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このリポジトリの skills
SkillsMP は majiayu000/claude-skill-registry から 5,485 件の skill を収集しています。skill を開くとソースと詳細を確認できます。
majiayu000/claude-skill-registry収集済み skill 5,485 件中 40 件を表示しています。
Assist with grading by applying rubrics to student work, generating criterion-level feedback, and maintaining consistency across submissions. Use when grading assignments, applying rubrics, or providing feedback. Activates on "grade this", "apply rubric",…
原文の言語: 英語
Research subject matter, align to educational standards, map prerequisites, and recommend learning theories for curriculum design. Use when starting curriculum development, exploring new topics, or needing standards alignment. Activates on "research this…
原文の言語: 英語
Generate a concise, executive-ready digest of internal touchpoints and external intelligence for a named customer.
原文の言語: 英語
Analyze contractor bids against CWICR benchmarks. Identify pricing anomalies, compare bid components, and support bid evaluation decisions.
原文の言語: 英語
Track and analyze historical cost data using CWICR. Compare actual vs estimated costs, build project cost database, and improve future estimates.
原文の言語: 英語
Classify problems into Cynefin Framework domains (Clear, Complicated, Complex, Chaotic, Confusion) and recommend appropriate response strategies. Use when unsure how to approach a problem, facing analysis paralysis, or needing to choose between expert…
原文の言語: 英語
Classify problems into Cynefin Framework domains (Clear, Complicated, Complex, Chaotic, Confusion) and recommend appropriate response strategies. Use when unsure how to approach a problem, facing analysis paralysis, or needing to choose between expert…
原文の言語: 英語
Use when facing a new problem to classify its domain (Clear, Complicated, Complex, Chaotic, Confused) and select appropriate methods.
原文の言語: 英語
Use when facing a new problem to classify its domain (Clear, Complicated, Complex, Chaotic, Confused) and select appropriate methods.
原文の言語: 英語
Inspect code and propose root cause hypotheses. Creates ./.gtd/debug/current/HYPOTHESES.md
原文の言語: 英語
Learns from DAG execution history to improve future performance. Identifies successful patterns, detects anti-patterns, and provides recommendations. Activate on 'learn patterns', 'execution patterns', 'what worked', 'optimize based on history', 'pattern…
原文の言語: 英語
Query DARPA programs, opportunities, and BAAs. Searches RSS feeds and Grants.gov API for defense research funding opportunities across all DARPA technical offices.
原文の言語: 英語
Decision-first data analysis with statistical rigor gates.
原文の言語: 英語
AI-powered data analysis for Empathy Ledger - themes, quotes, story suggestions, transcript analysis.
原文の言語: 英語
Executive-grade data analysis with pandas/polars and McKinsey-quality visualizations. Use when analyzing data, building dashboards, creating investor presentations, or calculating SaaS metrics.
原文の言語: 英語
Fact-checking and source verification workflow for research documents. Launches parallel fact-checkers, aggregates findings, applies corrections systematically.
原文の言語: 英語
Generates a Mermaid sequence diagram showing how data moves between components. Use when tracing request flows or documenting data transformation pipelines.
原文の言語: 英語
Detect split data source anti-patterns and scattered business rule duplication where the same logic is reimplemented across multiple files and languages. Catches semantic duplication that syntactic tools like jscpd miss. Use at phase checkpoints or when…
原文の言語: 英語
Data journalism workflows for analysis, visualization, and storytelling. Use when analyzing datasets, creating charts and maps, cleaning messy data, calculating statistics or building data-driven stories. Essential for reporters, newsrooms and researchers…
原文の言語: 英語
Extract patterns and insights from large datasets using association rules, clustering, classification, and anomaly detection. Use for market basket analysis, customer segmentation, fraud detection, pattern discovery, predictive modeling, and knowledge…
原文の言語: 英語
Generate comprehensive data profiles for DataFrames. Use for EDA, data discovery, and understanding dataset characteristics.
原文の言語: 英語
Apply DE2 Factorization to separate multiplicative components to understand relative contribution of each factor.
原文の言語: 英語
Structured multi-perspective debate for important architectural decisions and complex trade-offs
原文の言語: 英語
Personal decision advisor for QUALITY over speed. Exhaustive discovery, option finding, sequential elimination, structured analysis. Use for investments, purchases, career, life decisions. Surfaces hidden factors, tracks eliminations with reasons, confident…
原文の言語: 英語
Structured decision critic that systematically stress-tests reasoning before commitment surfacing hidden assumptions verifying claims and generating adversarial perspectives to improve decision quality.
原文の言語: 英語
Triggers for automatically invoking the decision-framework skill before irreversible or high-blast-radius operations. Use as a checkpoint: when a proposed action matches a trigger pattern, pause and run the appropriate framework (first-principles, inversion,…
原文の言語: 英語
Use when choosing between mutually exclusive strategic paths or building a repeatable decision process—constructs Decision Matrices with probability estimates and runs Pre-Mortems.
原文の言語: 英語
Create or update an audit of recent decisions for quality
原文の言語: 英語
Facilitate data-driven technical decisions using embedded decision matrices and trade-off analysis. Use when relevant to the task.
原文の言語: 英語
Scores and strengthens startup pitch decks (pre-seed through Series A) against 35 investor-grade criteria grounded in Sequoia, DocSend, YC, a16z, and Carta data.
原文の言語: 英語
Apply DE3 decomposition to partition a problem into modular subproblems.
原文の言語: 英語
Investigate a game from raw logs, trace bugs to source code, and file detailed issues. Use for deeper debugging than export-only analysis.
原文の言語: 英語
Deep codebase exploration and synthesis using parallel task agents with dynamic planning. Use when asked for "deep analysis", "analyze codebase", "explore and analyze", "investigate codebase", or "deep understanding".
原文の言語: 英語
Deep exploration and synthesis workflow using dynamic planning and hub-and-spoke coordination. Use when asked for "deep analysis", "deep understanding", "analyze codebase", "explore and analyze", or "investigate codebase".
原文の言語: 英語
Reusable deep exploration and synthesis workflow. Use when asked for "deep analysis", "deep understanding", "analyze codebase", "explore and analyze", or "investigate codebase".
原文の言語: 英語
Deep codebase analysis that extracts learnings from documentation. Creates context entries and learnings from docs/ with pattern matching.
原文の言語: 英語
MUST CHECK FIRST before DeepWiki or any external research. Local-first codebase documentation wikis. **Priority order for library/framework research**: 1. deep-codebase-wiki (THIS) - check local wikis FIRST 2. DeepWiki - only if no local wiki exists 3.…
原文の言語: 英語
Three-stage Chain of Thought (CoT) analyzer for /deep-reasoning page. Performs comprehensive analysis in 3 stages Direct Impact → Secondary Effects → Final Conclusion for complex news articles. Optimized for depth over speed.
原文の言語: 韓国語
Analyzes development sessions to extract patterns, preferences, and learnings. Use after significant work sessions to capture insights, document system understanding, and improve future interactions.
原文の言語: 英語
In-depth research on technical topics using web search, documentation, and codebase analysis. Use when asked to 'research', 'investigate', 'find out about', or explore unfamiliar technologies.
原文の言語: 英語