Agents
AI agents and automation
Uses and selection guide
Use these skills to connect model capabilities with repeatable agent workflows. Check the model or tool requirements, context inputs and evaluation examples; choose the smallest workflow that covers your actual task.
ingestion-pipeline-doctor-nodejs
by PostHog
Ingestion pipeline architecture overview and convention reference. Use when you need a quick orientation to the pipeline framework or want to know which doctor agent to use for a specific concern.
remember
by langchain-ai
"Review the current conversation and capture valuable knowledge — best practices, coding conventions, architecture decisions, workflows, and user feedback — into persistent memory (AGENTS.md) or reusable skills. Use when the user says: (1) remember this, (2) save what we learned, (3) update memory, (4) capture learnings."
math-olympiad
by anthropics
"Solve competition math problems (IMO, Putnam, USAMO, AIME) with adversarial
replicate
by brycewang-stanford
"Build and verify replication packages — routes to reproducibility-auditor agent"
structural-modeling
by brycewang-stanford
This skill covers structural econometric models. Use when the user is building, estimating, or debugging structural models — including BLP demand estimation, dynamic discrete choice, auction models, or any workflow involving moment conditions, nested fixed-point algorithms, or MPEC formulations. Triggers on "structural model", "moment conditions", "NFXP", "MPEC", "BLP", "random coefficients", "dynamic discrete choice", "CCP", "Rust model", "auction estimation", "GMM objective", "inner loop", "contraction mapping", or convergence/starting value problems in optimization-based estimation.
bayesian-estimation
by brycewang-stanford
This skill covers Bayesian estimation and inference in quantitative social science. Use when the user is specifying priors, running MCMC, diagnosing chain convergence, or reporting posterior summaries — including hierarchical models, Bayesian structural models, and small-sample settings where priors regularize. Triggers on "Bayesian estimation", "Bayesian inference", "MCMC", "Markov chain Monte Carlo", "Stan", "PyMC", "NumPyro", "prior", "posterior", "credible interval", "Bayesian structural", "Bayesian BLP", "Bayesian DSGE", "hierarchical model", "random effects Bayesian", "posterior predictive check", "Bayes factor", "prior predictive check", "NUTS", "HMC", "Hamiltonian Monte Carlo", "R-hat", "rhat", "effective sample size", "ESS", "Bayesian calibration", "posterior distribution", "prior elicitation", "weakly informative prior", "brms", "rstanarm", "cmdstanpy", "pymc", "arviz".
workflows:brainstorm
by brycewang-stanford
Explore methodological approaches through structured analysis before planning implementation
pedagogy-review
by brycewang-stanford
Run holistic pedagogical review on lecture slides. Checks narrative arc, student prerequisites, worked examples, notation clarity, and deck pacing.
deep-audit
by brycewang-stanford
Deep consistency audit of the entire repository infrastructure. Launches 4 parallel specialist agents to find factual errors, code bugs, count mismatches, and cross-document inconsistencies. Then fixes all issues and loops until clean. Use when: after making broad changes, before releases, or when user says "audit", "find inconsistencies", "check everything".
slfg
by brycewang-stanford
Full autonomous research workflow using swarm mode for parallel execution
qa-quarto
by brycewang-stanford
Adversarial Quarto vs Beamer QA. Critic finds issues, fixer applies fixes, loops until APPROVED (max 5 rounds).
blackbox
by NousResearch
Delegate coding tasks to Blackbox AI CLI agent. Multi-model agent with built-in judge that runs tasks through multiple LLMs and picks the best result. Requires the blackbox CLI and a Blackbox AI API key.
kanban-video-orchestrator
by NousResearch
Plan, set up, and monitor a multi-agent video production pipeline backed by Hermes Kanban. Use when the user wants to make ANY video — narrative film, product/marketing, music video, explainer, ASCII/terminal art, abstract/generative loop, comic, 3D, real-time/installation — and the work warrants decomposition into specialized profiles (writer, designer, animator, renderer, voice, editor, etc.) coordinated through a kanban board. Performs adaptive discovery to scope the brief, designs an appropriate team for the requested style, generates the setup script that creates Hermes profiles + initial kanban task, then helps monitor execution and intervene when tasks stall or fail. Routes scenes to whichever Hermes rendering / audio / design skill fits each beat (ascii-video, manim-video, p5js, comfyui, touchdesigner-mcp, blender-mcp, pixel-art, baoyu-comic, claude-design, excalidraw, songsee, heartmula, …) plus external APIs for TTS, image-gen, and image-to-video as needed.
eve
by vercel
Build durable backend AI agents with the eve framework. Use when creating, editing, or debugging an eve project — agent instructions, skills, tools, connections, channels, sandboxes, subagents, schedules, or evals.
Local guide
by vercel
The consuming agent's own guide skill.
nature-writing
by Yuan1z0825
Draft, restructure, or plan Nature-style manuscript sections and initial-submission materials from author-provided claims, results, figures, notes, or Chinese drafts. Use for abstracts, introductions, related work, methods, Results or experiments, discussions, conclusions, titles, full manuscript arguments, and first-submission packages such as cover letters, title pages, highlights, author contributions, availability or declaration text, and reviewer suggestions. Also use to classify Results evidence, decide what belongs in main text, captions, Methods or source data, or Supplementary Information, compress Results to the shortest sufficient evidence chain, prevent revision accretion, and audit paragraph necessity or claim repetition. Trigger on drafting a paper or section, structuring a manuscript, academic writing, first submission, 投稿材料、首次投稿、投稿信、标题页、亮点、作者贡献、数据可用性声明、推荐审稿人.
pol-probe-advisor
by deanpeters
Select the right Proof of Life (PoL) probe based on hypothesis, risk, and resources. Use this to match the validation method to the real learning goal, not tooling comfort.
workshop-facilitation
by deanpeters
Facilitate workshop sessions in a one-step, multi-turn flow. Use when an interactive skill needs consistent pacing, options, and progress tracking.
discovery-process
by deanpeters
Run a full discovery cycle from problem hypothesis to validated solution. Use when a team needs a structured path through framing, interviews, synthesis, and experiments.
product-strategy-session
by deanpeters
Run an end-to-end product strategy session across positioning, discovery, and roadmap planning. Use when a team needs validated direction before committing to execution.
langgraph-docs
by langchain-ai
Fetches and references LangGraph Python documentation to build stateful agents, create multi-agent workflows, and implement human-in-the-loop patterns. Use when the user asks about LangGraph, graph agents, state machines, agent orchestration, LangGraph API, or needs LangGraph implementation guidance.
continuous-agent-loop
by affaan-m
Patterns for continuous autonomous agent loops with quality gates, evals, and recovery controls.
autonomous-loops
by affaan-m
"Patterns and architectures for autonomous Claude Code loops — from simple sequential pipelines to RFC-driven multi-agent DAG systems."
continuous-learning-v2
by affaan-m
Instinct-based learning system that observes sessions via hooks, creates atomic instincts with confidence scoring, and evolves them into skills/commands/agents. v2.1 adds project-scoped instincts to prevent cross-project contamination.