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Found 51 Skills
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.
Token-efficient model routing modifier
Get code context from repositories with examples and documentation. Use when you need code snippets, implementation examples, API usage patterns, or technical documentation for programming concepts, frameworks, or libraries.
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.
Token-efficient codebase exploration using RepoPrompt CLI. Use when user says "use rp to..." or "use repoprompt to..." followed by explore, find, understand, search, or similar actions.
Use when users say "create a skill", "make a new skill", "build a skill", "skill for X", "package this as a skill", or when refactoring/updating/auditing existing skills that extend agent capabilities with specialized knowledge, workflows, or tool integrations.
Bulk grading workflows for Canvas LMS assignments using rubrics. Covers single grading, batch grading, and code execution strategies with safety-first dry runs.
Compress documents for LLM token efficiency while preserving semantic content. Use when asked to compress, compact, shrink, or optimize a document, CLAUDE.md, system prompt, skill file, or any text for fewer tokens. Also use when the user mentions token count, token budget, context window limits, or wants to make prompts shorter for cost savings.
For the creation, review, refactoring, and presentation of .ipynb Notebooks (Jupyter / JupyterLab / Google Colab / VS Code). Covers engineered directory structures, efficient token processing, demonstration/sharing patterns, and reproducible workflows with uv/venv.
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.
Context window coach. Proactive guidance for token-efficient Claude Code projects, multi-agent systems, and skill architecture.
Execute Python code in isolated rootless containers with MCP server proxying for token-efficient agent workflows