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/airmcp-com/mcp-standardsWrote 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/airmcp-com/mcp-standards/pattern-learn)<a href="https://agentmods.dev/commands/airmcp-com/mcp-standards/pattern-learn"><img src="https://agentmods.dev/badge/commands/airmcp-com/mcp-standards/pattern-learn/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/airmcp-com/mcp-standards/pattern-learn"><img src="https://agentmods.dev/badge/commands/airmcp-com/mcp-standards/pattern-learn.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 | $0.00000 | $0.00132 |
| Opus 5.5 | $0.00000 | $0.00053 |
| Sonnet 5.5 | $0.00000 | $0.00026 |
| Haiku 4.5 | $0.00000 | $0.00013 |
Grade A, and why
pattern-learn 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.
This is a copy
100% identical to pattern-learn — 50 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
What it actually says
pattern-learn
Learn patterns from successful operations.
Usage
npx claude-flow training pattern-learn [options]
Options
--source <type>- Pattern source--threshold <score>- Success threshold--save <name>- Save pattern set
Examples
# Learn from all ops
npx claude-flow training pattern-learn
# High success only
npx claude-flow training pattern-learn --threshold 0.9
# Save patterns
npx claude-flow training pattern-learn --save optimal-patterns
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 · 26 lines · 0 tokens per session scan A 2bc9784010fc
pattern-learn is a command published in the GitHub repository airmcp-com/mcp-standards (3 stars, last pushed 10mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 132 tokens. A static security scan graded it A with 0 findings. It is 100% identical to pattern-learn, differing in 50 lines, and is treated as a copy.
Other commands, from other repositories
audit-agents-skills
Audit quality of agents, skills, and commands in a Claude Code project.
workflow-classify
Classify task size (S/M/L/XL) and recommend the appropriate workflow pipeline.
security-audit
Comprehensive security audit of your project AND Claude Code configuration. Analyzes secrets exposure, injection surfaces, dependencies, hook security, and produces a scored security posture assessment.
research-verify
Verify existing research findings against independent primary sources. Upgrades confidence from 'sources agree' to 'independently verified.'.
init-docs
Fetch official Anthropic Claude Code docs and store as local baseline snapshot.
init
Install the formatters this repository needs, with every command visible before it runs.