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 Kdevos12/ALKYL --skill generative-designgit clone --depth 1 https://github.com/Kdevos12/ALKYLWrote 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/kdevos12/alkyl/generative-design)<a href="https://agentmods.dev/skills/kdevos12/alkyl/generative-design"><img src="https://agentmods.dev/badge/skills/kdevos12/alkyl/generative-design/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/kdevos12/alkyl/generative-design"><img src="https://agentmods.dev/badge/skills/kdevos12/alkyl/generative-design.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 | $0.01781 |
| Opus 5.5 | $0.00031 | $0.00712 |
| Sonnet 5.5 | $0.00016 | $0.00356 |
| Haiku 4.5 | $0.00008 | $0.00178 |
Grade A, and why
generative-design 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 1mo 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 — 145 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Generative Molecular Design
De novo design of novel molecules with desired properties using generative models — the core ML capability for lead generation and scaffold hopping in drug discovery.
When to Use This Skill
- Generate molecules with target properties (QED, LogP, SA, docking score)
- Explore chemical space around a hit/lead (analogue generation, scaffold hopping)
- Design molecules conditioned on a protein pocket (SBDD)
- Optimize multi-property objectives (Pareto front: potency + selectivity + ADMET)
- Benchmark or compare generative models (MOSES / GuacaMol suites)
- Build a RL-based focused library generator (REINVENT 4)
- Design linkers or grow fragments (fragment-based generative design)
Generation Paradigms
| Paradigm | Method | Strength | Weakness |
|---|---|---|---|
| Language model | SMILES/SELFIES GPT, LSTM | Fast, scalable, fine-tunable | SMILES can be invalid; needs SELFIES |
| VAE | JT-VAE, MolVAE | Smooth latent space, BO-ready | Mode collapse; slow tree encode |
| GNN flow/GAN | GraphAF, GCPN, JunctionGAN | Graph-native; no linearity | Training instability |
| RL optimization | REINVENT 4, REINFORCE | Property-guided; no new arch needed | Reward hacking; mode collapse |
| 3D diffusion | DiffSBDD, TargetDiff | Pocket-conditioned; 3D geometry | Slow, needs structure |
| Fragment-based | DeLinker, DiffLinker | Fragment growing, FBDD | Limited to provided fragments |
Evaluation Metrics (Know These)
| Metric | What it measures | Target |
|---|---|---|
| Validity | % chemically valid | ~100% (SELFIES) / 85-99% (SMILES LM) |
| Uniqueness | % unique in generated set | >99% |
| Novelty | % not in training set | >99% |
| FCD | Fréchet ChemNet Distance (distribution) | Lower = closer to drug-like distribution |
| KL divergence | Property distributions vs. reference | Lower |
| Scaffold diversity | # unique Murcko scaffolds / N | Higher |
| IntDiv | Internal diversity (mean pairwise 1-Tc) | > 0.85 |
| SNN | Similarity to nearest neighbor in training | < 0.6 (novel) |
What ships with it
5 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.
- 1mo ago First seen · 145 lines · 78 tokens per session scan A 5af0859e1534
generative-design is a skill published in the GitHub repository Kdevos12/ALKYL (7 stars, last pushed 7mo ago), licensed MIT. It adds 78 tokens to every session and 1,781 once invoked, about $0.0003 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.
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