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Found 776 Skills
Audit installed skills across project, global, and plugin levels. Lists skills with line counts, identifies improvement opportunities (conciseness, clarity, overlap, token waste). Use when reviewing skill quality, finding bloated skills, or optimizing token budgets.
Run application agents through SpendGuard with strict hard budget caps. Use when setting up `spendguard-sidecar`, creating agent IDs, setting or topping budgets, sending OpenAI/Grok/Gemini/Anthropic calls through SpendGuard endpoints, and troubleshooting budget enforcement errors like insufficient budget, in-flight lock conflicts, missing `x-cynsta-agent-id`, or remote pricing signature failures.
Best practices for prompt engineering and context engineering for Coding Agent prompts
Use when synthesizing multiple sources into coherent knowledge bases, performing multi-source analysis, or creating topic expertise from URLs and files. Also use when encountering content integration tasks requiring connections across disparate materials.
Orders scheduler. Reads .noodle/mise.json, writes .noodle/orders-next.json. Schedules work orders based on backlog state, plan phases, session history, and task type schedules.
[WHAT] Universal content intake system for URLs (GitHub repos, YouTube videos, articles, PDFs) and skill packages (skills.sh, skill:// protocol) [HOW] Phase 1: Clone repos/fetch transcripts/scrape content/resolve skills to ~/lev/workshop/intake/. Phase 2-3: Load workshop/intake.md for full analysis [WHEN] Use when user provides a URL to analyze, says "intake/download", wants to evaluate external content, or references a skill package [WHY] Systematically evaluates external content and skill packages for adoption/adaptation with tier classification and ADR creation Triggers: "intake", "download", "analyze this url", "check out this repo", "review this video", "evaluate content", "install skill", "skill://"
Optimize a prompt through a critique-compress pipeline with semantic equivalence verification at each stage. Applies think-critically to improve the prompt, then compress-prompt to reduce it, validating that behavior is preserved after each transformation.
This skill should be used when users want to train or fine-tune language models using TRL (Transformer Reinforcement Learning) on Hugging Face Jobs infrastructure. Covers SFT, DPO, GRPO and reward modeling training methods, plus GGUF conversion for...