csdlc-review

Review sub-agent outputs against acceptance criteria and quality standards.

Updated Apr 10, 2026
One-click install
npx skills add https://github.com/danhannah94/claymore-plugins --skill csdlc-review
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: csdlc-review
Source: https://github.com/danhannah94/claymore-plugins/tree/main/csdlc/skills/csdlc-review
Command: npx skills add https://github.com/danhannah94/claymore-plugins --skill csdlc-review

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Sub-agent outputs in complex CSDLC workflows often require a consistent, formal review before human stakeholders act. This skill provides an AI Lead-style evaluation against acceptance criteria and quality standards to guard against scope creep and quality gaps.

Core Features & Use Cases

  • Structured checklist: Applies a formal Scope, Quality, Verification, and Acceptance Criteria review to sub-agent outputs.
  • Evidence-driven verdicts: Captures pass/fail with explicit evidence and suggested improvements.
  • Foundry annotation: Writes a review annotation back to the story/doc to facilitate handoffs and async human review.

Quick Start

Initiate an AI Lead review on the given sub-agent output and generate a structured verdict using the built-in checklist.

Frequently Asked Questions about csdlc-review

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I automate PR review for sub-agent outputs against acceptance criteria?▼

You can automate PR review by running an AI Lead evaluation that checks sub-agent outputs against scope, quality, verification, and acceptance criteria. It generates a structured pass/fail report with evidence and suggested improvements.

What is an AI Lead review in the CSDLC workflow?▼

An AI Lead review in the CSDLC workflow is a formal evaluation of sub-agent outputs against acceptance criteria and quality standards. It guards against scope creep and quality gaps before human stakeholders act.

How do I validate Foundry annotations for design artifacts and implementation stories?▼

You validate Foundry annotations by applying a structured checklist to design artifacts and implementation stories. The review captures pass/fail verdicts with explicit evidence and writes a Foundry annotation back to facilitate human handoffs.

Can I use this AI review for scope creep and quality gap checks on pull requests?▼

Yes, you can use this AI review for pull requests. It enforces scope, quality, verification, and acceptance criteria checks on PRs, generating a structured report to guard against scope creep and quality gaps.

Does this automated QA review work without external dependencies?▼

Yes, this automated QA review works without external dependencies. It operates independently to perform formal scope, quality, verification, and acceptance criteria evaluations on sub-agent outputs.

What's the best way to generate structured pass/fail verdicts with evidence for async human review?▼

The best way to generate structured verdicts is performing an AI Lead review that captures pass/fail status with explicit evidence and suggested improvements. It creates a Foundry annotation on the story or doc to facilitate async human review.