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qfoldit-plastic-characterization

qfoldit-plastic-characterization is a skill for Claude Code from qfoldit/Protein-Design-MCP. It costs 144 tokens per session (799 once invoked), scanned A, original, Apache-2.0.

A reference-based estimator for how mixtures of common plastics may break down during pyrolysis, a process that heats plastic without oxygen.

In plain words
What is it for?
Use it to estimate oil, gas, and char yields; assess PVC-related chlorine risk; assess catalyst fouling; and suggest temperature or residence-time adjustments for a described mixture.
Why use it?
It gives approximate product ranges and highlights chlorine and reactor-fouling risks without pretending to be a trained real-time optimizer.

Skill for Claude Code

Written for Claude Code: shipped in a Claude Code plugin.

Part of the qfoldit-skills plugin — 20 skills shipped together

not rated 1repo 2mo ago A scan 144 tokens original Apache-2.0

Good fit Use it to estimate oil, gas, and char yields; assess PVC-related chlorine risk; assess catalyst fouling; and suggest temperature or residence-time adjustments for a described mixture.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/qfoldit/protein-design-mcp/plastic-characterization
View source ↗ qfoldit/Protein-Design-MCP
Install

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.

Any agent
npx skills add qfoldit/Protein-Design-MCP --skill plastic-characterization
Clone the repo
git clone --depth 1 https://github.com/qfoldit/Protein-Design-MCP

Made for: Claude Code.

Or install qfoldit-skills, the plugin that ships this one along with the rest of its 20 skills.

Wrote 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.

agentmods badge for qfoldit-plastic-characterization

README.md
[![agentmods](https://agentmods.dev/badge/skills/qfoldit/protein-design-mcp/plastic-characterization/github.svg)](https://agentmods.dev/skills/qfoldit/protein-design-mcp/plastic-characterization)
Your own site
<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.

agentmods 80×15 button for qfoldit-plastic-characterization

Your own site · 80×15
<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>
Per session 144 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 799 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00144 $0.00799
Opus 5.5 $0.00058 $0.00320
Sonnet 5.5 $0.00029 $0.00160
Haiku 4.5 $0.00014 $0.00080

Measured 2mo ago against content hash 6c8e1013a840, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

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 2mo ago.

The scan reads SKILL.md. This mod also ships 1 executable file (scripts/estimate_yield.py), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

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.

claude-skills/skills/plastic-characterization/SKILL.md · 41 lines

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

  1. 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").
  2. Run scripts/estimate_yield.py with the composition as input (see example below).
  3. Read references/pyrolysis_yields.md if you need to explain to the user where the ranges come from and what literature they're based on.
  4. 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."
  5. 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.

Read the full file on GitHub · 41 lines

Files

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.

Changes

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.

  1. 2mo ago First seen · 41 lines · 144 tokens per session scan A 6c8e1013a840

Subscribe to this mod's changes

qfoldit-plastic-characterization is a skill published in the GitHub repository qfoldit/Protein-Design-MCP (1 stars, last pushed 2mo ago), licensed Apache-2.0. It adds 144 tokens to every session and 799 once invoked, about $0.0006 per session on Opus 5.5. 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.

Scientific
Related

qfoldit-plastic-characterization compared with skills from other repositories

qfoldit-plastic-characterization next to 6 skills from other repositories
ModTypeStarsLast pushLicenceScanTokensWorks with
qfoldit-plastic-characterizationqfoldit/Protein-Design-MCPSkill12mo agoApache-2.0A144Claude Code
instrument-data-to-allotropeanthropics/knowledge-work-pluginsSkill35ktodayApache-2.0A123Claude Code
exploratory-data-analysisK-Dense-AI/scientific-agent-skillsSkill56k5d agoMITA83Claude Code
matlabK-Dense-AI/scientific-agent-skillsSkill56k5d agoMITA44Claude Code
phylogeneticsK-Dense-AI/scientific-agent-skillsSkill56k5d agoMITA71Claude Code, Codex
research-engineerdavila7/claude-code-templatesSkill33ktodayMITA43Claude Code, Codex
mapping-to-snomedmaziyarpanahi/openmedSkill5.8ktodayApache-2.0A205Claude Code