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 agentmods add skills/mrtblount/spec-to-ship/generatornpx skills add mrtblount/Spec-to-Ship --skill generatorgit clone --depth 1 https://github.com/mrtblount/Spec-to-ShipWrote 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/mrtblount/spec-to-ship/generator)<a href="https://agentmods.dev/skills/mrtblount/spec-to-ship/generator"><img src="https://agentmods.dev/badge/skills/mrtblount/spec-to-ship/generator.svg" alt="Measured on agentmods" 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 | $0.00035 | $0.00962 |
| Opus 5 | $0.00017 | $0.00481 |
| Sonnet 5 | $0.00007 | $0.00192 |
| Haiku 4.5 | $0.00003 | $0.00096 |
Grade A, and why
generator 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 5d 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 — 114 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Write in plain English. Be specific. Be concise. Every sentence should either define what the product does or inform a decision.
Inputs
READ both:
_prd/discovery-notes.md— user's vision, problem, features, context_prd/research-notes.md— market context, competitors, user patterns
Also READ the PRD template at:
references/prd-template.md— the output structure to follow
Generation Rules
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Use the user's language. If they called it "smart suggestions," don't rename it to "AI-powered recommendation engine" in the PRD.
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Be ruthlessly specific. "Good UX" is useless. "Onboarding completes in under 60 seconds with zero configuration" is useful.
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Separate problem from solution. The problem statement should make sense even if you removed every feature. The features should clearly trace back to the problem.
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Include only what applies. If the product doesn't use AI, omit the AI sections entirely. If there's no compliance concern, don't add a compliance section for the sake of completeness.
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Prioritize clearly. Every feature should be tagged as Must-Have, Nice-to-Have, or Future. If the user didn't prioritize, use the "would we delay launch for this?" test.
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Ground in research. Reference competitive gaps, user patterns, and market context where they strengthen the rationale for decisions.
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Flag open questions. Don't paper over uncertainty. If the user said "I don't know" during discovery, it goes in the Open Questions section.
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Write user stories for real humans. Not "As a user, I want to log in so that I can access the app." Write stories that capture actual user motivation and context.
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.
- 5d ago First seen · 114 lines · 35 tokens per session scan A 73f5a2b9b94c
generator is a skill published in the GitHub repository mrtblount/Spec-to-Ship (2 stars, last pushed 1mo ago), licensed MIT. It adds 35 tokens to every session and 962 once invoked, about $0.0002 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.
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