score-findings

Score and verify code review findings against repository code.

Updated Sep 9, 2024
One-click install
npx skills add https://github.com/axel-kaliff/dotfiles --skill score-findings
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: score-findings
Source: https://github.com/axel-kaliff/dotfiles/tree/main/claude/.claude/skills/score-findings
Command: npx skills add https://github.com/axel-kaliff/dotfiles --skill score-findings

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Pull requests and automated review tools generate many findings, including duplicates, vague suggestions, and false positives, which waste reviewer time and slow merges. This Skill automates verification against the actual repository code, filters out low-value findings, and prioritizes actionable fixes so reviewers can focus on real regressions.

Core Features & Use Cases

  • Category-aware deduplication: Merge findings by file:line:category and surface possible shared root causes when multiple related findings appear in the same function.
  • Parallel, batched verification: Spawn parallel Sonnet agents to read code context, compare branch vs origin/master, and evaluate logic or semantic regressions with up to five findings per agent.
  • Deterministic scoring and filtering: Assign 0-100 scores based on accuracy and actionability and return only findings >=50 with a breakdown of filtered items for prompt tuning and triage.
  • Use case: Run as part of pre-merge checks to automatically verify and prioritize code-review findings from multiple analysis agents.

Quick Start

Score the provided list of findings for the current branch and return the high-confidence findings along with counts of filtered results.

Frequently Asked Questions about score-findings

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

FAQPage Schema
How do I automatically verify code review findings against actual repository code?

You can automatically verify code review findings by spawning parallel Sonnet agents that read code context, compare branches against origin/master, and evaluate semantic regressions to filter out false positives.

How does automated deduplication of code review findings work?

Automated deduplication merges code review findings by file, line, and category, then surfaces possible shared root causes when multiple related findings appear in the same function or code block.

Can I batch verify pull request findings before merging?

Yes, you can batch verify pull request findings by spawning parallel Sonnet agents that process up to five findings per agent while keeping file context together for pre-merge checks.

What is the best way to score and filter low-value code review suggestions?

Score and filter low-value code review suggestions by assigning a 0-100 rating based on accuracy and actionability, returning only findings scoring 50 or above along with a breakdown of filtered items.

How do I detect pre-existing code changes before scoring review findings?

Detect pre-existing code changes by using git to compare the current branch against origin/master before evaluating code review findings, ensuring automated scoring only accounts for new regressions.

Why are my automated code review tools generating duplicate and false positive findings?

Automated code review tools generate duplicates and false positives because they lack semantic context, which parallel Sonnet agents can resolve by reading actual repository code and applying deterministic scoring.