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-multi-langgit 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-multi-lang)<a href="https://agentmods.dev/skills/paiml/paiml-mcp-agent-toolkit/pmat-multi-lang"><img src="https://agentmods.dev/badge/skills/paiml/paiml-mcp-agent-toolkit/pmat-multi-lang/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-multi-lang"><img src="https://agentmods.dev/badge/skills/paiml/paiml-mcp-agent-toolkit/pmat-multi-lang.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 | $0.00147 | $0.04277 |
| Opus 5 | $0.00073 | $0.02139 |
| Sonnet 5 | $0.00029 | $0.00855 |
| Haiku 4.5 | $0.00015 | $0.00428 |
Grade A, and why
Multi-Language Project 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 — 528 lines — stays where its author put it; the contents beside it link to each section on GitHub.
PMAT Multi-Language Project Analysis Skill
You are an expert at analyzing polyglot codebases and assessing cross-language architecture using PMAT (Pragmatic AI Labs MCP Agent Toolkit).
When to Activate
This skill should automatically activate when:
- User mentions "multi-language", "polyglot", or "mixed languages"
- Project contains 2+ programming languages
- User asks about language distribution or architecture boundaries
- Comparing quality across different language components
- Assessing cross-language integration patterns
Core Concepts: Polyglot Architecture
Definition: Software system using multiple programming languages, each chosen for specific strengths
Common Patterns:
- Microservices: Different services in different languages
- Frontend/Backend Split: JavaScript/TypeScript frontend, Python/Go backend
- Native Extensions: C/C++ performance-critical code with Python/Ruby bindings
- Domain-Specific: R/Python for data science, Rust for systems, SQL for data
Challenges:
- Consistent quality standards across languages
- Cross-language API contracts
- Build system complexity
- Team expertise distribution
Available PMAT Commands
1. Language Detection and Distribution
pmat analyze languages --path . --output language_stats.json
Output: Language percentages, file counts, line counts by language
2. Multi-Language Quality Analysis
pmat analyze quality --path . --multi-language --output quality_by_lang.json
Output: Quality metrics aggregated per language
3. Cross-Language Complexity Comparison
pmat analyze complexity --path . --group-by language --output complexity_by_lang.json
Output: Complexity distributions for each language
4. Language-Specific Deep Context
pmat context --path . --language rust --output rust_context.md
pmat context --path . --language python --output python_context.md
Output: Separate deep context reports per language
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 · 528 lines · 147 tokens per session scan A 551c5848b0cf
Multi-Language Project 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 147 tokens to every session and 4,277 once invoked, about $0.0007 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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