Closed Loop Delivery

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.

AGI Framework Integration

Adapted for @techwavedev/agi-agent-kit Original source: antigravity-awesome-skills

Memory-First Protocol

Retrieve prior deployment configurations, rollback procedures, and incident post-mortems. Avoid re-discovering infrastructure patterns.

# Check for prior infrastructure context before starting
python3 execution/memory_manager.py auto --query "deployment configuration and patterns for Closed Loop Delivery"

Storing Results

After completing work, store infrastructure decisions for future sessions:

python3 execution/memory_manager.py store \
  --content "Deployment pipeline: configured blue-green deployment with health checks on port 8080" \
  --type technical --project <project> \
  --tags closed-loop-delivery devops

Multi-Agent Collaboration

Broadcast deployment changes so frontend and backend agents update their configurations accordingly.

python3 execution/cross_agent_context.py store \
  --agent "<your-agent>" \
  --action "Deployed infrastructure changes — updated CI/CD pipeline with new health check endpoints" \
  --project <project>

Playbook Integration

Use the ship-saas-mvp or full-stack-deploy playbook to sequence this skill with testing, documentation, and deployment verification.