universal-agent-skills

Framework-agnostic agent skills for any AI agent runtime. Drop-in adapters for Claude Agent SDK, OpenAI Assistants, LangChain, CrewAI, AutoGen, and any system that takes a system prompt.

Each skill is a markdown file with frontmatter and a careful, prompt-engineered body — load it, hand it to your agent as a system prompt or role, done.

Install

npm install universal-agent-skills

Quick start

import { forClaude, forOpenAI, forLangChain, listSkills } from "universal-agent-skills";

// See what's available
console.log(listSkills());
// [
//   { name: "code-reviewer",  description: "...", tags: ["code","review",...] },
//   { name: "data-analyst",   description: "...", tags: [...] },
//   ...
// ]

// Use with Claude Agent SDK / Anthropic Messages API
import Anthropic from "@anthropic-ai/sdk";
const anthropic = new Anthropic();
const { system } = forClaude("code-reviewer");
const msg = await anthropic.messages.create({
  model: "claude-opus-4-7",
  max_tokens: 4096,
  system,
  messages: [{ role: "user", content: "Review this diff:\n\n" + diff }],
});

// Use with OpenAI Assistants
import OpenAI from "openai";
const openai = new OpenAI();
const assistant = await openai.beta.assistants.create({
  model: "gpt-4o",
  ...forOpenAI("researcher"),
});

// Use with LangChain
import { PromptTemplate } from "@langchain/core/prompts";
const tw = forLangChain("test-writer");
const prompt = PromptTemplate.fromTemplate(tw.template);
const formatted = await prompt.format({
  target_code: "...",
  framework: "vitest",
  existing_tests: "",
});

Skills

Engineering

| Skill | What it does | |---|---| | code-reviewer | Reviews code for bugs, security, performance — prioritized findings with file:line + fixes | | test-writer | Generates unit/integration tests covering happy path, edges, errors | | refactorer | Refactors with a behavior-equivalence argument; small safe steps | | debugger | Diagnoses errors with root cause, fix, and verification step | | doc-writer | Generates READMEs, API refs, JSDoc/docstrings |

Design & Design Engineering

| Skill | What it does | |---|---| | component-designer | Specs a UI component — anatomy, states, variants, API, accessibility, edge cases | | visual-polish-reviewer | Reviews UI for alignment, spacing, typography, shadows — the details that separate fine from great | | interaction-designer | Designs micro-interactions with timing, easing, purpose; outputs animation code | | design-token-architect | Designs primitive/semantic/component token layers with theme support | | design-to-code | Translates Figma or design specs into accessible, responsive, token-driven component code |

Knowledge & Research

| Skill | What it does | |---|---| | summarizer | Faithful summaries at TLDR / short / medium / long lengths | | researcher | Decomposes a question, gathers cited sources, synthesizes a brief | | data-analyst | Profiles, analyzes, and reports on tabular data with caveats |

Every skill follows the same authoring principles: lead with the spine, prioritize findings, prefer concrete output formats, name what to avoid.

API

// Load
loadAllSkills(): Skill[]
getSkill(name: string): Skill
listSkills(): SkillSummary[]
asSystemPrompt(name: string): string

// Framework adapters
forClaude(name):     { system, metadata }
forOpenAI(name):     { name, description, instructions }
forLangChain(name):  { template, inputVariables }
forCrewAI(name):     { role, goal, backstory }
forAutoGen(name):    { name, system_message }
forGeneric(name):    { name, description, version, tags, inputs, instructions }

Use with Python frameworks (CrewAI, AutoGen)

The package is ESM JS, but the markdown skills are framework-neutral. To consume from Python:

# After npm install, the markdown files are at:
node_modules/universal-agent-skills/skills/<name>/SKILL.md

Read the file directly, strip frontmatter (---...---), use the body as your agent's backstory (CrewAI) or system_message (AutoGen).

Or generate a JSON catalog from Node and hand it to Python:

node -e "import('universal-agent-skills').then(m => console.log(JSON.stringify(m.loadAllSkills(), null, 2)))" > skills.json

Authoring your own skills

A skill is a directory containing SKILL.md:

skills/
  my-skill/
    SKILL.md

SKILL.md frontmatter:

---
name: my-skill
description: One sentence describing when to use this skill and what it produces.
version: 0.1.0
tags: [domain, capability]
inputs:
  - name: input_name
    description: What this input is.
    required: true
---

# Body

The system-prompt content. Be specific about process, output format, and what to avoid.

Drop the directory into skills/ and loadAllSkills() picks it up.

Design principles

These skills were written to follow a few rules:

• Lead with intent. The first paragraph tells the model when to apply the skill.
• Process before output. How to think, not just what to emit.
• Specify output shape. Models drift without a target format.
• Name failure modes. "What to avoid" sections prevent common errors.
• Concrete over abstract. Concrete examples beat abstract advice.

License

MIT