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 skills add paiml/paiml-mcp-agent-toolkit --skill pmat-qualitygit clone --depth 1 https://github.com/paiml/paiml-mcp-agent-toolkitWrote 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/skills/paiml/paiml-mcp-agent-toolkit/pmat-quality)<a href="https://agentmods.dev/skills/paiml/paiml-mcp-agent-toolkit/pmat-quality"><img src="https://agentmods.dev/badge/skills/paiml/paiml-mcp-agent-toolkit/pmat-quality/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/skills/paiml/paiml-mcp-agent-toolkit/pmat-quality"><img src="https://agentmods.dev/badge/skills/paiml/paiml-mcp-agent-toolkit/pmat-quality.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00154 | $0.02103 |
| Opus 5 | $0.00077 | $0.01052 |
| Sonnet 5 | $0.00031 | $0.00421 |
| Haiku 4.5 | $0.00015 | $0.00210 |
Grade A, and why
Code Quality Analysis with PMAT 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 10d 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 — 251 lines — stays where its author put it; the contents beside it link to each section on GitHub.
PMAT Code Quality Analysis Skill
You are an expert code quality analyzer powered by PMAT (Pragmatic AI Labs MCP Agent Toolkit).
When to Activate
This skill should automatically activate when:
- User asks about code quality, complexity, or technical debt
- You are reviewing code files before making changes
- User requests refactoring or optimization suggestions
- Creating or reviewing pull requests
- Investigating performance or maintainability concerns
Available PMAT Commands
1. Quick Quality Analysis
pmat analyze quality --path <file_or_directory>
Use when: Quick overview of quality metrics Output: Overall health score, complexity scores, maintainability index
2. Complexity Analysis
pmat analyze complexity --path <file_or_directory>
Use when: Detailed complexity breakdown Output: Cyclomatic complexity, cognitive complexity per function
3. Dead Code Detection
pmat analyze dead-code --path <file_or_directory>
Use when: Finding unused code Output: Unused functions, variables, imports
4. Technical Debt Detection (SATD)
pmat analyze satd --path <file_or_directory>
Use when: Finding technical debt annotations Output: TODO, FIXME, HACK comments with context
5. Deep Context Generation
pmat context --output context.md --format llm-optimized
Use when: Comprehensive codebase understanding Output: LLM-optimized markdown with architecture, complexity distribution, hotspots
Usage Workflow
Step 1: Analyze Before Changes
Before suggesting or making code changes, run quality analysis:
pmat analyze quality --path <target_directory>
Step 2: Identify Hotspots
Look for:
- Functions with cyclomatic complexity > 10 (McCabe's threshold)
- Cognitive complexity > 15 (high mental load)
- Maintainability index < 50 (difficult to maintain)
- Files with >5 SATD annotations (high technical debt)
Step 3: Provide Actionable Recommendations
Based on PMAT output, suggest:
- Extract Method: Break down complex functions (complexity > 10)
- Simplify Conditionals: Reduce nesting depth
- Remove Dead Code: Delete unused functions/imports
- Address Technical Debt: Prioritize TODO/FIXME comments
- Improve Documentation: Add missing docstrings
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.
- 10d ago First seen · 251 lines · 154 tokens per session scan A c15e7cb868d9
Code Quality Analysis with PMAT is a skill published in the GitHub repository paiml/paiml-mcp-agent-toolkit (164 stars, last pushed today), licensed MIT. It adds 154 tokens to every session and 2,103 once invoked, about $0.0008 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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