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 plant-growthgit 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/plant-growth)<a href="https://agentmods.dev/skills/qfoldit/protein-design-mcp/plant-growth"><img src="https://agentmods.dev/badge/skills/qfoldit/protein-design-mcp/plant-growth/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/plant-growth"><img src="https://agentmods.dev/badge/skills/qfoldit/protein-design-mcp/plant-growth.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 | $0.00160 | $0.00999 |
| Opus 5 | $0.00080 | $0.00500 |
| Sonnet 5 | $0.00032 | $0.00200 |
| Haiku 4.5 | $0.00016 | $0.00100 |
Grade A, and why
qfoldit-plant-growth 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 12d 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 — 49 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Plant Growth Model (NPK + light spectrum → morphology & metabolites)
What this skill does
A qualitative heuristic model that, from nitrogen/phosphorus/potassium (NPK) levels and lighting parameters (intensity, photoperiod, spectral composition), computes:
- a growth-rate index (Liebig's law of the minimum — limited by the scarcest resource),
- morphological effects (internode length, branching density, compactness — based on the shade-avoidance syndrome via the R:FR ratio and blue-light fraction),
- qualitative deficiency/excess symptoms for macronutrients,
- a stress-related secondary-metabolite index (anthocyanins/flavonoids).
This is NOT an ML model calibrated on real data. These are transparent, explainable formulas based on well-known plant physiology principles — see references/plant_physiology_basis.md for a full explanation of each relationship. Always present the result as a qualitative/illustrative estimate, not a precise agronomic forecast.
When to use
- Questions about NPK plant nutrition, fertilizer deficiency/excess.
- Questions about the effect of light spectrum/intensity on growth (grow lights, LED spectrum, DLI, R:FR, shade-avoidance syndrome).
- A request to model/visualize how growing conditions will affect plant shape.
- Context — a "virtual lab"/greenhouse in a game/VR demo, where you need to show the effect of environmental parameters on a plant.
How to work
- Gather the input parameters: N/P/K (in ppm or relative units — if the user doesn't know exact figures, help translate a description like "low nitrogen" into approximate relative levels, explicitly flagging this as an assumption), PPFD and photoperiod (or total DLI directly), and the red/blue/far-red/UV fractions of the spectrum.
- If the user doesn't know some parameters — use the script's sensible defaults (see
--help) and explicitly state which defaults were applied. - Run
scripts/plant_model.py, read the result. - Explain the result to the user in plain language: what the limiting factor is, what symptoms to expect, what the secondary-metabolite index means — using the wording from
references/plant_physiology_basis.md. - If the user is also using the "qfoldit-l-systems" skill (or asks for a visualization) — add the
--emit-lsystem-paramsflag and pass the resultingpreset/angle/iterations/stepintoqfoldit-l-systems/scripts/lsystem.pyto generate an SVG reflecting the computed morphology. Mention that the suggested color (note_stroke_color) needs to be passed manually — lsystem.py doesn't yet support color directly via the CLI. - Always end with a reminder: this is an illustrative model; real-world application (a specific greenhouse, specific plant species) requires calibration against real data from that specific installation and species.
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.
- 12d ago First seen · 49 lines · 160 tokens per session scan A b188373e109b
qfoldit-plant-growth is a skill published in the GitHub repository qfoldit/Protein-Design-MCP (1 stars, last pushed 15d ago), licensed Apache-2.0. It adds 160 tokens to every session and 999 once invoked, about $0.0008 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.
Other skills, from other repositories
instrument-data-to-allotrope
Convert laboratory instrument output files (PDF, CSV, Excel, TXT) to Allotrope Simple Model (ASM) JSON format or flattened 2D CSV. Use this skill when scientists need to standardize instrument data for LIMS systems, data lakes, or downstream analysis. Supports auto-detection of instrument types. Outputs include full…
matlab
Build, review, migrate, and safely plan MATLAB or GNU Octave numerical workflows, including arrays, tabular/time data, tests, projects, graphics, MAT files, and explicit Python interoperability.
exploratory-data-analysis
Perform bounded, local exploratory analysis of explicitly supported scientific files. Use for redacted CSV/TSV/JSON profiles; optional NumPy, HDF5, FASTA/FASTQ, and basic image metadata inspection; missingness/leakage audits; outlier and transformation sensitivity; and rigorous EDA report scaffolds. Other domain…
phylogenetics
Build and analyze phylogenetic trees using MAFFT (multiple alignment), IQ-TREE 2 (maximum likelihood), and FastTree (fast NJ/ML). Visualize with ETE3 or FigTree. For evolutionary analysis, microbial genomics, viral phylodynamics, protein family analysis, and molecular clock studies.
research-engineer
An uncompromising Academic Research Engineer. Operates with absolute scientific rigor, objective criticism, and zero flair. Focuses on theoretical correctness, formal verification, and optimal implementation across any required technology.
mapping-to-snomed
Maps clinical concept spans extracted by OpenMed to SNOMED CT concepts through a USER-SUPPLIED terminology server (the user's own Ontoserver, Snowstorm, or UMLS/UTS), never a bundled vocabulary. Use when the user wants to code findings, disorders, procedures, body structures, or substances to SNOMED CT, run an ECL…