acceptance-orchestrator
Use when a coding task should be driven end-to-end from issue intake through implementation, review, deployment, and acceptance verification with minimal human re-intervention.
Best use case
acceptance-orchestrator is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Use when a coding task should be driven end-to-end from issue intake through implementation, review, deployment, and acceptance verification with minimal human re-intervention.
Teams using acceptance-orchestrator should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/acceptance-orchestrator/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How acceptance-orchestrator Compares
| Feature / Agent | acceptance-orchestrator | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Use when a coding task should be driven end-to-end from issue intake through implementation, review, deployment, and acceptance verification with minimal human re-intervention.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# Acceptance Orchestrator
## Overview
Orchestrate coding work as a state machine that ends only when acceptance criteria are verified with evidence or the task is explicitly escalated.
Core rule: **do not optimize for "code changed"; optimize for "DoD proven".**
## When to Use
- The task already has an issue or clear acceptance criteria and should run end-to-end with minimal human re-intervention.
- You need structured handoff across implementation, review, deployment, and final verification.
- You want explicit stop conditions and escalation instead of silent partial completion.
## Required Sub-Skills
- `create-issue-gate`
- `closed-loop-delivery`
- `verification-before-completion`
Optional supporting skills:
- `deploy-dev`
- `pr-watch`
- `pr-review-autopilot`
- `git-ship`
## Inputs
Require these inputs:
- issue id or issue body
- issue status
- acceptance criteria (DoD)
- target environment (`dev` default)
Fixed defaults:
- max iteration rounds = `2`
- PR review polling = `3m -> 6m -> 10m`
## State Machine
- `intake`
- `issue-gated`
- `executing`
- `review-loop`
- `deploy-verify`
- `accepted`
- `escalated`
## Workflow
1. **Intake**
- Read issue and extract task goal + DoD.
2. **Issue gate**
- Use `create-issue-gate` logic.
- If issue is not `ready` or execution gate is not `allowed`, stop immediately.
- Do not implement anything while issue remains `draft`.
3. **Execute**
- Hand off to `closed-loop-delivery` for implementation and local verification.
4. **Review loop**
- If PR feedback is relevant, batch polling windows as:
- wait `3m`
- then `6m`
- then `10m`
- After the `10m` round, stop waiting and process all visible comments together.
5. **Deploy and runtime verification**
- If DoD depends on runtime behavior, deploy only to `dev` by default.
- Verify with real logs/API/Lambda behavior, not assumptions.
6. **Completion gate**
- Before any claim of completion, require `verification-before-completion`.
- No success claim without fresh evidence.
## Stop Conditions
Move to `accepted` only when every acceptance criterion has matching evidence.
Move to `escalated` when any of these happen:
- DoD still fails after `2` full rounds
- missing secrets/permissions/external dependency blocks progress
- task needs production action or destructive operation approval
- review instructions conflict and cannot both be satisfied
## Human Gates
Always stop for human confirmation on:
- prod/stage deploys beyond agreed scope
- destructive git/data operations
- billing or security posture changes
- missing user-provided acceptance criteria
## Output Contract
When reporting status, always include:
- `Status`: intake / executing / accepted / escalated
- `Acceptance Criteria`: pass/fail checklist
- `Evidence`: commands, logs, API results, or runtime proof
- `Open Risks`: anything still uncertain
- `Need Human Input`: smallest next decision, if blocked
Do not report "done" unless status is `accepted`.
## Limitations
- Use this skill only when the task clearly matches the scope described above.
- Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
- Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.Related Skills
tdd-orchestrator
Master TDD orchestrator specializing in red-green-refactor discipline, multi-agent workflow coordination, and comprehensive test-driven development practices.
subagent-orchestrator
Coordinate quota-aware parallel subagents for large, multi-file Antigravity tasks.
social-orchestrator
Orquestrador unificado de canais sociais — coordena Instagram, Telegram e WhatsApp em um unico fluxo de trabalho. Publicacao cross-channel, metricas unificadas, reutilizacao de conteudo por formato, agendamento sincronizado e gestao centralizada de campanhas em todos os canais simultaneamente.
multi-agent-task-orchestrator
Route tasks to specialized AI agents with anti-duplication, quality gates, and 30-minute heartbeat monitoring
antigravity-skill-orchestrator
A meta-skill that understands task requirements, dynamically selects appropriate skills, tracks successful skill combinations using agent-memory-mcp, and prevents skill overuse for simple tasks.
agent-orchestrator
Meta-skill que orquestra todos os agentes do ecossistema. Scan automatico de skills, match por capacidades, coordenacao de workflows multi-skill e registry management.
zustand-store-ts
Create Zustand stores following established patterns with proper TypeScript types and middleware.
zoom-automation
Automate Zoom meeting creation, management, recordings, webinars, and participant tracking via Rube MCP (Composio). Always search tools first for current schemas.
zoho-crm-automation
Automate Zoho CRM tasks via Rube MCP (Composio): create/update records, search contacts, manage leads, and convert leads. Always search tools first for current schemas.
zod-validation-expert
Expert in Zod — TypeScript-first schema validation. Covers parsing, custom errors, refinements, type inference, and integration with React Hook Form, Next.js, and tRPC.
zipai-optimizer
Ultra-dense token optimizer skill for prompt caching, log pruning, AST-based inspection, and minified JSON payloads.
zeroize-audit
Detects missing zeroization of sensitive data in source code and identifies zeroization removed by compiler optimizations, with assembly-level analysis, and control-flow verification. Use for auditing C/C++/Rust code handling secrets, keys, passwords, or other sensitive data.