closed-loop-delivery

Use when a coding task must be completed against explicit acceptance criteria with minimal user re-intervention across implementation, review feedback, deployment, and runtime verification.

31,392 stars
Complexity: medium

About this skill

This skill empowers an AI agent to execute coding tasks with a robust, "closed-loop" development process. Instead of merely modifying code, the agent treats a task as incomplete until all explicit acceptance criteria are verified in evidence. It emphasizes delivering against a "Definition of Done" (DoD), not just code changes. This includes writing code and tests, addressing pull request feedback, deploying to a development environment, and performing runtime checks, all without requiring repeated manual prompts from the user. It aims to reduce friction and accelerate the delivery of verified code solutions, ensuring quality and completeness for every coding task.

Best use case

Ideal for software development scenarios where a high degree of confidence in the implemented solution is required, and where the AI agent needs to independently manage the entire delivery pipeline for a coding task. This includes feature implementation, bug fixes, refactoring, or any development effort that demands thorough verification beyond mere code changes.

Use when a coding task must be completed against explicit acceptance criteria with minimal user re-intervention across implementation, review feedback, deployment, and runtime verification.

A fully implemented, tested, deployed, and verified code solution that demonstrably meets all specified acceptance criteria, with the AI agent reporting completion only after successful runtime verification. Users can expect minimal interaction during the task lifecycle, receiving a production-ready or thoroughly validated outcome.

Practical example

Example input

Implement a new API endpoint `/api/v1/users` that returns a list of all active users. Ensure it includes pagination, is secured with an API key, and passes all unit and integration tests. Deploy to dev and confirm it works.

Example output

Task completed. The `/api/v1/users` endpoint has been implemented, tested, deployed to development, and runtime verified. Pagination and API key security are confirmed functional. All acceptance criteria met. (Agent's internal process included: code generation, test writing, PR creation/feedback simulation, dev deployment, and runtime validation checks).

When to use this skill

  • User gives a coding/fix task and expects end-to-end completion
  • Task spans code + tests + PR comments + dev deploy + runtime checks
  • Repeated manual prompts like "now test", "now deploy", "now re-check PR" should be avoided

When not to use this skill

  • When only a quick, unverified code snippet is needed without full integration or testing.
  • For tasks that do not have explicit acceptance criteria or a clear 'Definition of Done'.
  • In environments where the AI agent lacks the necessary permissions or access to perform deployment or runtime verification.
  • When the primary goal is rapid prototyping without a strong emphasis on production-readiness or deep verification.

Installation

Claude Code / Cursor / Codex

$curl -o ~/.claude/skills/closed-loop-delivery/SKILL.md --create-dirs "https://raw.githubusercontent.com/sickn33/antigravity-awesome-skills/main/plugins/antigravity-awesome-skills-claude/skills/closed-loop-delivery/SKILL.md"

Manual Installation

  1. Download SKILL.md from GitHub
  2. Place it in .claude/skills/closed-loop-delivery/SKILL.md inside your project
  3. Restart your AI agent — it will auto-discover the skill

How closed-loop-delivery Compares

Feature / Agentclosed-loop-deliveryStandard Approach
Platform SupportClaudeLimited / Varies
Context Awareness High Baseline
Installation ComplexitymediumN/A

Frequently Asked Questions

What does this skill do?

Use when a coding task must be completed against explicit acceptance criteria with minimal user re-intervention across implementation, review feedback, deployment, and runtime verification.

Which AI agents support this skill?

This skill is designed for Claude.

How difficult is it to install?

The installation complexity is rated as medium. You can find the installation instructions above.

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.

Related Guides

SKILL.md Source

# Closed-Loop Delivery

## Overview

Treat each task as incomplete until acceptance criteria are verified in evidence, not until code is merely changed.

Core rule: **deliver against DoD (Definition of Done), not against code diff size.**

## When to Use
Use this skill when:
- user gives a coding/fix task and expects end-to-end completion
- task spans code + tests + PR comments + dev deploy + runtime checks
- repeated manual prompts like "now test", "now deploy", "now re-check PR" should be avoided

Do not use this skill for:
- pure Q&A/explanations
- prod deploy requests without explicit human approval
- tasks blocked by missing secrets/account access that cannot be inferred

## Required Inputs

Before execution, define these once:
- task goal
- acceptance criteria (DoD)
- target environment (`dev` by default)
- max iteration rounds (default `2`)

If acceptance criteria are missing, request them once. If user does not provide, propose a concrete default and proceed.

## Issue Gate Dependency

Before execution, prefer using `create-issue-gate`.

- If issue status is `ready` and execution gate is `allowed`, continue.
- If issue status is `draft`, do not execute implementation/deploy/review loops.
- Require user-provided, testable acceptance criteria before starting execution.

## Default Workflow

1. **Define DoD**
   - Convert request into testable criteria.
   - Example: checkout task DoD = "checkout endpoint returns a valid, openable third-party payment URL in dev".

2. **Implement minimal change**
   - Keep scope tight to task goal.

3. **Verify locally**
   - Run focused tests first, then broader checks if needed.

4. **Review loop**
   - Fetch PR comments/reviews.
   - Classify valid vs non-actionable.
   - Fix valid items, re-run verification.

5. **Dev deploy + runtime verification**
   - Deploy to `dev` when runtime behavior matters.
   - Verify via real API/Lambda/log evidence against DoD.

6. **Completion decision**
   - Only report "done" when all DoD checks pass.
   - Otherwise continue loop until pass or stop condition.

## PR Comment Polling Policy

Avoid noisy short polling by default. Use batched windows:

- **Round 1:** wait `3m`, collect delta comments/reviews
- **Round 2:** wait `6m`, collect delta again
- **Final round:** wait `10m`, collect all remaining visible comments/reviews

At each round:
- process all new comments in one batch
- avoid immediate re-poll after each single comment
- after the `10m` round, stop waiting and proceed with all comments visible at that point

If CI is still running, align polling to check completion boundaries instead of fixed rapid polling.

## Human Gate Rules (Must Ask)

Require explicit user confirmation for:
- production/staging deploy beyond agreed scope
- destructive operations (history rewrite, force push, data-destructive ops)
- actions with billing/security posture changes
- secret values not available in repo/runtime
- ambiguous DoD that materially changes outcome

## Iteration/Stop Conditions

Stop and escalate with a concise blocker report when:
- DoD still fails after max rounds (`2` default)
- external dependency blocks progress (provider outage, missing creds, account permission)
- conflicting review instructions cannot both be satisfied

Escalation report must include:
- what passed
- what failed
- evidence (commands/logs/API result)
- smallest decision needed from user

## Output Contract

When claiming completion, always include:
- acceptance criteria checklist with pass/fail
- commands/tests run
- runtime evidence (endpoint/Lambda/log key lines)
- PR status (new actionable comments count)

Do not claim success without evidence.

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