Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/matteotitta/genesys-skillsnpx agentmods add skills/matteotitta/genesys-skills/ship-learningsWrote 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/matteotitta/genesys-skills/ship-learnings)<a href="https://agentmods.dev/skills/matteotitta/genesys-skills/ship-learnings"><img src="https://agentmods.dev/badge/skills/matteotitta/genesys-skills/ship-learnings/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/matteotitta/genesys-skills/ship-learnings"><img src="https://agentmods.dev/badge/skills/matteotitta/genesys-skills/ship-learnings.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00156 | $0.01372 |
| Opus 5 | $0.00078 | $0.00686 |
| Sonnet 5 | $0.00031 | $0.00274 |
| Haiku 4.5 | $0.00016 | $0.00137 |
Grade A, and why
ship-learnings 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 9d 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 — 138 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Ship-learnings — post-ship compound-learnings capture
Capture what we learned from a completed ship so the next strategy refresh has accumulated evidence. Adapted from /ce-compound in EveryInc/compound-engineering-plugin v3.5.0 (MIT).
Per Moretti's framing (K1): everything that ships is an experiment. Each ship tests a hypothesis from the strategy doc. Capture the result.
When to run
Invoke when the user says:
- "We just shipped [feature] — what did we learn?"
- "Run ship-learnings on [release]"
- "Post-ship review for [experiment]"
- "Compound learnings"
Do NOT invoke when:
- The session is the unit (use
/session-wrapfor session-scoped wrap) - The ship is broken / incident (use
engineering:incident-response) - No ship has occurred — this is post-ship only
Distinct from /session-wrap
| Skill | Scope | When |
|---|---|---|
/session-wrap |
One Claude Code session | End of session |
/ship-learnings |
One ship / release / experiment | After feature ships, regardless of how many sessions it took |
A ship may span 5 sessions; one ship-learnings record captures all of them.
Inputs
Required:
- Locked
strategy-doc(the hypothesis being tested by this ship) - Ship description: name, date shipped, scope summary
Recommended:
- Latest
product-pulse(the metric movement post-ship) - User feedback / quotes / support tickets from the ship period
- Original strategy track this ship belongs to
Steps
- Phase 1 — Load context. Read locked
strategy-doc+ latestproduct-pulse. Identify which strategy track this ship belonged to. - Phase 2 — Pull ship signals. Metric deltas (from pulse), user quotes, support volume changes, anomalies.
- Phase 3 — Run interview. Walk the user through the 7-section structure. The hardest section is §3 Result — push back if the user says "kind of worked" without evidence.
- Phase 4 — Compose record. Apply length discipline: ≤ 600 words total.
- Phase 5 — Self-roast. Run checks below.
- Phase 6 — Push. Save to ship-learnings folder + flag in next strategy refresh review.
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.
- 9d ago First seen · 138 lines · 154 tokens per session scan A 9f1c16830749
ship-learnings is a skill published in the GitHub repository matteotitta/genesys-skills (36 stars, last pushed 1mo ago), licensed MIT. It adds 156 tokens to every session and 1,372 once invoked, about $0.0008 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 2026-09-03.
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