Verified:GitHub

aidlc-review

Reviews AI-DLC documentation and GitLab merge requests with sub-agent analysis and confidence scoring.

Updated Jul 22, 2026

Search Tags:#code review #acceptance criteria #jira #gitlab #merge request #peer review #ai-dlc

One-click install

npx skills add https://github.com/Trigent-Software-Pvt-Ltd/trigen-aidlc --skill aidlc-review-trigent-software-pvt-ltd

Or copy as Structured Prompt for Agent▼

Please help me install this Agent Skill.
Skill: aidlc-review
Source: https://github.com/Trigent-Software-Pvt-Ltd/trigen-aidlc/tree/main/plugins/aidlc/skills/aidlc-review
Command: npx skills add https://github.com/Trigent-Software-Pvt-Ltd/trigen-aidlc --skill aidlc-review-trigent-software-pvt-ltd

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve? Teams using AI-DLC workflows need rigorous peer review of planning documents and sprint implementations, but manual review of epics, design docs, and merge requests across GitLab, Linear, and Confluence is slow and inconsistent. ## Core Features & Use Cases - Documentation Review: Scores AI-DLC documents (features, epics, designs, tasks) on completeness, quality, accuracy, ambiguity, and open questions using parallel review sub-agents. - Implementation Review: Peer-reviews merge requests against Jira or Linear acceptance criteria, producing AC-to-test coverage maps, test gap analysis, and severity-ranked findings. - Multi-Backend Support: Detects and fetches content from GitLab markdown files, Linear initiatives/issues, or Confluence pages and Jira tickets. - Use Case: A tech lead asks to review the MR for PROJ-123; the skill fetches the sprint's acceptance criteria and MR diff, spawns review sub-agents, and returns a weighted confidence score with blocking findings and missing test coverage. ## Quick Start Ask the assistant to review the merge request for Jira sprint PROJ-123 and validate it against the sprint's acceptance criteria.

Frequently Asked Questions about aidlc-review

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

FAQPage Schema

How do I review a GitLab merge request against Jira acceptance criteria?▼

Provide the Jira sprint key (e.g., PROJ-123) and the MR link. The skill fetches the sprint's acceptance criteria via acli and the MR diff via glab, then spawns review sub-agents that map each criterion to test evidence and score requirements fit, code quality, testing, and security.

How do I review AI-DLC planning documents for completeness?▼

Provide a GitLab feature branch, Linear initiative URL, or Confluence page links. The skill detects the backend, fetches all related documents, and scores each on completeness, quality, accuracy, ambiguity risk, and open questions with a weighted overall confidence score.

Does the review skill support Linear and Confluence or only GitLab?▼

It supports GitLab markdown files, Linear initiatives/projects/issues, and Confluence pages with Jira tickets. The backend is detected automatically from the artifacts you provide, and content is fetched via CLI tools or MCP servers.

Can review findings be posted back to GitLab or Jira automatically?▼

Yes, after presenting the report the skill offers to post findings as inline MR comments via glab or gh, and summary comments on the Jira sprint or Linear issue. You can also discuss findings first and revise them before posting.

What happens if glab or acli CLI tools are not installed?▼

The skill falls back to MCP tools for GitLab, GitHub, Atlassian, or Linear. If neither CLI nor MCP is available, it asks you to paste the document content or MR diff manually.

What are the limitations when reviewing very large merge requests?▼

If an MR is too large for context, the skill asks you to specify which files or components to focus on and can split the review into multiple passes. Missing acceptance criteria are flagged as a blocking finding since requirements fit cannot be assessed.