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/stefanoskarakasis/Product-Marketing-SkillsWrote 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/stefanoskarakasis/product-marketing-skills/meta-learn)<a href="https://agentmods.dev/commands/stefanoskarakasis/product-marketing-skills/meta-learn"><img src="https://agentmods.dev/badge/commands/stefanoskarakasis/product-marketing-skills/meta-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/stefanoskarakasis/product-marketing-skills/meta-learn"><img src="https://agentmods.dev/badge/commands/stefanoskarakasis/product-marketing-skills/meta-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 | $0.00010 | $0.00146 |
| Opus 5.5 | $0.00004 | $0.00058 |
| Sonnet 5.5 | $0.00002 | $0.00029 |
| Haiku 4.5 | $0.00001 | $0.00015 |
Grade A, and why
meta-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.
What it actually says
/pmm-meta:meta-learn -- Post-Session Learning
Capture patterns from a completed skill session and route them to the correct knowledge files so intelligence compounds across every future session.
Invocation
/pmm-meta:meta-learn Extract learnings from the last retro
/pmm-meta:meta-learn What's been captured so far?
Workflow
Uses the meta-learn skill. Reads the session output, identifies any
pattern that's appeared before, promotes confirmed hypotheses to rules,
and updates the shared knowledge base other skills read from.
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 · 24 lines · 10 tokens per session scan A 4809c0da3edf
meta-learn is a command published in the GitHub repository stefanoskarakasis/Product-Marketing-Skills (8 stars, last pushed yesterday), licensed MIT. It adds 10 tokens to every session and 146 once invoked, about $0.0000 per session on Opus 5.5. 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 2026-08-31.
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