Loading...
Loading...
Found 236 Skills
Set up automated agent-driven development with Ralph. Run AI agents in a loop to implement features from user stories, verify acceptance criteria, and log progress for the next agent.
Deep codebase exploration. Triggers: research, explore, investigate, understand, deep dive, current state.
N coordinated agents on shared task list using Claude Code native teams
An Agent dedicated to brainstorming and finalizing specifications. Finalize a single, implementable and testable Spec.md.
Use jj (Jujutsu) for local version control instead of git. Activate when: the repo has a .jj/ directory, the user or project config mentions jj, the user says 'use jj', or any version control operation is needed in a jj-managed repo. Also use this skill when the user asks to commit, branch, stash, rebase, or perform any git-like operation in a repo that uses jj. If unsure whether the repo uses jj, check for a .jj/ directory.
Orchestrate work through a team of agents coordinating via chat. Use when entering orchestrator mode, managing agents, launching agents, or the user says "launch", "spin up", "orchestrate", or wants work delegated to agents.
Helps coding agents use vit to discover, follow, skim, and ship software capabilities (caps) over ATProto. Activates when the user mentions vit, beacons, caps, shipping, skimming, following, vetting, or social coding.
Find or generate a Nimble agent for a task, then run it. Use when the user needs structured web data extraction via Nimble agents/templates.
Create visualize finance logic diagrams (e.g., Draw.io XML) to explain complex finance transmission chains or finance logic flows.
Use when the user says /bye, "wrap up", "end session", or similar. Reconstructs full session history including compacted context, creates a sessionlog, commits changes, and summarizes next steps.
Sequence tasks from a feature breakdown into an optimal execution order, identify dependencies and parallelization opportunities, and create an agent-ready execution sequence. Use when you have a feature breakdown and need to determine the correct order to build tasks and which can run in parallel.
Operational prompt engineering for production LLM apps: structured outputs (JSON/schema), deterministic extractors, RAG grounding/citations, tool/agent workflows, prompt safety (injection/exfiltration), and prompt evaluation/regression testing. Use when designing, debugging, or standardizing prompts for Codex CLI, Claude Code, and OpenAI/Anthropic/Gemini APIs.