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 plastic-characterizationgit 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/plastic-characterization)<a href="https://agentmods.dev/skills/qfoldit/protein-design-mcp/plastic-characterization"><img src="https://agentmods.dev/badge/skills/qfoldit/protein-design-mcp/plastic-characterization/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/plastic-characterization"><img src="https://agentmods.dev/badge/skills/qfoldit/protein-design-mcp/plastic-characterization.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 | $0.00144 | $0.00799 |
| Opus 5 | $0.00072 | $0.00400 |
| Sonnet 5 | $0.00029 | $0.00160 |
| Haiku 4.5 | $0.00014 | $0.00080 |
Grade A, and why
qfoldit-plastic-characterization 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 9d 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 — 41 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Plastic Characterization (pyrolysis feedstock estimator)
What this skill does
Gives an estimated (not a precise prediction from a trained model) range of pyrolysis products for a plastic mixture, based on published literature yield ranges for the main polymer types. This is a reference-data-based calculator, not a proprietary ML model — the result should be presented to the user that way.
Important: this skill does NOT implement the autoresearch / quantum-adapter piece (a real-time reactor-parameter optimizer trained on real runs) — that is a separate, much harder task requiring real data from a specific reactor for calibration. Do not present heuristic estimates as the output of such an optimizer.
When to use
- The user describes a batch of plastic waste composition (in % of PE, PP, PS, PET, PVC, other) and asks about expected oil/gas/char yield.
- The user asks about chlorine contamination risk in the oil (from PVC) or reactor coking/fouling risk.
- The user asks for a recommended temperature/residence time for a specific feedstock batch.
How to work
- Gather the batch composition (mass fractions of PE / PP / PS / PET / PVC / other). If the user gives incomplete data, explicitly state what assumptions you're making (e.g. "other = mixed polymers, using averaged literature values").
- Run
scripts/estimate_yield.pywith the composition as input (see example below). - Read
references/pyrolysis_yields.mdif you need to explain to the user where the ranges come from and what literature they're based on. - Always present the result as a range, not a precise number, and explicitly state: "this is an estimate based on averaged literature data for pure-polymer pyrolysis at 450-550°C, not the result of training on data from a specific reactor. Industrial deployment requires calibration against real runs."
- If the user asks for "the exact percentage oil yield" — don't fabricate precision that doesn't exist; offer a range and explain that only a test run on the real feedstock can narrow it.
What ships with it
4 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.
- 9d ago First seen · 41 lines · 144 tokens per session scan A 6c8e1013a840
qfoldit-plastic-characterization is a skill published in the GitHub repository qfoldit/Protein-Design-MCP (1 stars, last pushed 12d ago), licensed Apache-2.0. It adds 144 tokens to every session and 799 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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