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/TeamManagementPlugin/claude-pluginWrote 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/commands/teammanagementplugin/claude-plugin/clean-check)<a href="https://agentmods.dev/commands/teammanagementplugin/claude-plugin/clean-check"><img src="https://agentmods.dev/badge/commands/teammanagementplugin/claude-plugin/clean-check/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/commands/teammanagementplugin/claude-plugin/clean-check"><img src="https://agentmods.dev/badge/commands/teammanagementplugin/claude-plugin/clean-check.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00027 | $0.00318 |
| Opus 5.5 | $0.00011 | $0.00127 |
| Sonnet 5.5 | $0.00005 | $0.00064 |
| Haiku 4.5 | $0.00003 | $0.00032 |
Grade A, and why
clean-check 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 1mo 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.
What it actually says
Code Cleanliness Check
Delegate a read-only cleanliness analysis of the target code to the code-cleanliness agent. The agent reports findings only — it never modifies files.
Arguments
$ARGUMENTS:
- A file path, directory path, or glob pattern to analyze.
- Optional
--focus=to limit analysis to specific categories (comma-separated).
Usage
/team-management:clean-check src/utils.py
/team-management:clean-check src/**/*.ts
/team-management:clean-check lib/ --focus=unused,naming
/team-management:clean-check . --focus=comments,complexity
Focus Categories
unused, comments, formatting, naming, imports, duplication, magic, complexity, nesting, types, dead — see the code-cleanliness agent for the definition of each.
Invoke the code-cleanliness agent via the Task tool (subagent_type: "code-cleanliness") with:
Target: $ARGUMENTS
Analyze the specified code for cleanliness issues. Produce the standard report with severity classifications, exact file:line references, and actionable fix examples. If
--focus=is specified, limit analysis to those categories only.
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.
- 1mo ago First seen · 36 lines · 27 tokens per session scan A 2d6d5e60864a
clean-check is a command published in the GitHub repository TeamManagementPlugin/claude-plugin (6 stars, last pushed 2d ago), licensed MIT. It adds 27 tokens to every session and 318 once invoked, about $0.0001 per session on Opus 5.5. 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 2026-08-31.
Other commands, from other repositories
dream
Overnight autoresearch + maintainer sweep that surfaces a MORNING REPORT — suggested changes, issues raised with fixes, and kept improvements. Shadow-first; never auto-pushes. Runs on-demand or scheduled for off-hours.
pr
Open a Pull Request on GitHub following team conventions. Draft by default.
review
Adversarial staff-level review of a System Design Doc. Interrogates the 10 staff questions, finds cost and failure risks, returns a verdict. Sensors and evals gate.
p5-review-sprint
Runs ONE final code review over the entire sprint at sprint end, with a stronger model, to catch issues that single-story reviews structurally cannot see: cross-story interactions, the schema/data model as a whole, conformance to ADRs and Constitution Inviolables, and consistency with prior lessons/instincts.
p6-audit-sast
Checks source code for injection vulnerabilities, insecure cryptography, secrets in code, and insecure error handling.
p5-review
Conducts a structured code review: first code quality, then security. Each dimension is its own sub-command.