nousresearch/dogfood
Exploratory QA of web apps: find bugs, evidence, reports.
Exploratory QA of web apps: find bugs, evidence, reports.
npx skills add https://github.com/nousresearch/hermes-agent/tree/main/skills/dogfoodExploratory QA of web apps: find bugs, evidence, reports.
Loom is a unified agentic ecosystem that enables seamless collaboration between multiple AI tools (jcode, crush, claude-code, opencode, goose, etc.) while sharing skills, sessions, and MCP servers. While also giving flexibility to use it as workflow engine for your task and you can choose to use any agent per task.
Comprehensive guide for configuring and managing GitHub Dependabot. Use this skill when users ask about creating or optimizing dependabot.yml files, managing Dependabot pull requests, configuring dependency update strategies, setting up grouped updates, monorepo patterns, multi-ecosystem groups, security update configuration, auto-triage rules, or any GitHub Advanced Security (GHAS) supply chain security topic related to Dependabot. For pre-commit dependency vulnerability scanning in AI coding agents via the GitHub MCP Server, this skill references the Advanced Security plugin (`advanced-security@copilot-plugins`). Use this skill when an agent needs to scan dependencies for known vulnerabilities before committing.
Recently updated agent-adjacent repository: wivien19/Agentic-Workflow-for-AI-Assisted-Software-Vulnerability-Repair.
Handles LLM-as-judge evaluation workflows on Arize including creating/updating evaluators, running evaluations on spans or experiments, managing tasks, trigger-run operations, column mapping, and continuous monitoring. Use when the user mentions create evaluator, LLM judge, hallucination, faithfulness, correctness, relevance, run eval, score spans, score experiment, trigger-run, column mapping, continuous monitoring, or improve evaluator prompt.
Hermes AI agent configuration backup — skills, templates, and tool definitions (no secrets)
Drive a spec-first workflow for substantial features by writing PRODUCT.md before implementation, writing TECH.md when warranted, and keeping both specs updated as implementation evolves. Use when starting a significant feature, planning agent-driven implementation, or when the user wants product and tech specs checked into source control.