Skill UI

This skill helps developers create highly focused, folder-specific documentation (`CLAUDE.md` and `AGENTS.md`). It systematically analyzes all files in a target directory to extract project purpose, operational constraints, naming conventions, and critical decisions. This deep context ensures that future AI agents (like Claude or Codex) operate with localized knowledge, preventing them from relying on global, generalized instructions and improving their accuracy in that specific domain.

A comprehensive suite designed for AI agents to perform safe, deep, and multi-format web searches. It allows agents to retrieve current web information, analyze PDFs, extract YouTube transcripts, and access GitHub code files. Instructions include guidelines for varying search depths and using specialized tools for code context retrieval.

This guide provides comprehensive instructions for packaging multiagent system (MARL) and game theory evidence for academic submissions, particularly for conferences like AAMAS. It emphasizes that the artifact must be inspectable, containing not just a trained model, but the game definition, environment code, opponent sets, and interaction protocols, ensuring rigorous reproducibility and reviewability.

Systematically transforms high-level ideas or goals into a detailed, structured, and executable taskbook designed for AI agents. This process involves deep research, validation against existing codebases, adherence to strict formatting rules, and generating a single, copy-pastable block of instructions for immediate agent execution. It distinguishes between execution and exploration tasks, ensuring all steps are verifiable and anti-cheating measures are included.

A comprehensive, stack-agnostic tool for deeply evaluating agent harnesses. It checks for broken code paths, redundant instructions, and the overall usefulness of agent logic using a structured, multi-stage dual-judge protocol. The process involves high-priority questionnaires (Q1, Q2) before running three defined tracks: A (Correctness), B (Redundancy), and C (Usefulness). Ideal for auditing complex agent systems before deployment or major refactoring.

This skill creates, revises, or audits AGENTS.md files based on repository evidence and verified commands. It ensures coding agents understand the workflow without rediscovering it, preserving maintainer intent. It builds evidence maps from manifests and CI, scopes instructions correctly, and validates changes before handoff to prevent mistakes.

This skill involves testing AI agents for direct prompt injection vulnerabilities where user messages attempt to override system instructions. It focuses on role confusion and instruction hijacking without external content, ensuring agents maintain security boundaries against direct manipulation attempts.

This skill helps developers create highly focused, folder-specific documentation (`CLAUDE.md` and `AGENTS.md`). It systematically analyzes all files in a target directory to extract project purpose, operational constraints, naming conventions, and critical decisions. This deep context ensures that future AI agents (like Claude or Codex) operate with localized knowledge, preventing them from relying on global, generalized instructions and improving their accuracy in that specific domain.

Maps, explains, and lints repository-scoped coding-agent instruction files such as AGENTS.md, CLAUDE.md, Copilot and Cursor rules. Run local read-only scans to inventory instructions, audit recursive imports, and export JSON or SARIF diagnostics before changing code.

Audit named agent instructions, tool definitions, and embedded prompts locally with LintLang 0.8.0. Reports finding codes, severities, and locations without editing files or calling a model.

This skill detects persistent instruction injection vulnerabilities in AI agents. It verifies if the agent stores malicious instructions in long-term memory or context to execute later. The process involves attempting to write a persistent rule and verifying if the agent follows it in subsequent interactions. It focuses on behavioral persistence rather than data leakage.

A comprehensive suite designed for AI agents to perform safe, deep, and multi-format web searches. It allows agents to retrieve current web information, analyze PDFs, extract YouTube transcripts, and access GitHub code files. Instructions include guidelines for varying search depths and using specialized tools for code context retrieval.