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 pmprompt/claude-plugin-product-management --skill ai-ship-bargit clone --depth 1 https://github.com/pmprompt/claude-plugin-product-managementWrote 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/pmprompt/claude-plugin-product-management/ai-ship-bar)<a href="https://agentmods.dev/skills/pmprompt/claude-plugin-product-management/ai-ship-bar"><img src="https://agentmods.dev/badge/skills/pmprompt/claude-plugin-product-management/ai-ship-bar/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/pmprompt/claude-plugin-product-management/ai-ship-bar"><img src="https://agentmods.dev/badge/skills/pmprompt/claude-plugin-product-management/ai-ship-bar.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 | $0.00074 | $0.02532 |
| Opus 5.5 | $0.00030 | $0.01013 |
| Sonnet 5.5 | $0.00015 | $0.00506 |
| Haiku 4.5 | $0.00007 | $0.00253 |
Grade A, and why
ai-ship-bar 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.
How it starts
The opening of the file, as written. The whole thing — 171 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Domain Context
This skill implements a proven product management framework. The approach combines best practices from industry leaders and is designed for practical application in day-to-day PM work.
Input Requirements
- Context about your product, feature, or problem
- Relevant data, research, or constraints (recommended but optional)
- Clear articulation of what you're trying to achieve
AI Ship Bar
AI features fail quietly. A demo with five hand-picked prompts tells you almost nothing about the thousandth real request. A ship bar is the written agreement, made before launch, on which numbers the feature has to hit, on which test set, and who decides. It replaces "it feels good" with "it passed."
When to Use
- An AI feature is close to launch and the go/no-go conversation is based on demos
- You are changing the model, prompt, or retrieval behind a feature that is already live
- Leadership asks "how do we know it's good enough?"
- Engineering and product disagree on whether quality is there
- You need a regression gate so later changes can't quietly make it worse
What This Skill Does
Interviews you about the feature and its risks, then produces a ship-bar doc: what "success" means per request, the metrics that matter for this feature, how to build the eval set, thresholds with an owner for each, who signs off, the regression gates for future changes, and what to watch after launch. Every threshold in the output is labeled as an example until your team sets it.
Instructions
Step 1 — Interview first
Ask these before writing the doc:
- What does the feature do, and what does a correct result look like for one request?
- Who judges correctness: an exact answer, a rubric, a human reviewer, a downstream signal (accepted, edited, undone)?
- What does a wrong result cost: a mild annoyance, a support ticket, lost money, a safety or legal problem?
- Which is worse here, a false positive or a false negative?
- How fast does it need to feel? Is it interactive, background, or batch?
- What can you afford to spend per request, and what does a successful request earn or save?
- Do you have real inputs to test with (logs, tickets, documents)? Can they be used for testing under your data policy?
- Who has the authority to say "ship" and "roll back"?
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 · 171 lines · 74 tokens per session scan A ab349e2bb90e
ai-ship-bar is a skill published in the GitHub repository pmprompt/claude-plugin-product-management (53 stars, last pushed 2d ago), licensed MIT. It adds 74 tokens to every session and 2,532 once invoked, about $0.0003 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 2026-10-23.
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