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 K-Dense-AI/drug-discovery-agent-skills --skill binding-site-analysisgit clone --depth 1 https://github.com/K-Dense-AI/drug-discovery-agent-skillsWrote 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/k-dense-ai/drug-discovery-agent-skills/binding-site-analysis)<a href="https://agentmods.dev/skills/k-dense-ai/drug-discovery-agent-skills/binding-site-analysis"><img src="https://agentmods.dev/badge/skills/k-dense-ai/drug-discovery-agent-skills/binding-site-analysis/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/k-dense-ai/drug-discovery-agent-skills/binding-site-analysis"><img src="https://agentmods.dev/badge/skills/k-dense-ai/drug-discovery-agent-skills/binding-site-analysis.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00128 | $0.01878 |
| Opus 5 | $0.00064 | $0.00939 |
| Sonnet 5 | $0.00026 | $0.00376 |
| Haiku 4.5 | $0.00013 | $0.00188 |
Grade A, and why
binding-site-analysis 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 — 151 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Binding Site Analysis
The step before docking. Every docking skill in this bundle assumes you already know where the ligand goes and that the site is worth the compute — this is where those two assumptions get checked. fpocket runs in seconds and can save a month of screening against a pocket that was never going to bind anything.
Tool: fpocket, MIT, conda install -c conda-forge fpocket.
Alpha-sphere cavity detection by Voronoi tessellation.
Checked against: fpocket 4.x output format.
Read references/fpocket-output.md before parsing a run, references/druggability.md before calling a site druggable or not, and references/cryptic-and-allosteric.md when the answer is "no pocket" — that one is judgement, not syntax.
The three scripts
| Script | Answers |
|---|---|
pocket_report.py |
Which cavities are there, and is any of them worth targeting? |
pocket_box.py |
Where exactly does the docking box go? |
site_compare.py |
Does a pocket appear only when something is bound? |
Score and Druggability Score are different, and pocket1 is not the answer
This is the thing to get right. fpocket reports two numbers per cavity and they measure different things. Score ranks cavities geometrically, and pocket numbering follows it. Druggability Score is a logistic model trained to separate sites with known drug-like ligands from sites without — it is the one that answers "worth a campaign".
They disagree often:
python skills/binding-site-analysis/scripts/pocket_report.py rank --out-dir receptor_out
# fpocket ranks pocket 1 first by Score, but pocket 2 is the most druggable.
pocket druggability score volume apolar_fraction verdict reason
2 0.871 0.31 720.5 0.7143 druggable resembles sites with known drug-like ligands
1 0.183 0.412 980.4 0.3172 poor does not resemble a small-molecule binding site
What ships with it
6 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 · 151 lines · 128 tokens per session scan A 81990055c40e
binding-site-analysis is a skill published in the GitHub repository K-Dense-AI/drug-discovery-agent-skills (28 stars, last pushed 5d ago), licensed MIT. It adds 128 tokens to every session and 1,878 once invoked, about $0.0006 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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