60 boosters for "lua" — open source, verified from GitHub, ready to install
Brain in the Fish evaluates documents (essays, policies, contracts, clinical reports, surveys) against evaluation criteria using a panel of AI agents. Each agent's mental state exists as OWL ontology. Scoring is grounded in an Evidence Density Scorer (EDS) that makes hallucination mathematically det
Evaluate the user's ambient context artifacts for compatibility with swarm's governance rules. You are a read-only diagnostic — never modify any files. 1. CLAUDE.md files. Read the project's (working directory root). If exists, read that too. Also check (global config) — it loads into every sessi
Use AskUserQuestion to ask the buyer: Tell the user the version was updated, then re-read the EVALUATION.md file from the updated directory and proceed with the skill. After the preamble, read the full evaluation methodology:
Turn social media paper recommendations into actionable research items. Use platform-specific tools to fetch the full content: From the extracted content, identify all referenced papers:
"name": "phdtaketaketake", "description": "Connection-first PhD advisor matcher — finds the right advisor by network strength, not h-index. Evidence-first: every signal traces to a real source the agent fetched. Best-supported for physics / MSE; extensible to other STEM with field-specific caveats."
An agent designed to evaluate other agents and tasks, with library-first constraints and multi-tool integration across Claude platforms. Useful for teams building quality assurance workflows into their Claude-based systems.
"description": "Smart command safety filter for Claude Code — parses shell pipelines and evaluates per-command safety rules to auto-approve safe commands and block dangerous ones",
AgentAsJudge is an agentic evaluation framework that enables AI to systematically assess and compare the quality of multiple-choice questions across educational value, clarity, and answerability. It benefits educators, content creators, and assessment teams looking to automate quality control of exam and quiz questions.
AgentAsJudge is an agentic evaluation framework that enables AI systems to critically review educational introductions by validating them against specified quality metrics and providing constructive feedback. It benefits educators, instructional designers, and developers building AI-assisted learning platforms who need reliable, fair assessment of educational content.
"version": "5.10.0", "description": "Memory → Evaluation → Credential → Access Control for AI agents. Persistent memory with W3C Verifiable Credentials, capability-based access control, drift detection, and FSRS-6 spaced repetition.", "name": "kobie3717",
"name": "open-academic-paper-machine", "description": "Open Academic Paper Machine — Autonomous academic paper production system with idea evaluation gate and paper-vs-code audit. NEW in v6.4: /audit-paper command and audit-engine skill — static audit of a paper's empirical claims (datasets, models,
A development guide for extending PatientHub with new patient simulation agents, enabling researchers and developers to implement custom behavioral models for healthcare simulations.
"name": "cre-skills", "description": "112 institutional-grade CRE skills covering ~97% of commercial real estate workflow steps. Deal screening, underwriting, structuring, due diligence, capital markets, market research, asset management, leasing, investor relations, development, disposition, sourci
Provides a standardized workflow for creating and maintaining unit tests for Jass modules in War3Lib using Zinc, covering file structure, test generation, assertion patterns, and validation. Essential for developers building or refactoring Jass libraries who need reproducible, maintainable test suites.
Alexi is an expropriation appraisal specialist that provides expert-level valuation analysis using before/after methods, comparable sales, and severance damage assessment for legal and real estate professionals. Real estate appraisers, lawyers, and property valuators benefit from delegating complex expropriation cases to this specialized agent.
The architect agent automates system design for new projects and major refactoring efforts, helping teams create scalable architectures with documented trade-offs. Ideal for engineering teams starting greenfield projects or evaluating architectural changes.
ArmBench-LLM is a system prompt for benchmarking large language models using Armenian character-to-numeric matching tasks. It's designed for developers evaluating LLM performance across multiple coding platforms.
"name": "io-skills", "name": "OpenMatter-Network" "description": "Modular skills for Industrial-Organizational psychologists: personnel selection validation and the evaluation/audit of AI-based assessment, derived from authoritative professional standards.",
ArmBench-LLM is a system prompt framework for evaluating large language models on Armenian language tasks through structured multiple-choice questions. It's designed for developers and AI researchers who need standardized benchmarking tools across popular coding assistants and chat platforms.
A debate judge agent that objectively evaluates arguments using zero-sum scoring across Toulmin structure, evidence strength, and logical rigor. Ideal for researchers, educators, and developers building computational debate systems.
Tool-evaluator is an agent that rapidly assesses development tools, frameworks, and services through structured benchmarking and comparative analysis to support informed technology adoption decisions. It benefits engineering teams and tech leads evaluating new solutions aligned with studio goals.
skill-auditor is an expert reviewer that evaluates SKILL.md files against Claude Code Skills best practices, helping developers ensure their skills meet structural and effectiveness standards. It's essential for skill creators and maintainers who want to validate compliance before publishing.
PrismBench enables developers to create specialized LLM agents through YAML configuration for systematic evaluation of model capabilities using Monte Carlo Tree Search. Useful for ML engineers, researchers, and teams building production LLM systems who need comprehensive benchmarking and evaluation frameworks.
PrismBench enables developers to create specialized LLM agents through YAML configuration for comprehensive benchmarking and evaluation of language model capabilities. Teams building AI evaluation systems and ML testing pipelines benefit from its systematic Monte Carlo Tree Search approach and containerized deployment.
