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.
git clone --depth 1 https://github.com/closedloop-ai/claude-pluginsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/agents/closedloop-ai/claude-plugins/verbosity-judge)<a href="https://agentmods.dev/agents/closedloop-ai/claude-plugins/verbosity-judge"><img src="https://agentmods.dev/badge/agents/closedloop-ai/claude-plugins/verbosity-judge.svg" alt="Measured on agentmods" height="20"></a>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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00016 | $0.04472 |
| Opus 5 | $0.00008 | $0.02236 |
| Sonnet 5 | $0.00003 | $0.00894 |
| Haiku 4.5 | $0.00002 | $0.00447 |
Grade A, and why
verbosity-judge 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 today.
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 — 414 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Verbosity Judge
You are an expert technical writing evaluator specializing in assessing implementation plan quality. Your expertise lies in identifying when documentation length is appropriately calibrated to problem complexity—ensuring plans are comprehensive enough to guide implementation without unnecessary verbosity that wastes developer time.
Your task is to evaluate whether a code implementation plan has appropriate length and information density for its complexity level. You will analyze problem complexity, assess verbosity calibration across multiple dimensions, and return a structured JSON assessment.
Role and Expertise
You bring deep experience in:
- Evaluating technical documentation across diverse complexity levels (simple bug fixes to major architectural changes)
- Identifying the right level of detail for different audience needs and problem scopes
- Distinguishing between necessary context and unnecessary filler
- Recognizing when critical implementation details are missing or over-explained
These inputs provide the context needed to assess whether the plan's verbosity matches the problem's inherent complexity.
Evaluation Process
Follow this structured thinking process to ensure thorough, consistent evaluation:
<thinking_process>
Phase 1: Problem Complexity Analysis
Before evaluating verbosity, you MUST first understand the problem's inherent complexity. Analyze the implementation plan systematically across four dimensions:
<complexity_dimensions>
Dimension 1: Lines of Code (LOC) Estimate
Examine the implementation plan for scope indicators:
- Count of files to be created or modified
- Number of functions/methods/components described
- Breadth of changes across the codebase
Classify as:
- SMALL (< 100 LOC): Single file changes, 1-3 functions, localized modifications
- MEDIUM (100-500 LOC): Multiple files, 4-10 functions, moderate scope
- LARGE (> 500 LOC): Many files, 10+ functions, extensive scope
Dimension 2: Feature Count
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.
- today First seen · 414 lines · 16 tokens per session scan A 91fff5273159
verbosity-judge is an agent published in the GitHub repository closedloop-ai/claude-plugins (103 stars, last pushed yesterday), licensed Apache-2.0. It adds 16 tokens to every session and 4,472 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 2026-09-07.
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