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 JanoshMoshiri/MarkdownLLM --skill compliance-patterns-specificationgit clone --depth 1 https://github.com/JanoshMoshiri/MarkdownLLMWrote 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/janoshmoshiri/markdownllm/compliance-patterns-specification)<a href="https://agentmods.dev/skills/janoshmoshiri/markdownllm/compliance-patterns-specification"><img src="https://agentmods.dev/badge/skills/janoshmoshiri/markdownllm/compliance-patterns-specification/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/janoshmoshiri/markdownllm/compliance-patterns-specification"><img src="https://agentmods.dev/badge/skills/janoshmoshiri/markdownllm/compliance-patterns-specification.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 | $0.00016 | $0.01583 |
| Opus 5 | $0.00008 | $0.00792 |
| Sonnet 5 | $0.00003 | $0.00317 |
| Haiku 4.5 | $0.00002 | $0.00158 |
Grade A, and why
Compliance Patterns Specification 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 9d 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 — 193 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Compliance Patterns Specification
What This Domain Is
A collection of example things demonstrating compliance patterns, multi-lens reasoning, and regulatory best practices for domains operating under constraints (GDPR, HIPAA, financial regulations, etc.).
This is not a compliance domain itself. It's a reference library for domain builders creating regulated systems.
Philosophy: Why Examples Work for Compliance
Traditional compliance approaches:
- Write rules and hope the system follows them
- Rely on checklists to prevent violations
- Separate "compliance team" from "engineering team"
- End up with technical debt and audit friction
Better approach: Make compliance verifiable through examples.
LLMs excel at verifiable reasoning—tasks with clear right/wrong answers. Compliance is inherently verifiable:
- ✓ Data classified or not classified
- ✓ Access logged or not logged
- ✓ Data residency UK or non-UK
- ✓ Retention policy followed or violated
By providing both positive examples (compliant patterns) and negative examples (violations with explanations), we transform compliance from abstract rules into verifiable patterns. The LLM learns not just "what to do" but "why this is right and that is wrong."
Contrast Creates Clarity
Showing both the correct pattern AND the violation pattern (with consequences) gives the LLM two concrete reference points. This is why anti-patterns paired with remediation are more effective than rules alone—the LLM can verify its reasoning against both.
Core Principles
Verifiable Compliance: Every compliance decision should be reducible to verifiable facts, not subjective interpretation.
Multi-Lens Reasoning: Every significant decision should be evaluated through domain logic, compliance logic, and audit logic.
Explicit Conflicts: When lenses conflict, surface the conflict for human resolution rather than hiding it in logic.
Pattern-Based Learning: LLMs learn from examples; compliance becomes reinforced through every decision, not imposed as external constraints.
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.
- 9d ago First seen · 193 lines · 16 tokens per session scan A df8857f668a6
Compliance Patterns Specification is a skill published in the GitHub repository JanoshMoshiri/MarkdownLLM (5 stars, last pushed 2d ago), licensed MIT. It adds 16 tokens to every session and 1,583 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 2026-09-04.
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