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 researchergit 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/researcher)<a href="https://agentmods.dev/skills/mrtblount/spec-to-ship/researcher"><img src="https://agentmods.dev/badge/skills/mrtblount/spec-to-ship/researcher/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/researcher"><img src="https://agentmods.dev/badge/skills/mrtblount/spec-to-ship/researcher.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 | $0.00039 | $0.00944 |
| Opus 5 | $0.00019 | $0.00472 |
| Sonnet 5 | $0.00008 | $0.00189 |
| Haiku 4.5 | $0.00004 | $0.00094 |
Grade A, and why
researcher 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 10d 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 — 145 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Inputs
READ _prd/discovery-notes.md to understand:
- What product is being built
- Who the target user is
- What problem it solves
- What current alternatives exist
- What the user's assumptions are
Research Protocol
Conduct targeted web research using WebSearch and WebFetch. Do NOT research everything — focus on what will actually change or strengthen the PRD.
Research Area 1: Competitive Landscape
SEARCH for:
- Direct competitors (products solving the same problem)
- Indirect competitors (different approach to same problem)
- Adjacent products (solving related problems)
FOR each significant competitor:
- What they do well
- Where they fall short (from user reviews, complaints, forums)
- Their pricing model
- Their target user (is it the same as ours?)
OUTPUT: 3-5 competitor profiles with strengths/weaknesses
Research Area 2: Market Context
SEARCH for:
- Market size or demand signals (search volume, funding activity, industry reports)
- Trends relevant to the product space
- Regulatory or compliance considerations (if applicable)
- Technology shifts that enable or threaten this product
OUTPUT: 2-3 key market insights that inform product decisions
Research Area 3: User Patterns (if user hasn't done customer discovery)
SEARCH for:
- Forum posts, Reddit threads, Twitter/X complaints about the problem
- Reviews of competing products (what users love/hate)
- Common workflows or workarounds people use today
- User expectations in this product category
OUTPUT: User sentiment summary and notable quotes/patterns
Research Area 4: AI-Specific Context (only if product uses AI)
SEARCH for:
- Similar AI-powered products and how they handle the AI UX
- Best practices for AI transparency, error handling, human-in-the-loop
- Relevant AI model capabilities and limitations for the use case
- Data privacy standards and regulations for the product's domain
OUTPUT: AI implementation context and best practices
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.
- 10d ago First seen · 145 lines · 39 tokens per session scan A 02f90a4bf051
researcher is a skill published in the GitHub repository mrtblount/Spec-to-Ship (2 stars, last pushed 1mo ago), licensed MIT. It adds 39 tokens to every session and 944 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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