By lefant
HumanLayer-style RPI toolkit with agents and commands for research/plan/implement flows.
Create implementation plans with thorough research (no thoughts directory)
Implement technical plans from thoughts/shared/plans with verification
Document codebase as-is without evaluation or recommendations
Analyzes codebase implementation details. Call the codebase-analyzer agent when you need to find detailed information about specific components. As always, the more detailed your request prompt, the better! :)
Locates files, directories, and components relevant to a feature or task. Call `codebase-locator` with human language prompt describing what you're looking for. Basically a "Super Grep/Glob/LS tool" — Use it if you find yourself desiring to use one of these tools more than once.
codebase-pattern-finder is a useful subagent_type for finding similar implementations, usage examples, or existing patterns that can be modeled after. It will give you concrete code examples based on what you're looking for! It's sorta like codebase-locator, but it will not only tell you the location of files, it will also give you code details!
The research equivalent of codebase-analyzer. Use this subagent_type when wanting to deep dive on a research topic. Not commonly needed otherwise.
Discovers relevant documents in thoughts/ directory (We use this for all sorts of metadata storage!). This is really only relevant/needed when you're in a reseaching mood and need to figure out if we have random thoughts written down that are relevant to your current research task. Based on the name, I imagine you can guess this is the `thoughts` equivilent of `codebase-locator`
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Claude Code plugins for development workflows.
lefant/agent-skillsSkills have been migrated to lefant/agent-skills.
The rpi Claude plugin remains here because it still carries Claude-specific subagent configuration. A copy of the RPI workflow also exists in agent-skills for other CLIs.
npx claudepluginhub lefant/claude-code-plugins --plugin rpiSend Claude Code notifications via ntfy
Autonomous research orchestration: agents for hypothesis-driven investigation, experiment running, fresh-eyes review, and batch evaluation.
Deep research coordination: academic papers, technical analysis, data insights, and web intelligence
No description provided.
Structured AI-enabled research workflows for software development: Research, Plan, Experiment, Implement
Code-grounded Agent Skills for building with Pi (pi-mono). Covers all 7 packages: pi coding agent (workspace, extensions, packages, RPC/SDK, customization), pi-ai (multi-provider LLM API), pi-agent-core (agent runtime), pi-tui (terminal UI), pi-web-ui (web components), pi-mom (Slack bot), and pi-pods (vLLM GPU deployments). Skills are grounded in the pi-mono source corpus and cite real file paths.
Autonomous, personalized research loops for Claude Code. Set a topic, walk away, come back to a quality-gated report adapted to your projects.