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-synthesis)<a href="https://agentmods.dev/commands/stefanoskarakasis/product-marketing-skills/meta-synthesis"><img src="https://agentmods.dev/badge/commands/stefanoskarakasis/product-marketing-skills/meta-synthesis/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-synthesis"><img src="https://agentmods.dev/badge/commands/stefanoskarakasis/product-marketing-skills/meta-synthesis.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 | $0.00009 | $0.00154 |
| Opus 5 | $0.00005 | $0.00077 |
| Sonnet 5 | $0.00002 | $0.00031 |
| Haiku 4.5 | $0.00001 | $0.00015 |
Grade A, and why
meta-synthesis 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 12d 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-synthesis -- Pattern Synthesis
Detect patterns across multiple skills' session logs and propose updates to the shared brain — the mechanism that makes the whole system compound over time instead of resetting every session.
Invocation
/pmm-meta:meta-synthesis
/pmm-meta:meta-synthesis Focus on the last month of retro and pre-mortem
sessions
Workflow
Uses the meta-synthesis skill. Reads recent session logs across
skills, looks for recurring signals, and proposes specific, named brain
updates for approval before writing anything.
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.
- 12d ago First seen · 25 lines · 9 tokens per session scan A 897edb986f04
meta-synthesis is a command published in the GitHub repository stefanoskarakasis/Product-Marketing-Skills (5 stars, last pushed today), licensed MIT. It adds 9 tokens to every session and 154 once invoked, about $0.0000 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 2026-08-31.
Other commands, from other repositories
brief-me
Get briefed — loads your full memory, pulls live state from all connected tools, surfaces risks, staleness, upcoming milestones, and gives you a prioritized briefing so you're never starting blank.
retro
Run a post-ship retrospective — measure outcomes vs. predictions, extract lessons, update memory, and feed insights back into the PM system.
onboarding
Set up PM Copilot — a guided wizard that builds your persistent memory profile so every future session is grounded in your product context.
design-ai-feature
Design an AI-powered feature end-to-end — model selection, prompt architecture, eval framework, failure modes, cost modeling, and improvement flywheel.
competitive-intel
Run a competitive intelligence analysis — landscape mapping, battlecards, 7 Powers moat comparison, positioning gaps, and monitoring plan.
discover
Run a full discovery cycle — problem framing, JTBD demand-side analysis, assumption mapping, opportunity sizing, and OST mapping — from a rough idea to validated opportunity.