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 qfoldit/Protein-Design-MCP --skill digital-twin-buildergit clone --depth 1 https://github.com/qfoldit/Protein-Design-MCPWrote 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/qfoldit/protein-design-mcp/digital-twin-builder)<a href="https://agentmods.dev/skills/qfoldit/protein-design-mcp/digital-twin-builder"><img src="https://agentmods.dev/badge/skills/qfoldit/protein-design-mcp/digital-twin-builder/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/qfoldit/protein-design-mcp/digital-twin-builder"><img src="https://agentmods.dev/badge/skills/qfoldit/protein-design-mcp/digital-twin-builder.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 | $0.00132 | $0.00906 |
| Opus 5 | $0.00066 | $0.00453 |
| Sonnet 5 | $0.00026 | $0.00181 |
| Haiku 4.5 | $0.00013 | $0.00091 |
Grade A, and why
digital-twin-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 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 — 32 lines — stays where its author put it; the contents beside it link to each section on GitHub.
digital-twin-builder
Purpose
A raw UAG from game-designer describes WHAT is in a scene and WHERE. It does not necessarily carry the physical semantics needed for a scene to behave correctly as a digital twin (a simulation that should mirror real physical/biological behavior, not just look plausible). This skill fills that gap: it adds units, material/mass/physical properties, and validates that constraints are physically coherent, before the graph goes to an engine adapter.
What "digital-twin-ready" means concretely
- Units are explicit. Every numeric property that has physical meaning (position, mass, temperature, concentration) must carry an explicit unit in
properties(e.g."mass_kg": 2.4, not a bare"mass": 2.4). If the incoming UAG has bare numbers, ask the user for the intended unit rather than guessing -- guessing units is a common, costly source of simulation bugs (see Mars Climate Orbiter-class failures from unit mismatches -- a cautionary example, not a joke). - Material/physical properties are attached where relevant. Nodes of
type: meshintended to participate in physics need at minimum: mass, friction/restitution (if colliding), and for anything meant to model a real process (e.g. a plant fromplant-growth, an ore sample frommining) -- asource_contextback-reference to the science skill and inputs that produced its properties, so the twin can be re-run/audited later. - Constraints are physically coherent. E.g. a
physics_collisionconstraint on two overlapping-at-rest nodes with no exception configured will produce simulation blow-up in most physics engines -- flag this instead of passing it through silently. - No fabricated precision. If the incoming science-skill data was a qualitative index (e.g.
plant-growth'sgrowth_rate_index_0_100) rather than a calibrated physical quantity, do NOT silently convert it into a fake-precise physical unit (e.g. don't invent "this plant has calculated mass 340.2g" from a 0-100 index). Carry the qualitative index through as-is and let the engine adapter/visual layer decide how to represent it (e.g. scale, color) -- see each science skill's own "known limitations" section before assuming its output is more precise than it is.
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 · 32 lines · 132 tokens per session scan A 015d08f77597
digital-twin-builder is a skill published in the GitHub repository qfoldit/Protein-Design-MCP (1 stars, last pushed 14d ago), licensed Apache-2.0. It adds 132 tokens to every session and 906 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.
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