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 ma-compbio-lab/SkillFoundry --skill deepchem-circular-featurizationgit clone --depth 1 https://github.com/ma-compbio-lab/SkillFoundryWrote 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/ma-compbio-lab/skillfoundry/deepchem-circular-featurization)<a href="https://agentmods.dev/skills/ma-compbio-lab/skillfoundry/deepchem-circular-featurization"><img src="https://agentmods.dev/badge/skills/ma-compbio-lab/skillfoundry/deepchem-circular-featurization/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/ma-compbio-lab/skillfoundry/deepchem-circular-featurization"><img src="https://agentmods.dev/badge/skills/ma-compbio-lab/skillfoundry/deepchem-circular-featurization.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 | $0.00049 | $0.00568 |
| Opus 5 | $0.00024 | $0.00284 |
| Sonnet 5 | $0.00010 | $0.00114 |
| Haiku 4.5 | $0.00005 | $0.00057 |
Grade A, and why
deepchem-circular-featurization 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 — 53 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Purpose
Turn one or more SMILES strings into deterministic DeepChem CircularFingerprint summaries without requiring TensorFlow or PyTorch.
When to use
- You need a lightweight DeepChem-backed fingerprinting step before downstream molecular ML work.
- You want a compact JSON payload with canonical SMILES, dense bit vectors, and active bit indices.
When not to use
- You need graph featurizers, model training, or dataset download workflows.
- You need batch-scale featurization for very large libraries.
Inputs
- Repeated
--smilesarguments, or no arguments to use the bundled aspirin/caffeine example - Optional
--size,--radius, and--out
Outputs
- JSON summary with
canonical_smiles,bit_vector,on_bits, andon_bit_countfor each molecule
Requirements
slurm/envs/deepchem- DeepChem 2.8.0 and RDKit installed in that prefix
Procedure
- Run
slurm/envs/deepchem/bin/python skills/drug-discovery-and-cheminformatics/deepchem-circular-featurization/scripts/compute_circular_fingerprints.py --out skills/drug-discovery-and-cheminformatics/deepchem-circular-featurization/assets/aspirin_caffeine_fingerprints.json. - Inspect
size,radius, and each molecule'scanonical_smiles,bit_vector, andon_bits. - Reuse the compact JSON as a deterministic preprocessing artifact for later experiments.
Validation
- The command exits successfully under
slurm/envs/deepchem/bin/python. - Each molecule gets a non-empty canonical SMILES and a bit vector of the requested length.
- Repeated runs with the same inputs produce the same fingerprint payload.
Failure modes and fixes
- Missing DeepChem runtime: run the script with
slurm/envs/deepchem/bin/python. - Invalid SMILES: correct the input string before featurization.
- Optional backend warnings: TensorFlow and PyTorch are not required for this fingerprint-only skill.
Safety and limits
- Local featurization only.
- No activity prediction, medicinal-chemistry recommendation, or safety interpretation is implied.
What ships with it
8 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 · 53 lines · 49 tokens per session scan A 9d6c16f62e8e
deepchem-circular-featurization is a skill published in the GitHub repository ma-compbio-lab/SkillFoundry (38 stars, last pushed 4mo ago), licensed Apache-2.0. It adds 49 tokens to every session and 568 once invoked, about $0.0002 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 2026-08-30.
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