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AgentOS — Ship with AI. Trust What You Ship.

A workflow operating system for AI coding tools. Turn "just generate code" into a repeatable engineering process with quality gates, interface contracts, and cost visibility.

License: Apache-2.0 PRs Welcome


What Is This?

Most AI coding sessions fail in predictable ways:

  • Scope starts fuzzy and drifts mid-implementation
  • API assumptions diverge across agents or modules
  • Code gets generated without enough verification
  • Token spend spirals before you notice

AgentOS fixes this with one principle: plan first, gate every step, keep evidence.

It works with your existing repo, your existing AI tools, and your existing GitHub workflow. No lock-in. One skill file. Drop it in and go.


The 5 Modules

Each module does one thing well.

Module What It Does Personality
Idea-to-Plan Rough idea -> scoped MVP + tasks in under 5 minutes "That's broad. What's the one thing existing tools don't do well that this solves?"
Gate System Enforce Ready / Code / Acceptance / Deploy quality gates "Gates are PHYSICAL BLOCKERS — not suggestions. A failed gate stops all execution."
Protocol Lock Signed interface contracts before anyone writes code "No contract, no code. Diverged assumptions are the #1 multi-agent failure mode."
Cost Guard Track token usage per task/session with budget rules "You're 80% through your budget with 40% of tasks remaining. Pausing for review."
Sprint Engine Deliver through GitHub Issues, labels, and milestones "Sprint velocity: 34 points. Carry-over: 2 tasks. Retrospective filed."

How It Works

Idea
  -> Idea-to-Plan (scope + challenge weak assumptions)
  -> Ready Gate (block if no acceptance criteria or contracts)
  -> Execute (write code)
  -> Code Gate (block if coverage < 80% or contract mismatch)
  -> PR with evidence
  -> Acceptance Gate (verify every AC one by one)
  -> Deploy Gate (load test + security scan + rollback plan)
  -> Done

Every stage has explicit checks. Failed gates block progress until fixed. No gate can be self-overridden — only a human can authorize a skip.


Real-World Use Cases

Scenario 1: Solo Founder Building an MVP

Your workflow:

  1. /agentos plan — Tell it your idea, get challenged on scope, receive a structured plan with max 3 MVP features
  2. Ready Gate — Blocks until every task has acceptance criteria
  3. Code Gate — Blocks until test coverage hits 80%
  4. Cost Guard — Alerts when token spend exceeds budget

Result: Ship a validated MVP without scope creep or surprise AI bills.

Scenario 2: Multi-Agent Development Team

Your workflow:

  1. Protocol Lock — Lock API contracts between frontend and backend agents before anyone writes code
  2. Gate System — Each agent's output must pass quality gates before handoff
  3. Sprint Engine — Track all work through GitHub Issues with automated labels and milestones
  4. Cost Guard — Per-task token budgets prevent runaway agent loops

Result: Multiple AI agents working in coordination without contract drift or quality regression.

Scenario 3: Adding AI Assistance to an Existing Team

Your workflow:

  1. Drop AgentOS into your repo as a submodule
  2. AI agents follow the same gate process your team already uses
  3. Every PR includes gate evidence — coverage numbers, contract compliance, AC verification

Result: AI-generated code held to the same standard as human code.


Quick Start

1) Add AgentOS to your project

# Option A: git submodule
git submodule add https://github.com/dongowu/agentos-skill .agentos

# Option B: clone directly
git clone https://github.com/dongowu/agentos-skill .agentos

2) Use Claude Skill standard layout (recommended)

mkdir -p .claude/skills
cp -r .agentos/.claude/skills/* .claude/skills/

Then invoke directly in Claude Code:

/agentos I want to build a customer feedback tool

3) Shortcut routing

/agentos plan <idea>          # Idea-to-Plan
/agentos gate <task>          # Gate System
/agentos protocol <interface> # Protocol Lock
/agentos cost <budget>        # Cost Guard
/agentos sprint <goal>        # Sprint Engine

4) Or point any AI tool to SKILL.md

Tool Command
Claude Code claude --skill .agentos/SKILL.md "build a feedback tool"
Codex CLI codex --instructions .agentos/SKILL.md "build a feedback tool"
Gemini CLI gemini --context .agentos/ "build a feedback tool"
Cursor Add @.agentos/SKILL.md to .cursor/rules
OpenClaw claw skill install github:dongowu/agentos-skill

Module Deep Dive

Idea-to-Plan

"Max 3 MVP features. More than 3 means scope creep. Push back."

