Loading...
Loading...
Found 2,691 Skills
Implements the Chain of Responsibility pattern in Python. Use when the user mentions chain of responsibility, CoR, or when you need to chain handlers that each process and pass to the next—validation pipelines, processing steps, transformation chains, or any sequential pipeline.
Create operational runbooks and standard operating procedures. Document troubleshooting guides and recovery procedures. Use when documenting operational knowledge.
Optimizes markdown documents for token efficiency, clarity, and LLM consumption. Use when (1) a markdown file needs streamlining for use as LLM context, (2) reducing token count in documentation without losing meaning, (3) converting verbose docs into concise reference material, (4) improving structure and scannability of markdown files, or (5) preparing best-practices or knowledge docs for agent consumption.
Iteratively improve skill frontmatter compliance using the Ralph loop pattern. USE FOR: run sensei, sensei help, improve skill, fix frontmatter, skill compliance, frontmatter audit, improve triggers, add anti-triggers, batch skill improvement, check skill tokens. DO NOT USE FOR: creating new skills (use skill-authoring), writing skill content, token optimization only (use markdown-token-optimizer), or non-frontmatter changes.
Use when working with Infrastructure as Code tools and platforms. Covers Terraform, Pulumi, CloudFormation, Bicep, ARM, Kubernetes, Helm, Docker, Crossplane, and Dagger. USE FOR: choosing IaC tools, comparing Terraform vs Pulumi vs CloudFormation, infrastructure strategy DO NOT USE FOR: specific tool syntax (use the sub-skills: terraform, pulumi, bicep, etc.)
Analyzes markdown files for token efficiency. TRIGGERS: optimize markdown, reduce tokens, token count, token bloat, too many tokens, make concise, shrink file, file too large, optimize for AI, token efficiency, verbose markdown, reduce file size
A specialized skill for handling complex object comparison and deep validation. Use when you need to compare deep objects, exclude specific properties, handle circular references, or validate DTO/Entity. Covers BeEquivalentTo, Excluding, Including, custom comparison rules, etc. Keywords: object comparison, deep comparison, BeEquivalentTo, DTO comparison, Entity validation, property exclusion, circular reference, Excluding, Including, ExcludingNestedObjects, RespectingRuntimeTypes, WithStrictOrdering, ignore timestamp, exclude timestamp
Sequential subagent execution with two-stage review gates for implementation plans. Use when executing multi-task plans in current session, when tasks need fresh subagent context to avoid pollution, when formal review cycles (spec compliance then code quality) are required between tasks, or when you need diff-based validation of each task before proceeding.
Collaborative design exploration that refines ideas into validated specs through iterative questioning. Use before any creative work including creating features, building components, adding functionality, or modifying behavior.
Use when working with AI agent protocols, standards, and interoperability specifications. Covers MCP, A2A, ACP, Agent Skills, AGENTS.md, ADL, x402, AP2, MCP Apps, and cagent. USE FOR: agent protocol selection, comparing MCP vs A2A vs ACP, understanding agent standards ecosystem, choosing payment protocols DO NOT USE FOR: specific protocol implementation details (use the sub-skills: mcp, a2a, acp, x402, etc.)
Data visualization for Python: Matplotlib, Seaborn, Plotly, Altair, hvPlot/HoloViz, and Bokeh. Use when creating exploratory charts, interactive dashboards, publication-quality figures, or choosing the right library for your data and audience.
Use this when writing Python argparse CLI scripts that require colorful --help output with customizable color control, including support for NO_COLOR and FORCE_COLOR environment variables.