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 stefanoskarakasis/Product-Marketing-Skills --skill meta-learngit 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/skills/stefanoskarakasis/product-marketing-skills/meta-learn)<a href="https://agentmods.dev/skills/stefanoskarakasis/product-marketing-skills/meta-learn"><img src="https://agentmods.dev/badge/skills/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/skills/stefanoskarakasis/product-marketing-skills/meta-learn"><img src="https://agentmods.dev/badge/skills/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 | $0.00088 | $0.01823 |
| Opus 5.5 | $0.00035 | $0.00729 |
| Sonnet 5.5 | $0.00018 | $0.00365 |
| Haiku 4.5 | $0.00009 | $0.00182 |
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 7d 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 — 194 lines — stays where its author put it; the contents beside it link to each section on GitHub.
meta-learn
The deliberate, on-demand deep-dive into a completed skill session — for
when the automatic one-line Learning Close every T1/T2 skill already writes
isn't enough. Asks what the session actually taught the user — not what the
skill's output said, but what surprised them, what they disagreed with, or
what was missing. Turns real answers into a specific, falsifiable statement
and logs it to the same file, in the same format, every T1/T2 skill's own
Learning Close writes to — so meta-synthesis reads one consistent log
regardless of which produced any given row.
Trigger
-
When: The user wants a deeper capture of a completed skill session than the automatic one-line row every T1/T2 skill already logs at its own close (see that skill's Learning Close step). Use this for a session that deserves real reflection — something surprised the user, a recommendation felt wrong, or important context was missing — and a structured three-question interview is worth the extra few minutes.
-
Not for: Auditing a
SKILL.md's structure → usemeta-review. Checking whether a skill's output itself is correct → usemeta-verify. Detecting patterns across multiple sessions or proposing guardrails → usemeta-synthesis, which reads what this skill logs. -
Example prompts:
- "Capture what we learned from that retro"
- "Log this session"
- "What surprised you about that positioning run?"
- "Save the learnings before we close"
Inputs
- Args: The completed session's skill name and a short description of
its output.
N/Aif invoked immediately after a skill session in the same conversation — infer both from context. - Defaults: If the skill name isn't obvious, ask for it before proceeding.
- Context keys:
/context/skill-sessions.md— appended to, created if it doesn't exist yet.
Pre-flight
- This skill is context-agnostic. Do not load
/foundation/brain.mdand do not let prior company context shape what counts as a pattern — extraction comes from the user's answers alone. - If there's no completed session to close out (skill hasn't produced output yet), stop and say so — this isn't a mid-session check-in.
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 7d ago Changed · -4 lines · -44 tokens per session e75127eef983
- 15d ago Changed 941d838b9fec
- 1mo ago Changed · +2 lines 5df66b63a799
- 1mo ago First seen · 196 lines · 132 tokens per session scan A ba710171b050
meta-learn is a skill published in the GitHub repository stefanoskarakasis/Product-Marketing-Skills (8 stars, last pushed yesterday), licensed MIT. It adds 88 tokens to every session and 1,823 once invoked, about $0.0004 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.
Other skills, from other repositories
new-course
A guided course-building workflow that turns a subject into a structured course stored on disk. It surveys the subject, checks the learner's starting level, researches the topic, and organizes lessons into books and chapters.
self-assess
A placement quiz process that measures what someone can actually do in a subject and records the result in PROFILE.md.
learn
A learning-session command that starts a new course or continues an existing one from its saved progress. It checks course folders and begins the next lesson without rebuilding the plan.
new-book
A course-writing tool that turns a learning plan into chapters and teachable lessons. Each lesson includes a question, explanation, corrections, and the origins of important terms.
decode
A codebase guide that explains software to product managers in plain Persian, using a high-level story, a browsable file tree, and short file summaries.
learn-from-fix
Capture Elixir/Ecto/LiveView lessons and Hex API rules. Use after corrections or when asked to document learning, record a lesson, prevent a fixed mistake, or remember package guidance with --library.