOT for internal codebase discovery (see mk:scout); NOT for project-only Q&A (see mk:ask-me); NOT for comparing solution designs (see mk:brainstorming); NOT for root-cause debugging (see mk:investigate); NOT for wiki ingestion of sources (see mk:wiki-research).
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-side cache + project-context discovery via the jira-as wrapper. Triggers: 'jira cache status', 'clear jira cache', 'discover project context for PROJ'. Diagnostic-only — no Atlassian state changes. NOT for issue CRUD; NOT for project admin (mk:jira-admin).
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 or compare multiple technical approaches for one validated decision. Do not use for product discovery or an existing plan.
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。
Manage Model Context Protocol (MCP) servers - discover, analyze, and execute tools/prompts/resources from configured MCP servers. Use when working with MCP integrations, need to discover available MCP capabilities, filter MCP tools for specific tasks, execute MCP tools programmatically, access MCP prompts/resources, or implement MCP client functionality. Supports intelligent tool selection, multi-server management, and context-efficient capability discovery.
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。
Reddit automation skill collection. Supports authentication, content publishing, search & discovery, social interactions, and compound operations. Triggered when a user asks to operate Reddit (post, search, comment, login, analyze, upvote, 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。
Reddit content discovery skill. Browse subreddits, search posts, view post details, check user profiles. Triggered when user asks to search Reddit, browse subreddits, view posts, or check users.
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。
This skill should be used when the user asks to "create a plugin", "scaffold a plugin", "understand plugin structure", "organize plugin components", "set up plugin.json", "use ${CLAUDE_PLUGIN_ROOT}", "add commands/agents/skills/hooks", "configure auto-discovery", or needs guidance on plugin directory layout, manifest configuration, component organization, file naming conventions, or Claude Code plugin architecture best practices.
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。
OpenLineage, DataHub, Marquez for data lineage tracking and impact analysis. Activate on: data lineage, OpenLineage, DataHub, Marquez, impact analysis, data catalog, column lineage, data discovery. NOT for: data quality validation (use data-quality-guardian), dbt documentation (use dbt-analytics-engineer).
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。
Matches natural language task descriptions to appropriate skills using semantic similarity. Handles fuzzy matching, intent extraction, and capability alignment. Activate on 'find skill', 'match task', 'semantic search', 'skill lookup', 'what skill for'. NOT for ranking matches (use dag-capability-ranker) or skill catalog (use dag-skill-registry).
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。
Predict RBP binding from RNA sequence using deep learning models (RBPNet sequence-to-signal, RNAProt RNN, GraphProt2 GCN with structure, DeepCLIP, DeepRiPe multi-modal CNN) for variant-effect prediction, in silico binding-site discovery, model interpretation, and transfer learning from CLIP and RBNS datasets. Use when computational prediction of RBP binding from sequence is needed, evaluating variant effects on binding without further wet-lab experiments, comparing model performance, or training a custom model on ENCODE eCLIP data.
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。
Predict enhancer-gene regulatory connections from ATAC-seq using ABC, ENCODE-rE2G, HiChIP, or Cicero. Use when linking distal enhancers to target genes, choosing between contact-aware (ABC, ENCODE-rE2G), accessibility-only (Cicero), and orthogonal (HiChIP H3K27ac, EpiMap) approaches, validating predictions against CRISPRi-FlowFISH gold-standard, or building cell-type-specific regulatory maps for fine-mapping or therapeutic target discovery.
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。
Query BindingDB for measured drug-target binding affinities (Ki, Kd, IC50, EC50). Search by target (UniProt ID), compound (SMILES/name), or pathogen. Essential for drug discovery, lead optimization, polypharmacology analysis, and structure-activity relationship (SAR) studies.
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。
Access AlphaFold's 200M+ AI-predicted protein structures. Retrieve structures by UniProt ID, download PDB/mmCIF files, analyze confidence metrics (pLDDT, PAE), for drug discovery and structural biology.
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 Phase 2 — Codebase Scouting and Constraint Discovery. Maps the relevant slice of the codebase for a given task. Produces a scout report at `docs/scout/<slug>.md` naming the files, modules, and patterns the proposed work touches or constrains. Report decisions execute in main context with full conversation visibility; gathering MAY be delegated to read-only advisory subagents (seed.md §4.2-A).
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。