lfx-self-serve-learnings-review

Audits commits against an empirical pattern knowledge base extracted from past PR review comments.

12|6|Updated Jul 14, 2025
One-click install
npx skills add https://github.com/linuxfoundation/lfx-self-serve --skill lfx-self-serve-learnings-review-linuxfoundation
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: lfx-self-serve-learnings-review
Source: https://github.com/linuxfoundation/lfx-self-serve/tree/main/.claude/skills/lfx-self-serve-learnings-review
Command: npx skills add https://github.com/linuxfoundation/lfx-self-serve --skill lfx-self-serve-learnings-review-linuxfoundation

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve? Code review intuition is often generic and misses the specific mistakes that reviewers have repeatedly flagged on a repository. This Skill audits the latest commit (or full branch diff) in the lfx-self-serve repo against a knowledge base of patterns extracted from real PR review comments, so every finding is grounded in an actual documented rule rather than guesswork. ## Core Features & Use Cases - KB-gated findings: Every finding must quote a pattern entry's rule ID and a phrase from its Pattern or Detect clause; unsourced findings are dropped automatically. - Routed pattern loading: Reads only the knowledge-base files relevant to the changed file paths (TypeScript correctness, templates and accessibility, server request handling, observability, Snowflake data access, and more), avoiding wasted context. - False-positive suppression: Applies a known-false-positives list after matching so documented non-issues are removed even when a pattern matches. - Two audit modes: Default mode reviews the latest commit; passing the keyword branch audits the full branch diff against origin/main for the pre-PR sweep. - Use Case: After committing a change to an Angular component and its service in lfx-self-serve, invoke this Skill to receive a markdown report of Critical and Important findings, each citing file, line, confidence score, the KB rule it matched, and the recommended fix. ## Quick Start Ask the AI to run the lfx-self-serve learnings review on the latest commit, optionally adding the keyword branch for a full-branch audit before opening a pull request.

Frequently Asked Questions about lfx-self-serve-learnings-review

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

FAQPage Schema
How do I review my latest commit against past PR review feedback?▼

Invoke the Skill after committing in the lfx-self-serve repo. It runs git show on HEAD, loads the relevant knowledge-base pattern files for the changed paths, and emits only findings that quote a documented pattern entry.

How do I audit a full branch diff before opening a pull request?▼

Pass the keyword branch when invoking the Skill. It switches to full-branch mode, fetching origin and diffing origin/main...HEAD so everything the branch adds is audited in one pre-PR sweep.

What is the difference between lfx-self-serve-learnings-review and lfx-self-serve-code-review?▼

The learnings review covers the empirical surface: patterns actually flagged by bots and human reviewers, stored in docs/reviews/knowledge-base. The code review audits the documented rule surface such as Angular structure, repo rule files, and architecture checklists. They are designed to run in parallel.

Why did the review drop a finding that looked like a real issue?▼

Findings are gated by knowledge-base matches: any finding that cannot quote a pattern entry's rule ID and Pattern or Detect phrase is dropped. Findings are also removed if they match an entry in known-false-positives.md, which takes precedence over pattern matches.

Can I run this review from outside the lfx-self-serve repository?▼

Yes. The Skill can be launched from the LFX workspace root or a multi-repo session and will locate the lfx-self-serve repo as a sibling or child directory. If the repo cannot be found, it aborts with an INCOMPLETE status.

What do the confidence scores in the review report mean?▼

Confidence is derived from each knowledge-base entry's severity header: Critical maps to 90-100, Important to 80-89, and Nit below 80. Findings under 80 are suppressed, so the report only shows Critical and Important groups.