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 agentmods add commands/airmcp-com/mcp-standards/pattern-learngit 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.svg" alt="Measured on agentmods" 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 | $0.00000 | $0.00132 |
| Opus 5 | $0.00000 | $0.00066 |
| Sonnet 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 yesterday.
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 — 0 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.
- yesterday 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 9mo 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 0 lines, and is treated as a copy.
Other commands, from other repositories
merge
Finish a PR properly: every check green, every review addressed — human and bot — then merge and clean up.
pr
Prepare and open a pull request the senior way: gate, template, scrubbed, everything visible.
spec
Spec-first design: a gap-closing interview that produces a complete spec, with a quality controller that blocks until every section is answered and every question resolved.
debug
Systematic debugging: root cause before any fix, one hypothesis at a time, and a three-strikes rule that questions the architecture instead of stacking patches.
index
The code index: build it, then find, search, refs, outline, and impact instead of grepping blind.
plan
Turn an approved spec into an implementation plan an engineer with zero context could execute — with a quality controller that blocks placeholders and hollow tasks.