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Topic: agents

2,643 skills in this topic.

paseo-orchestrate End-to-end implementation orchestrator. Use when the user says "orchestrate", "implement this end to end", "build this", or wants a full feature/fix implemented through a team of agents with planning, implementation, review, and QA phases.
getpaseo/paseo 2,147
paseo Paseo CLI reference for managing agents. Load this skill whenever you need to use paseo commands.
getpaseo/paseo 2,147
paseo-committee Form a committee of two high-reasoning agents to step back, do root cause analysis, and produce a plan. Use when stuck, looping, tunnel-visioning, or facing a hard planning problem.
getpaseo/paseo 2,147
paseo-handoff Hand off the current task to another agent with full context. Use when the user says "handoff", "hand off", "hand this to", or wants to pass work to another agent (Codex or Claude).
getpaseo/paseo 2,147
ralph-log-events Event schemas for ml-ralph log.jsonl. Use when logging any event.
pentoai/ml-ralph 33
ml-ralph Create ML project PRDs. Triggers: ml-ralph, create prd, ml project, kaggle.
pentoai/ml-ralph 33
ml-ralph REQUIRED first step for ANY ML task. When user describes an ML problem, goal, experiment, or model improvement — ALWAYS invoke this skill BEFORE exploring code or planning. Triggers: ml-ralph, create prd, ml project, kaggle, implement model, improve model, train model, better model, new approach, experiment.
pentoai/ml-ralph 33
ralph-file-schemas Schema reference for ml-ralph state files (prd.json, kanban.json). Use when reading or writing these files.
pentoai/ml-ralph 33
opentui Comprehensive OpenTUI skill for building terminal user interfaces. Covers the core imperative API, React reconciler, and Solid reconciler. Use for any TUI development task including components, layout, keyboard handling, animations, and testing.
nitodeco/ralph 7
td-task-management Task management for AI agents across context windows. Use when agents need to track work, log progress, hand off state, and maintain context across sessions. Includes workflows for single-issue focus, multi-issue work sessions, and structured handoffs. Essential for AI-assisted development where context windows reset between sessions.
marcus/td 188
td-integration-test Write integration tests for the td-sync admin API using the TestHarness in internal/api/testharness_test.go. Use when asked to write, add, or fix integration tests for admin API endpoints (server, users, projects, events, snapshots, CORS, auth). The harness provides a real HTTP server, fluent state builder, and assertion helpers. Tests go in internal/api/admin_integration_test.go.
marcus/td 188
playwright-debug
voicetreelab/voicetree 783
playwright-debug This skill should be used when the user asks to "debug the electron app", "connect playwright to VoiceTree", "take screenshots of the running app", "interact with the live UI", "inspect the running application", or "test UI elements live". Provides step-by-step instructions for connecting Playwright MCP to a running Electron app for live debugging and automation.
voicetreelab/voicetree 783
glide-mq Creates message queues, workers, job workflows, and fan-out broadcasts using glide-mq on Valkey/Redis Streams. Provides API reference, code patterns, and configuration for queues, workers, delayed/priority jobs, schedulers, batch processing, DAG workflows, request-reply, serverless producers, and AI-native primitives (usage tracking, token streaming, suspend/resume, budget caps, fallback chains, dual-axis rate limiting, rolling usage summaries, vector search, HTTP proxy/SSE). Triggers on "glide-mq", "glidemq", "job queue valkey", "background tasks valkey", "message queue redis streams", "glide-mq LLM queue", "glide-mq AI orchestration queue", "glide-mq token rate limiting", "glide-mq model fallback", "glide-mq human-in-the-loop queue", "glide-mq vector search", "glide-mq AI pipeline".
avifenesh/glide-mq 69
glide-mq-migrate-bullmq Migrates Node.js applications from BullMQ to glide-mq. Covers connection config conversion, API mapping, breaking changes, and new features available after migration. Use when converting BullMQ queues and workers to glide-mq, replacing bullmq with glide-mq, or comparing BullMQ vs glide-mq APIs. Triggers on "bullmq to glide-mq", "replace bullmq with glide-mq", "migrate from bullmq", "switch from bullmq to glide-mq", "convert bullmq to glide-mq", "bullmq migration glide-mq".
avifenesh/glide-mq 69
glide-mq-migrate-bee Migrates Node.js applications from Bee-Queue to glide-mq. Covers the chained builder-to-options API conversion, Queue/Worker separation, and event mapping. Use when converting bee-queue projects to glide-mq, replacing bee-queue with glide-mq, or planning a bee-queue migration. Triggers on "bee-queue to glide-mq", "replace bee-queue with glide-mq", "migrate from bee-queue", "beequeue migration glide-mq".
avifenesh/glide-mq 69
grid-ctf-ops Operational knowledge for the grid_ctf scenario including strategy playbook, lessons learned, and resource references. Use when generating, evaluating, coaching, or debugging grid_ctf strategies.
greyhaven-ai/autocontext 729
autocontext Iterative strategy generation and evaluation system. Use when the user wants to evaluate agent output quality, run improvement loops, queue tasks for background evaluation, check run status, or discover available scenarios. Provides LLM-based judging with rubric-driven scoring.
greyhaven-ai/autocontext 729
nanoresearch-experiment Generate a Python code skeleton from an experiment blueprint
OpenRaiser/NanoResearch 689
nanoresearch-writing Draft a LaTeX research paper from all previous stage outputs
OpenRaiser/NanoResearch 689
nanoresearch-planning Produce an experiment blueprint from a research hypothesis
OpenRaiser/NanoResearch 689
huggingface-accelerate Simplest distributed training API. 4 lines to add distributed support to any PyTorch script. Unified API for DeepSpeed/FSDP/Megatron/DDP. Automatic device placement, mixed precision (FP16/BF16/FP8). Interactive config, single launch command. HuggingFace ecosystem standard.
OpenRaiser/NanoResearch 689
autoresearch Orchestrates end-to-end autonomous AI research projects using a two-loop architecture. The inner loop runs rapid experiment iterations with clear optimization targets. The outer loop synthesizes results, identifies patterns, and steers research direction. Routes to domain-specific skills for execution, supports continuous agent operation via Claude Code /loop and OpenClaw heartbeat, and produces research presentations and papers. Use when starting a research project, running autonomous experiments, or managing a multi-hypothesis research effort.
OpenRaiser/NanoResearch 689
academic-plotting Generates publication-quality figures for ML papers from research context. Given a paper section or description, extracts system components and relationships to generate architecture diagrams via Gemini. Given experiment results or data, auto-selects chart type and generates data-driven figures via matplotlib/seaborn. Use when creating any figure for a conference paper.
OpenRaiser/NanoResearch 689