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 mrtblount/Spec-to-Ship --skill discoverygit 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/discovery)<a href="https://agentmods.dev/skills/mrtblount/spec-to-ship/discovery"><img src="https://agentmods.dev/badge/skills/mrtblount/spec-to-ship/discovery/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/mrtblount/spec-to-ship/discovery"><img src="https://agentmods.dev/badge/skills/mrtblount/spec-to-ship/discovery.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 | $0.00044 | $0.01767 |
| Opus 5.5 | $0.00018 | $0.00707 |
| Sonnet 5.5 | $0.00009 | $0.00353 |
| Haiku 4.5 | $0.00004 | $0.00177 |
Grade A, and why
discovery 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 2mo 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 — 214 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Part A: Codebase Scan (EXISTING_PRODUCT mode only)
If mode is EXISTING_PRODUCT, scan the codebase BEFORE interviewing the user. This gives you context to ask smarter questions.
STEP 1: Project Overview Scan
READ (in parallel where possible):
- README.md or similar documentation
- package.json / requirements.txt / Cargo.toml / pyproject.toml (dependencies, scripts)
- CLAUDE.md or project context files
- Any existing PRD, product docs, or specs in the project
- .env.example or .env.local (to understand what services are used — DO NOT read .env)
STEP 2: Architecture Scan
SCAN the codebase structure:
- Use Glob to map the directory structure (src/, app/, pages/, components/, etc.)
- Identify the tech stack (framework, database, APIs, services)
- Identify major features by looking at route files, page components, API endpoints
- Look for authentication, payments, admin panels, or other common product patterns
STEP 3: Feature Inventory
Based on the scan, create a bullet-point inventory:
- What the product appears to do (core functionality)
- What tech stack it uses
- What external services/APIs it integrates with
- What user-facing features exist
- What appears to be in progress or incomplete
STEP 4: Present to User
PRESENT the inventory:
"Based on scanning your codebase, here's what I understand about your product:
[inventory]
Is this accurate? What am I missing or getting wrong?"
INCORPORATE corrections before proceeding to the interview.
Part B: Discovery Interview
Walk the user through focused questions. Adapt based on their responses — skip questions they've already answered, dig deeper where they're vague.
Interview Protocol:
- Ask 2-3 questions at a time, not all at once
- Use their language, not PM jargon
- If they give a vague answer, ask a follow-up to sharpen it
- If they say "I don't know," that's fine — log it as an open question
- For existing products, reference what you found in the codebase scan
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.
- 2mo ago First seen · 214 lines · 44 tokens per session scan A 05ea841a5c8a
discovery is a skill published in the GitHub repository mrtblount/Spec-to-Ship (2 stars, last pushed 3mo ago), licensed MIT. It adds 44 tokens to every session and 1,767 once invoked, about $0.0002 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.
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