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Acceptance orchestrator

Skill ranbot-ai/awesome-skills/skills/acceptance-orchestrator

Awesome Claude Skills, Tools for Customizing Claude AI workflows

Install
npx -y skills add ranbot-ai/awesome-skills --skill acceptance-orchestrator

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What its author says it does

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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.

SKILL.md

3.7 KB, 754 tokens by cl100k_base, as published. Nobody here has run it

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.

Gives 0 of the 12 instructions most agent orchestration skills give in 754 tokens

Counted across 742 of the 995 authors here whose files we hold, read 2026-08-06

  • run the full test suite after integrating changesin 53 of 742, across 20 files
  • reference existing artifacts by path or URLin 52 of 742, across 22 files
  • dispatch one agent per independent problem domainin 50 of 742, across 17 files
  • verify fixes do not conflictin 45 of 742, across 13 files
  • include a suggested skills section in the documentin 45 of 742, across 15 files
  • redact sensitive informationin 41 of 742, across 11 files
  • save to the temporary directory of the operating systemin 39 of 742, across 9 files
  • tailor the document to user-provided focus argumentsin 39 of 742, across 9 files
  • spot check agent changes for systematic errorsin 34 of 742, across 7 files
  • write a handoff document summarising the current conversationin 31 of 742, across 6 files
  • assign each agent a specific scopein 23 of 742, across 8 files
  • provide specific scope and clear goalin 23 of 742, across 5 files

Said here and by no other author read

  • read the issue and extract goals
  • require fresh evidence before completion
  • include pass/fail checklist in status reports
  • stop if success criteria are missing

Grouped from the skills themselves: near-identical wordings counted once, and counted by distinct author, so one author publishing three of these counts once. Length counted with cl100k_base; the agent that loads this file may tokenize it differently.

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