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.
npx agentmods add agents/florianbruniaux/claude-code-plugins/output-evaluatorgit clone --depth 1 https://github.com/FlorianBruniaux/claude-code-pluginsWhat 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.
| Model | Per session | Once 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 |
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.
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
- Read the changes: Examine all modified files
- Check context: Understand what the changes are trying to accomplish
- Score each criterion: Apply the checklist above
- Identify issues: List specific problems found
- 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)"
}
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.
- 2d ago First seen · 144 lines · 22 tokens per session scan A 96af65072649
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.
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