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AI Skill Security Audit

sickn33/antigravity-awesome-skills

Performs non-intrusive static analysis across Windows, macOS, Linux/Unix, and mobile platforms to spot malicious patterns, data leaks, and obfuscated payloads in AI skills and bundles, then reports findings with scoring and mitigation guidance.

OpenVLA-OFT Robotics Tuning

fine-tuning-openvla-oft

Orchestra-Research/AI-Research-SKILLs

Guides fine-tuning and evaluation of OpenVLA-OFT/OFT+ for robot action generation, covering LoRA adaptation, continuous action heads, FiLM conditioning, and LIBERO/ALOHA benchmarks plus deployment considerations.

LLM Cost Optimizer

llm-cost-optimizer

alirezarezvani/claude-skills

LLM Cost Optimizer audits and redesigns LLM pipelines to cut API spend 40-80% without quality loss by routing workloads, caching prompts, controlling lengths, and monitoring token costs.

DeerFlow Agent Bridge

claude-to-deerflow

bytedance/deer-flow

Provides command-line recipes for hitting DeerFlow HTTP endpoints to start threads, stream runs, manage memory/models/skills/agents, and upload files for in-depth research tasks.

Arize Trace Export Tool

github/awesome-copilot

Downloads spans, traces, and sessions from Arize via the ax CLI to debug LLM applications, using IDs, filters, sampling rules, and bulk/export safeguards while writing output to .arize-tmp-traces.

AI Model Recommender

model-recommendation

github/awesome-copilot

Reads `.agent.md` or `.prompt.md` files, assesses task complexity, tooling, and requirements, then recommends the most cost-efficient GitHub Copilot model(s) (e.g., GPT-5, Claude Sonnet) with rationale for users across different subscription tiers and priorities.

Napkin Visual Whiteboard

github/awesome-copilot

Napkin gives Copilot CLI users a browser whiteboard to draw, sketch, and add sticky notes; Copilot reads snapshots and JSON data, then replies with analysis, suggestions, and next steps for business-oriented thinkers.

Phoenix Evaluators Toolkit

github/awesome-copilot

Phoenix Evals lets teams build evaluators for AI/LLM apps by wiring deterministic code with LLM nuance, running Python or TypeScript workloads, validating against humans, and iterating through error analysis, RAG, and production workflows.

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