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 OneDro1d/dark-factory --skill df-product-ownergit clone --depth 1 https://github.com/OneDro1d/dark-factoryWrote 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/onedro1d/dark-factory/df-product-owner)<a href="https://agentmods.dev/skills/onedro1d/dark-factory/df-product-owner"><img src="https://agentmods.dev/badge/skills/onedro1d/dark-factory/df-product-owner/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/onedro1d/dark-factory/df-product-owner"><img src="https://agentmods.dev/badge/skills/onedro1d/dark-factory/df-product-owner.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 | $0.00063 | $0.00718 |
| Opus 5 | $0.00032 | $0.00359 |
| Sonnet 5 | $0.00013 | $0.00144 |
| Haiku 4.5 | $0.00006 | $0.00072 |
Grade A, and why
df-product-owner 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 11d 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 — 42 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Dark Factory — Product Owner (define the semantics)
Overview
The PO turns messy intent into a self-contained, testable target. It produces three must-have outputs: Vision, Requirements, Test Scenarios. Through the data-transform lens (df-data-transform-lens): Requirements = data contracts + validation rules; Test Scenarios = those rules as cases. The PO owns the semantics (domain truth); the Solution Architect formalizes them.
When to use
Defining what to build, writing requirements/acceptance criteria, or assembling the package a cold Solution Architect will design from.
What the PO defines (with SMEs) — the semantics
Per capability, name the data and the rules over it:
| Field | What to capture |
|---|---|
| Data (schema) | the fields collected, conceptually |
origin |
real-world source: web form · mobile · scanned doc · 3rd-party API · another service (for audit — never a trust signal) |
authority |
who is system-of-record for this fact (a domain fact, e.g. "the bank owns balance") |
governance |
PHI/PII class, retention, residency |
| Validation rules | the business predicate + its scope (holds within one record, or must agree across systems/time) |
| Effect? | does the acceptance criterion touch the outside world (send/charge/notify/write-external)? Flag it so SA assigns idempotency + compensation |
You state what must be true and who is authoritative; the SA decides where it's checked and how (the formal LOCAL/GLOBAL locus + mechanism is SA's call).
Instructions
- Vision — why / what / who / value, and the non-goals ("what this is not").
- Requirements — capabilities + testable acceptance criteria, each as a data contract + validation rule (use the table above).
- Test Scenarios — concrete real-life situations, not screen assertions. Frame each as a state change:
State 0 (precondition) → Trigger (input) → State 1 (end state), with happy path + edge + failure modes. These are the executable evidence standard downstream. - Label every claim Confirmed / Inferred / Assumption / Open — never silently invent product facts.
- Mark any outside-world trigger as an effect.
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.
- 11d ago First seen · 42 lines · 63 tokens per session scan A eebe4abd10dd
df-product-owner is a skill published in the GitHub repository OneDro1d/dark-factory (1 stars, last pushed today), licensed Apache-2.0. It adds 63 tokens to every session and 718 once invoked, about $0.0003 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 2026-09-01.
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