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 similarity-searchinggit 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/similarity-searching)<a href="https://agentmods.dev/skills/gptomics/bioskills/similarity-searching"><img src="https://agentmods.dev/badge/skills/gptomics/bioskills/similarity-searching/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/similarity-searching"><img src="https://agentmods.dev/badge/skills/gptomics/bioskills/similarity-searching.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 | $0.00101 | $0.04533 |
| Opus 5 | $0.00051 | $0.02266 |
| Sonnet 5 | $0.00020 | $0.00907 |
| Haiku 4.5 | $0.00010 | $0.00453 |
Grade A, and why
bio-similarity-searching 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 8d 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-similarity-searching — 92% identical, 12 lines differ
How it starts
The opening of the file, as written. The whole thing — 330 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+, scikit-learn 1.4+, mhfp 1.9+.
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.
Similarity Searching
Find structurally similar compounds and cluster libraries by similarity. The choice of similarity coefficient and fingerprint is task-aware: Tanimoto for symmetric similarity in lead optimization, Tversky for asymmetric "substructure-like" queries, Dice for higher sensitivity in low-similarity regimes, and MaxCommon Substructure (MCS) for scaffold-hopping. Tanimoto similarity above 0.7 is not a guarantee of activity preservation; activity cliffs (similar molecules with dissimilar activities) are common (Maggiora 2014).
For fingerprint choice, see chemoinformatics/molecular-descriptors. For 3D shape similarity, see chemoinformatics/shape-similarity.
Similarity Coefficient Taxonomy
| Coefficient | Formula | Range | Symmetric | Use case | Fails when |
|---|---|---|---|---|---|
| Tanimoto | c / (a + b - c) | 0-1 | Yes | Default for ECFP4 similarity, ranking analogs | Saturates at low similarity (drug vs natural product) |
| Dice | 2c / (a + b) | 0-1 | Yes | Bit or nonnegative sparse-count vectors when Dice semantics are intended | Thresholds depend on vector type; analog choice subjective |
| Cosine (Ochiai) | c / sqrt(a*b) | 0-1 | Yes | Count vectors, weighted similarity | Not standard for bit vectors |
| Tversky alpha,beta | c / (alpha*(a-c) + beta*(b-c) + c) | 0-1 | No when alpha != beta | Asymmetric "is A a substructure of B" queries | Parameter choice subjective; alpha=1,beta=0 = substructure-like |
| Hamming | (a + b - 2c) / nBits | 0-1 | Yes | Binary bit vectors when bit-wise disagreement matters | Does not preserve count magnitude |
| Russell-Rao | c / nBits | 0-1 | Yes | Sparse fingerprints | Biased by fingerprint density |
| Kulczynski | (c/a + c/b) / 2 | 0-1 | Yes | When fingerprints have very different bit-density | Less standard |
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.
- 8d ago First seen · 330 lines · 101 tokens per session scan A 4d757d20e7d4
bio-similarity-searching is a skill published in the GitHub repository GPTomics/bioSkills (1,201 stars, last pushed 27d ago), licensed MIT. It adds 101 tokens to every session and 4,533 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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