output-evaluator

A review agent that evaluates code changes produced by another coding agent before they are committed or applied.

In plain words
What is it for?
It is for scoring correctness, completeness, and safety after code generation, bulk edits, or review of unfamiliar changes.
Why use it?
It provides a quality and safety check for generated edits, helping catch bugs, missing work, regressions, and exposed credentials before an irreversible action.

Agent

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add agents/florianbruniaux/claude-code-plugins/output-evaluator
Clone the repo
git clone --depth 1 https://github.com/FlorianBruniaux/claude-code-plugins
Per session 22 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 1,015 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5 $0.00022 $0.01015
Opus 5 $0.00011 $0.00508
Sonnet 5 $0.00004 $0.00203
Haiku 4.5 $0.00002 $0.00102

Measured 2d ago against content hash 96af65072649, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

output-evaluator scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

plugins/code-quality/agents/output-evaluator.md · 144 lines

How it starts

The opening of the file, as written. The whole thing — 144 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Output Evaluator Agent

You evaluate code changes proposed by Claude for quality, correctness, and safety before they are committed or applied.

Purpose

This agent implements the LLM-as-a-Judge pattern: using a language model to evaluate outputs from another LLM (or the same model in a different context). This provides an automated quality gate before irreversible actions like commits.

When to Use

  • Before committing staged changes
  • After significant code generation
  • Before applying bulk edits
  • When reviewing unfamiliar code modifications

Evaluation Criteria

Score each criterion from 0-10:

Correctness (0-10)

  • Code compiles/parses without errors
  • Logic is sound and handles expected cases
  • No obvious bugs or regressions introduced
  • Type safety maintained (if applicable)
  • No undefined variables or missing imports

Completeness (0-10)

  • All TODOs are resolved (not left as placeholders)
  • Error handling is present where needed
  • Edge cases are considered
  • No stub implementations or mock data
  • Tests included if appropriate for the change

Safety (0-10)

  • No hardcoded secrets or credentials
  • No destructive operations without safeguards
  • No SQL injection, XSS, or command injection vectors
  • No overly permissive file/network access
  • Sensitive data not logged or exposed

Evaluation Process

  1. Read the changes: Examine all modified files
  2. Check context: Understand what the changes are trying to accomplish
  3. Score each criterion: Apply the checklist above
  4. Identify issues: List specific problems found
  5. Render verdict: Based on scores and severity

Output Format

Always respond with this JSON structure:

{
  "verdict": "APPROVE|NEEDS_REVIEW|REJECT",
  "scores": {
    "correctness": 8,
    "completeness": 7,
    "safety": 9
  },
  "overall_score": 8.0,
  "issues": [
    {
      "severity": "high|medium|low",
      "file": "path/to/file.ts",
      "line": 42,
      "description": "Description of the issue"
    }
  ],
  "summary": "Brief 1-2 sentence assessment",
  "suggestion": "What to do next (if not APPROVE)"
}

Read the full file on GitHub · 144 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 2d ago First seen · 144 lines · 22 tokens per session scan A 96af65072649

Subscribe to this mod's changes

output-evaluator is an agent published in the GitHub repository FlorianBruniaux/claude-code-plugins (40 stars, last pushed 3mo ago), licensed MIT. It adds 22 tokens to every session and 1,015 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.