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 GPTomics/bioSkills --skill reaction-enumerationgit clone --depth 1 https://github.com/GPTomics/bioSkillsWrote 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/gptomics/bioskills/reaction-enumeration)<a href="https://agentmods.dev/skills/gptomics/bioskills/reaction-enumeration"><img src="https://agentmods.dev/badge/skills/gptomics/bioskills/reaction-enumeration/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/gptomics/bioskills/reaction-enumeration"><img src="https://agentmods.dev/badge/skills/gptomics/bioskills/reaction-enumeration.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 | $0.00095 | $0.04956 |
| Opus 5 | $0.00048 | $0.02478 |
| Sonnet 5 | $0.00019 | $0.00991 |
| Haiku 4.5 | $0.00010 | $0.00496 |
Grade A, and why
bio-reaction-enumeration 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 6d 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- bio-reaction-enumeration — 95% identical, 12 lines differ
How it starts
The opening of the file, as written. The whole thing — 363 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Version Compatibility
Reference examples tested with: RDKit 2024.09+, RDChiral 1.1+, mmpdb 3.1+, scikit-learn 1.4+, numpy 1.26+.
Before using code patterns, verify installed versions match. If versions differ:
- Python:
pip show <package>thenhelp(module.function)to check signatures
If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.
Reaction Enumeration
Generate virtual libraries by applying reaction SMARTS to building blocks, enumerate analog series via matched molecular pairs, decompose into R-groups for SAR modeling, or extract transformations from reaction data. Reaction enumeration sits at the intersection of medicinal chemistry, lead optimization, and de novo design; DOGS is one published example of reaction-driven de novo design (Hartenfeller et al. 2012). The two key operations are transform (apply known reactions to make new compounds) and mine (extract rules from observed analog series). RDKit's reaction SMARTS handles the former; mmpdb / Free-Wilson handle analog-series analysis, while mapped-reaction template extraction requires separate tooling such as RDChiral.
For retrosynthetic planning (target-to-starting-material decomposition), see chemoinformatics/retrosynthesis. For ML-driven design, see chemoinformatics/generative-design. For scaffold-based design, see chemoinformatics/scaffold-analysis.
Operation Taxonomy
| Operation | Goal | Tool | Fails when |
|---|---|---|---|
| Forward enumeration | Apply reaction to building blocks -> products | RDKit ReactionFromSmarts + RunReactants |
Wrong atom mapping; missing connectivity |
| Reverse enumeration (retrosynthesis) | Product -> starting materials | AiZynthFinder, Chemformer | See retrosynthesis skill |
| Template mining | Reaction database -> reaction SMARTS templates | RXNMapper + RDChiral | Atom mapping ambiguous; mechanism unclear |
| RECAP fragmentation | Molecule -> retro-synthetic fragments | RDKit Chem.Recap |
Inflexible bond rules |
| BRICS fragmentation | Molecule -> retro-synthetic fragments | RDKit BRICS module |
Many false fragments |
| R-group decomposition | Set of mols + scaffold -> R-group table | RDKit Chem.rdRGroupDecomposition |
Multiple scaffolds; ambiguous attachment |
| Matched Molecular Pairs (MMPA) | Set of mols -> transformation rules | mmpdb | Sparse or context-confounded matched pairs |
| Free-Wilson | Compounds + activities -> additive R-group contributions | scikit-learn linear regression | Strict additivity assumption |
What ships with it
2 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.
- 6d ago First seen · 363 lines · 95 tokens per session scan A e58960dab0b2
bio-reaction-enumeration is a skill published in the GitHub repository GPTomics/bioSkills (1,199 stars, last pushed 25d ago), licensed MIT. It adds 95 tokens to every session and 4,956 once invoked, about $0.0005 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 2026-09-03.
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