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 prd-buildergit 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/prd-builder)<a href="https://agentmods.dev/skills/mrtblount/spec-to-ship/prd-builder"><img src="https://agentmods.dev/badge/skills/mrtblount/spec-to-ship/prd-builder.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 | $0.00135 | $0.01843 |
| Opus 5 | $0.00068 | $0.00922 |
| Sonnet 5 | $0.00027 | $0.00369 |
| Haiku 4.5 | $0.00014 | $0.00184 |
Grade A, and why
prd-builder 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 6d 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 — 201 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Core Philosophy: A PRD defines WHAT a product should do, WHO it is for, and WHY it matters. It is NOT a technical specification — that is left to engineering (and your spec-driven-dev skill). The PRD is a strategic anchor that prevents costly miscommunication and keeps user needs at the center.
Sub-Skills
| Component | Purpose |
|---|---|
| discovery | Phase 1: Codebase scan (existing) + structured interview → raw context |
| researcher | Phase 2: Web research for market, competitive, and user context |
| generator | Phase 3: Synthesize all context into the full PRD document |
| reviewer | Phase 4: Self-review, pressure-test, and refinement loop |
Dependency Chain:
- New product: discovery → researcher → generator → reviewer → finalize
- Existing product: discovery (with codebase scan) → researcher → generator → reviewer → finalize
The Solution: A structured, interview-driven PRD process that:
- Scans existing code to understand what's already built (if applicable)
- Walks the user through focused discovery questions
- Researches market context, competitors, and user patterns
- Generates a comprehensive PRD using the 7-section framework + AI-native sections
- Self-reviews for gaps, assumptions, and engineering pushback points
- Hands off cleanly to spec-driven-dev for technical specification
Relationship to Spec-Driven Development:
- PRD = strategic/product side (what, who, why)
- SDD SPEC.md = technical side (how, architecture, implementation)
- The PRD feeds directly into SDD Phase 1 (specifier) as input context
- After finalizing a PRD, offer to kick off spec-driven-dev
When to Use This Skill:
- Starting a new product or product idea
- An existing project that never had a formal PRD
- Pivoting or redefining an existing product
- Adding a major new product area to an existing project
- When the user needs clarity on what to build before building it
When NOT to Use This Skill:
- Adding a feature to an already-defined product → use spec-driven-dev directly
- Bug fixes, refactors, or small changes → just build
- Technical architecture decisions → use spec-driven-dev Phase 2
STEP 1: Detect project state
CHECK for existing project indicators:
- package.json, requirements.txt, Cargo.toml, go.mod, pyproject.toml
- .git directory with meaningful history
- src/ or app/ directory with application code
- README.md with product description
- Any existing PRD, SPEC.md, or product documentation
- CLAUDE.md or project context files
IF existing project with code detected:
mode = EXISTING_PRODUCT
INFORM user: "I see an existing codebase. I'll scan it first to understand
what's already built, then walk you through discovery questions."
ELSE:
mode = NEW_PRODUCT
INFORM user: "Starting fresh. I'll walk you through discovery questions
to define your product, then research the market context."
STEP 2: Check for existing PRD
IF PRD.md or similar document exists:
READ it
ASK user: "I found an existing PRD. Would you like to:
(a) Start fresh and replace it
(b) Use it as a starting point and refine it
(c) Create a PRD for a different product area"
STEP 3: Route to Phase 1
PROCEED to Phase 1 with mode = EXISTING_PRODUCT or NEW_PRODUCT
What ships with it
6 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 6d ago First seen · 201 lines · 135 tokens per session scan A 29a818d9cae0
prd-builder is a skill published in the GitHub repository mrtblount/Spec-to-Ship (2 stars, last pushed 1mo ago), licensed MIT. It adds 135 tokens to every session and 1,843 once invoked, about $0.0007 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 skills, from other repositories
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
local-ai-agents
Build local-first AI agents that run entirely on a developer workstation with Microsoft Foundry Local and Qwen function-calling models. Covers Small Language Models (SLMs), the OpenAI-compatible local endpoint, sandboxed local tools, local RAG with Chroma, local MCP servers, hybrid cloud/local routing, and the…
next-cache-components-adoption
Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…
chat-pet-sprite-creation
Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.
cpu-profile-analysis
Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…
insight-error-page
Write or audit an insight-kind error page for the Next.js dev overlay. Use when creating a new errors/ .mdx page, auditing an existing one, or checking that a page matches the framework fix cards. Covers page structure, title alignment, FixCard cards with Copy prompt button, code snippets, terminology verification…