Agent Skills Hub

subagent-driven-development

Execute implementation plans using isolated subagents for each task, featuring a rigorous two-stage review process for spec compliance and code quality.

tdd-workflow

A rigorous TDD workflow agent that enforces test-first development, ensuring 80%+ code coverage across unit, integration, and E2E tests for features, bug fixes, and refactoring.

p9

P9 Tech Lead mode: Manages P8 agent teams via Task Prompts (six-element) without direct coding. Orchestrates 3+ parallel agents for project management, task decomposition, and architecture.

french-claude

A design-focused coding agent that brings world-class interface craft, motion, and systematic front-end engineering to your development workflow.

workflows-plan

Transforms feature requests, bug reports, and improvement ideas into structured, actionable markdown project plans using repository research and industry best practices.

review-react-best-practices

Perform automated, rule-based performance and reliability audits for React and Next.js applications, covering bundle size, waterfalls, rendering, and data fetching.

openclaw-pr-maintainer

Automated OpenClaw repository maintainer: triage, label, and validate PRs/issues using gitcrawl and GitHub CLI.

creating-opencode-agents

Expert guidance and configuration standards for creating specialized OpenCode AI agents, including YAML frontmatter, tool permissions, and operational modes.

sparc-methodology

SPARC methodology for multi-agent development: systematic Specification, Pseudocode, Architecture, Refinement, and Completion workflows via Claude Flow orchestration.

identify-assumptions-existing

Stress-test existing product feature ideas by identifying risky assumptions across Value, Usability, Viability, and Feasibility using a multi-perspective devil's advocate framework.

root-cause-tracing

Systematic debugging skill to trace errors backward through call stacks, identify original triggers, and implement layered defenses instead of patching symptoms.

semantic-compression

Aggressively prune grammatical scaffolding and filler text from inputs to optimize LLM token usage while retaining core semantic content.