  • Acts as a product co-founder, not a chatbot
  • Challenges weak assumptions ("I want to build a social network" -> "What does GitHub + Twitter + LinkedIn not do well?")
  • Forces every task to have acceptance criteria
  • First Step must be actionable today — not "research X", but "open terminal and run Y"

Gate System

"Gates are not suggestions. A failed gate stops ALL execution."

4 gates, each with explicit checklists:

  • Ready Gate — No task starts without ACs, signed contracts, priority, and estimates
  • Code Gate — No PR without 80% coverage, contract compliance, no hardcoded secrets
  • Acceptance Gate — No "Done" without verifying every AC with evidence
  • Deploy Gate — No production without load test, security scan, rollback plan

Override requires explicit human authorization + logged justification.

Protocol Lock

"No contract, no code."

  • Interface contracts must be signed before implementation begins
  • Breaking changes are blocked until contracts are re-signed by both sides
  • Prevents the #1 multi-agent failure: diverged API assumptions

Cost Guard

"Token spend is hard to track until it's too late. Not anymore."

  • Per-task and per-session token tracking
  • Configurable budget thresholds with warnings at 60%, 80%, 100%
  • Hard pause at budget limit — no silent overspend
  • Session cost summaries for planning

Sprint Engine

"Delivery is a system, not a wish."

  • Maps tasks to GitHub Issues with labels and milestones
  • Tracks velocity, carry-over, and completion rates
  • Generates sprint reports and retrospectives
  • Hooks into task lifecycle events (start, code-done, sprint-end)

Compatibility

Works with your existing AI tools. No lock-in.

Tool Status
Claude Code Supported
Codex CLI Supported
Gemini CLI Supported
Cursor Supported
OpenClaw Supported
OpenAI Symphony Compatible as upstream planning/gating layer

Repository Layout

agentos-skill/
  SKILL.md                          # Entry point for any AI tool
  skills/
    idea-to-plan.md                 # Idea -> structured plan
    gate-system.md                  # 4 quality gates
    protocol-lock.md                # Interface contracts
    cost-guard.md                   # Token budget tracking
    sprint-engine.md                # GitHub-based delivery
  hooks/
    on-task-start.md                # Fires when a task begins
    on-code-done.md                 # Fires after code is written
    on-sprint-end.md                # Fires at sprint boundary
  templates/
    issue.md                        # GitHub Issue template
    pr.md                           # Pull Request template
    contract.json                   # Interface contract schema
    sprint-report.md                # Sprint retrospective
    gate-report.md                  # Gate evidence report
  .claude/skills/                   # Claude Code native integration
    agentos/SKILL.md
    agentos-idea-to-plan/SKILL.md
    agentos-gate-system/SKILL.md
    agentos-protocol-lock/SKILL.md
    agentos-cost-guard/SKILL.md
    agentos-sprint-engine/SKILL.md

What Makes This Different

vs. Generic AI Prompts

  • Not "act as a senior developer" — it's a structured operating model with enforceable gates

vs. Prompt Libraries

  • Not one-off prompts — it's a connected workflow where each module feeds the next

vs. AI Coding Tools

  • Not replacing your tools — it's a layer on top that adds discipline and evidence

vs. Agent Personality Collections

  • Not 55 characters waiting for instructions — it's 5 modules that enforce process whether the agent wants to or not

Contributing

Contributions are welcome.

  • Report friction: Open an issue when a gate feels wrong, a template is missing, or a workflow breaks
  • Improve modules: Better gate checks, sharper plan templates, tighter contracts
  • Share real usage: What worked, what failed, what you changed — this makes defaults better for everyone
  • Add integrations: New AI tool support, CI/CD hooks, reporting formats

If you submit a PR, include: the problem being solved, expected behavior, and evidence of the change.


Roadmap

  • Richer gate report templates with visual evidence
  • Agent handoff templates (standardized context passing)
  • Multi-agent orchestration patterns
  • Reference integrations for more AI CLIs
  • Onboarding examples for first-time users
  • Community-contributed gate presets

License

Apache-2.0 — Use freely, commercially or personally.


AgentOS: Your AI writes the code. AgentOS makes sure it ships.

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