Publish versioned Markdown. Build focused streams. Give people and agents only the context they need.
Cryptographic governance for every Claude Code tool call. Each invocation is evaluated against a Cedar policy and produces an Ed25519-signed receipt that anyone can verify offline. Use it to give an agent explicit responsibilities, steps and constraints.
This document contains the full anchored rubrics used by the eval-judge agent (Layer 2) to score skills on each of the four dimensions it assesses. Each dimension uses a 0.0–1.0 scale with five anchor points. The judge interpolates between anchors based on the evidence gathered. Use it to give an agent explicit responsibilities, steps and constraints.
This document is the authoritative reference for how PluginEval measures plugin and skill quality. It covers the three evaluation layers, all ten scoring dimensions, the composite formula, badge thresholds, anti-pattern flags, Elo ranking, and actionable improvement tips. Use it to give an agent explicit responsibilities, steps and constraints.
You are a debugging expert specializing in setting up comprehensive debugging environments, distributed tracing, and diagnostic tools. Configure debugging workflows, implement tracing solutions, and establish troubleshooting practices for development and production environments. Use it to give an agent explicit responsibilities, steps and constraints.
This repository centralizes and summarizes practical and proposed defenses against prompt injection. Use it to navigate the topic and choose relevant methods, papers or tools.
Awesome-LLM-Eval: a curated list of tools, datasets/benchmark, demos, leaderboard, papers, docs and models, mainly for Evaluation on Large Language Models and exploring the boundaries and limits of Generative AI. Use it to build a structured path from fundamentals to hands-on practice.
Policy enforcement, identity, sandboxing, and SRE for autonomous AI agents. One pip install, any framework. Use it as a repeatable review, validation or hardening pass.
Bringing clarity to questions about Prompt Injection Security. Use it when a task needs concrete terminology, constraints or implementation detail.
AI-RULES is a rule-aware CLI for AI-assisted coding governance. It turns project rules in Markdown into structured rule metadata, lightweight local evidence, and deterministic audit/fix prompts so AI coding agents follow your architecture, design patterns, and UI standards more c. Use it as a repeatable review, validation or hardening pass.
A curation of awesome tools, documents and projects about LLM Security. Use it to navigate the topic and choose relevant methods, papers or tools.
This repository serves as a comprehensive resource for learning, building, and sharing prompt engineering techniques, ranging from basic concepts to a. Use it as a repeatable review, validation or hardening pass.
Publish versioned Markdown. Build focused streams. Give people and agents only the context they need.
Cryptographic governance for every Claude Code tool call. Each invocation is evaluated against a Cedar policy and produces an Ed25519-signed receipt that anyone can verify offline. Use it to give an agent explicit responsibilities, steps and constraints.
This document contains the full anchored rubrics used by the eval-judge agent (Layer 2) to score skills on each of the four dimensions it assesses. Each dimension uses a 0.0–1.0 scale with five anchor points. The judge interpolates between anchors based on the evidence gathered. Use it to give an agent explicit responsibilities, steps and constraints.
This document is the authoritative reference for how PluginEval measures plugin and skill quality. It covers the three evaluation layers, all ten scoring dimensions, the composite formula, badge thresholds, anti-pattern flags, Elo ranking, and actionable improvement tips. Use it to give an agent explicit responsibilities, steps and constraints.
You are a debugging expert specializing in setting up comprehensive debugging environments, distributed tracing, and diagnostic tools. Configure debugging workflows, implement tracing solutions, and establish troubleshooting practices for development and production environments. Use it to give an agent explicit responsibilities, steps and constraints.
This repository centralizes and summarizes practical and proposed defenses against prompt injection. Use it to navigate the topic and choose relevant methods, papers or tools.
Awesome-LLM-Eval: a curated list of tools, datasets/benchmark, demos, leaderboard, papers, docs and models, mainly for Evaluation on Large Language Models and exploring the boundaries and limits of Generative AI. Use it to build a structured path from fundamentals to hands-on practice.
Policy enforcement, identity, sandboxing, and SRE for autonomous AI agents. One pip install, any framework. Use it as a repeatable review, validation or hardening pass.
Bringing clarity to questions about Prompt Injection Security. Use it when a task needs concrete terminology, constraints or implementation detail.
AI-RULES is a rule-aware CLI for AI-assisted coding governance. It turns project rules in Markdown into structured rule metadata, lightweight local evidence, and deterministic audit/fix prompts so AI coding agents follow your architecture, design patterns, and UI standards more c. Use it as a repeatable review, validation or hardening pass.
A curation of awesome tools, documents and projects about LLM Security. Use it to navigate the topic and choose relevant methods, papers or tools.
This repository serves as a comprehensive resource for learning, building, and sharing prompt engineering techniques, ranging from basic concepts to a. Use it as a repeatable review, validation or hardening pass.