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Found 51 Skills
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Active diagnostic tool for analyzing skill prompts to identify token waste, anti-patterns, trigger issues, and optimization opportunities. Use when reviewing skill prompts, debugging why skills aren't triggering, optimizing token usage, or preparing skills for publication. Provides specific, actionable suggestions with examples.
Create, optimize, update, and validate AGENTS.md files with maximum token efficiency. Use when the user asks to (1) create new AGENTS.md files for any repository, (2) optimize/condense existing AGENTS.md to reduce token count, (3) update/refresh AGENTS.md to sync with codebase changes, (4) validate AGENTS.md quality and completeness, or (5) improve AGENTS.md files to be more effective for AI agents. Always generates token-efficient, condensed output focused on actionable commands and patterns while maintaining model-agnostic language.
Reviews and grades an agent skill directory (SKILL.md plus supporting resources) for specification compliance, clarity, token efficiency, safety, robustness, and portability. Use when a user wants a rubric-based critique with a weighted score/grade and concrete, minimal patch suggestions.
Smart ClawdBot documentation access with local search index, cached snippets, and on-demand fetch. Token-efficient and freshness-aware.
You are an expert prompt engineer specializing in crafting effective prompts for LLMs through advanced techniques including constitutional AI, chain-of-thought reasoning, and model-specific optimizati
Use when writing instructions that guide Claude behavior - skills, CLAUDE.md files, agent prompts, system prompts. Covers token efficiency, compliance techniques, and discovery optimization.
Writing conventions for scannable, token-efficient skills and prompts. Use when creating or reviewing SKILL.md files, AGENTS.md files, or any markdown-based agent instruction documents.
Turn MCP, OpenAPI, or GraphQL servers into CLIs at runtime with zero codegen, saving 96-99% of tokens on tool schemas
Optimizer that refines and professionalizes AI agent skills through real usage — saves tokens, eliminates redundancy, and tightens instructions so skills cost less to run. Learns from mistakes, reviews quality, and improves over time. Observes skill execution in the current conversation, analyzes up to four sources (conversation friction, file diffs, user feedback, static diagnostic) plus accumulated lessons, and proposes concrete improvements to the target skill's SKILL.md. Works with Claude Code and compatible SKILL.md-based agent frameworks. Use after executing any skill: `/skill-optimizer [name]` or `/skill-optimizer` to auto-detect. `--review` processes accumulated lessons.
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.
Optimize agent skills to reduce context bloat while preserving answer coverage. Use when: (1) A skill's SKILL.md body exceeds ~250 lines or duplicates its references/ files (2) A skill's YAML description is verbose or triggers false positives from sibling skills (3) Planning or executing a body/reference split for a skill (4) Auditing skill token efficiency