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 free-energy-perturbationgit 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/free-energy-perturbation)<a href="https://agentmods.dev/skills/k-dense-ai/drug-discovery-agent-skills/free-energy-perturbation"><img src="https://agentmods.dev/badge/skills/k-dense-ai/drug-discovery-agent-skills/free-energy-perturbation/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/free-energy-perturbation"><img src="https://agentmods.dev/badge/skills/k-dense-ai/drug-discovery-agent-skills/free-energy-perturbation.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium analysis-evasion · line 1 Suspicious Unicode normalization or mixed-script contentFix: Review the flagged content for security risks. Ensure no credentials, secrets, or sensitive data are exposed.
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 | $0.00131 | $0.01882 |
| Opus 5 | $0.00066 | $0.00941 |
| Sonnet 5 | $0.00026 | $0.00376 |
| Haiku 4.5 | $0.00013 | $0.00188 |
Grade A, and why
free-energy-perturbation 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 — 163 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Alchemical Free Energy
The rigorous end of affinity prediction. Where a docking score is a heuristic that correlates weakly with potency, FEP computes a real thermodynamic quantity from statistical mechanics — including entropy and explicit water — and reaches about 1 kcal/mol RMSE on a congeneric series. It costs GPU-days for tens of compounds, which places it precisely: immediately before synthesis, choosing which twenty analogues to make.
Tool: OpenFE 1.12, MIT. pip install openfe fetches an unrelated
0.0.12 placeholder — install from conda-forge, docker, or singularity. An NVIDIA GPU is
effectively mandatory.
Checked against: v1.12, June 2026.
Read references/openfe-setup.md before your first run, references/network-design.md before committing GPU time, and references/interpreting-fep.md before quoting a number — that one is judgement, not syntax.
The two scripts
| Script | Answers |
|---|---|
fep_network.py |
What shape is the network, can it be validated, and what will it cost? |
fep_report.py |
Do the results hang together, and what do they say? |
Install the right package
mamba create -n openfe -c conda-forge openfe
PyPI's openfe is a placeholder at version 0.0.12 with no relation to this toolkit. Checked live;
it is the first thing that goes wrong.
A star map cannot be checked
This is the thing to get right. Free energy is a state function, so the sum around any closed loop must be zero. It never is, and the deviation is a direct measure of the error that assumes nothing — no experimental data, no reference, no error model.
A star map has no cycles, so it forfeits the only internal validation FEP offers:
python skills/free-energy-perturbation/scripts/fep_network.py plan --ligands a,b,c,d,e --shape star
# 5 ligands, 4 edges, 0 independent cycle(s)
# no cycles: this network has NO internal error check.
# every result is relative to `a`. A bad reference corrupts the whole map.
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.
- 12d ago First seen · 163 lines · 131 tokens per session scan A 08c5edf3fbaa
free-energy-perturbation 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 131 tokens to every session and 1,882 once invoked, about $0.0007 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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