By byronxlg
Configuration language and compiler for multi-agent AI pipelines. Compiles YAML into agent skills for Claude Code, Cursor, Windsurf, Codex, Copilot, Gemini, Goose, Roo Code, Kiro, and Junie.
Review code for correctness, clarity, and security.
Write clean, correct, production-quality code.
Evaluate trade-offs, document options, and justify recommendations.
Read, create, edit, and organize files and directories.
Work with GitHub branches, PRs, issues, and reviews via the gh CLI.
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Typed coordination for multi-agent pipelines
Documentation | Getting Started | Live Demo | Pipeline Builder
You have a dozen agents in scattered Markdown files. How do you know they're wired correctly? How do you validate that Agent A's output types match Agent B's input? How do you catch cycles or unreachable nodes before runtime?
Native platforms make it easy to define individual agents and skills. What they don't solve is the coordination layer between agents: typed state, execution flows, conditional routing, and compile-time validation. Skillfold fills that gap. Declare your pipeline in YAML, validate it at compile time, and output native SKILL.md files for every agent. No runtime, no daemon, no SDK.
You wire three agents together by hand. The engineer writes state.code, the reviewer reads it, and a conditional routes back on failure. Everything lives in separate Markdown files with no shared schema, no validation, and no way to check correctness before you run it.
end, or did you leave one dangling?These are coordination problems, and they only get worse as the pipeline grows.
Declare everything in one YAML config. The compiler validates it at compile time and outputs native agent files.
# skillfold.yaml
skills:
atomic:
planning: ./skills/planning
coding: ./skills/coding
review: ./skills/review
composed:
engineer:
compose: [planning, coding]
description: "Implements the plan and writes tests."
reviewer:
compose: [review]
description: "Reviews code for correctness and quality."
state:
Review:
approved: bool
feedback: string
code: { type: string }
review: { type: Review }
team:
flow:
- engineer:
writes: [state.code]
then: reviewer
- reviewer:
reads: [state.code]
writes: [state.review]
then:
- when: review.approved == false
to: engineer
- when: review.approved == true
to: end
npx skillfold
The compiler checks that every state read has a matching write, every transition target exists, every cycle has an exit condition, and no two agents write the same field in parallel. If anything is wrong, you get an error at build time instead of a broken pipeline at runtime.
npx skillfold init my-team # scaffold a starter pipeline
cd my-team
npx skillfold # compile it
For a step-by-step walkthrough, see the Getting Started guide. To compile directly to your platform, see the Integration Guide.
[!TIP] Add
team.orchestrator: orchestratorand the orchestrator's compiled SKILL.md gets a generated execution plan with numbered steps, state tables, and conditional branches.
$ npx skillfold init demo --template dev-team
skillfold: project initialized
-> skillfold.yaml
Next: cd demo && npm install skillfold && npx skillfold
$ cd demo && npm install skillfold && npx skillfold --target claude-code
skillfold: compiled dev-team
-> .claude/skills/planner/SKILL.md
-> .claude/skills/engineer/SKILL.md
-> .claude/skills/reviewer/SKILL.md
-> .claude/skills/orchestrator/SKILL.md
-> .claude/agents/planner.md
-> .claude/agents/engineer.md
-> .claude/agents/reviewer.md
-> .claude/commands/run-pipeline.md
3 agents, 5 skills (2 shared). ~79 lines deduplicated.
$ ls .claude/agents/
engineer.md planner.md reviewer.md
$ head -20 .claude/agents/engineer.md
<!-- Generated by skillfold v1.23.0 from dev-team (skillfold.yaml). Do not edit directly. -->
---
name: engineer
description: Implements the plan by writing production code and tests.
model: inherit
color: green
---
# engineer
Implements the plan by writing production code and tests.
## Reads
- `state.plan`
## Writes
- `state.implementation`
$ npx skillfold list
dev-team
npx claudepluginhub byronxlg/skillfold --plugin skillfoldMulti-agent orchestration with AI SDK v5 - handoffs, routing, and coordination for any AI provider (OpenAI, Anthropic, Google)
This skill should be used when the model's ROLE_TYPE is orchestrator and needs to delegate tasks to specialist sub-agents. Provides scientific delegation framework ensuring world-building context (WHERE, WHAT, WHY) while preserving agent autonomy in implementation decisions (HOW). Use when planning task delegation, structuring sub-agent prompts, or coordinating multi-agent workflows.
The team-architecture factory for Claude Code — a meta-skill that turns a domain description into an agent team and the skills they use, with six pre-defined team-architecture patterns (Pipeline, Fan-out/Fan-in, Expert Pool, Producer-Reviewer, Supervisor, Hierarchical Delegation). Claude Code용 팀 아키텍처 팩토리: 도메인 한 문장을 에이전트 팀과 스킬 세트로 변환하는 메타 스킬.
Mission Planner, Agent Creator, Skill Creator, and Librarian — the complete Forge suite for science-backed AI team assembly
Agent Teams スキルを設計・構築するためのベストプラクティスガイド。サブエージェント定義、SendMessage 通信プロトコル、タスク依存管理、PostToolUse Hook ログ、MCP ツール統合、コンテキストファイル設計を網羅。7つの実績あるチームスキルから抽出したパターン集
Multi-agent orchestrator — supervisor loop that launches agents to implement plans