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-solution-architectgit 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-solution-architect)<a href="https://agentmods.dev/skills/onedro1d/dark-factory/df-solution-architect"><img src="https://agentmods.dev/badge/skills/onedro1d/dark-factory/df-solution-architect/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-solution-architect"><img src="https://agentmods.dev/badge/skills/onedro1d/dark-factory/df-solution-architect.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 | $0.00078 | $0.00830 |
| Opus 5 | $0.00039 | $0.00415 |
| Sonnet 5 | $0.00016 | $0.00166 |
| Haiku 4.5 | $0.00008 | $0.00083 |
Grade A, and why
df-solution-architect 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 7d 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 — 35 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Dark Factory — Solution Architect (formalize the transform graph)
Overview
The SA answers how. It produces Data Model, Data Flow, Service Map. Through the data-transform lens (df-data-transform-lens), the three docs are the model. The PO already defined the semantics; the SA's job is formalization — turn each into a schema/contract, assign each rule its enforcement locus and mechanism, and tag transforms.
The three outputs
- Data Model = data nodes. Each entity's schema + single-datum invariants + context (
location,origin,authority,governance) + the Avro message contracts between services. Declare theauthority(system-of-record) for every fact that exists in more than one place. - Data Flow = the transform graph. Each step
(state, in) → (state', out), taggedpure | effect. Everyeffectcarries an idempotency key + compensation in the flow. Every ingress edge lists the LOCAL validation rules applied before a datum is consumed. One path per PO Test Scenario. - Service Map = transform ownership + boundaries. Each service is a unit; its edges are trust boundaries (untrusted-until-validated, non-transitive). The runtime trust profile is just the
origin/authority/boundary tags made explicit. Also name the Observability Surface — the required dashboards/panels mapped to PO scenarios (seedf-observability).
Ownership handoff (PO → SA)
The PO + SMEs defined the semantics (data, origin, trust, authority, governance, the validation-rule predicates). Your job is formalization:
- turn each into a schema / Avro contract;
- assign every validation rule its enforcement locus (
LOCAL→ reject at an edge;GLOBAL→ reconcile byauthority) and mechanism (XSD / Schematron / DB constraint / reconciliation job — a common split is XSD for structure and Schematron for cross-field business rules, but name the mechanism your stack actually enforces with); - tag transforms
pure/effectwith idempotency + compensation. Do not re-decide domain facts (which source is authoritative, what must be true) — if one is missing or wrong, loop back to PO.
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.
- 7d ago First seen · 35 lines · 78 tokens per session scan A 92c9765ac913
df-solution-architect is a skill published in the GitHub repository OneDro1d/dark-factory (0 stars, last pushed yesterday), licensed Apache-2.0. It adds 78 tokens to every session and 830 once invoked, about $0.0004 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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