bio-free-energy-calculations

bio-free-energy-calculations is a skill for Claude Code, Codex from GPTomics/bioSkills. It costs 117 tokens per session (4,299 once invoked), scanned A, original, MIT.

A toolkit for estimating how binding free energy changes when a molecule binds a protein. It covers relative calculations between related molecules and absolute calculations for a molecule's overall binding strength.

In plain words
What is it for?
Use it for lead-optimization decisions, comparing related compounds, estimating protein-binding strength, and checking whether simulation results are consistent across linked calculations.
Why use it?
It provides a simulation-based way to compare or estimate binding strength, while making the calculation setup, sampling quality, and uncertainty explicit. The results still depend on the chosen model and validation.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit Use it for lead-optimization decisions, comparing related compounds, estimating protein-binding strength, and checking whether simulation results are consistent across linked calculations.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/gptomics/bioskills/free-energy-calculations
Install

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.

Any agent
npx skills add GPTomics/bioSkills --skill free-energy-calculations
Clone the repo
git clone --depth 1 https://github.com/GPTomics/bioSkills

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for bio-free-energy-calculations

README.md
[![agentmods](https://agentmods.dev/badge/skills/gptomics/bioskills/free-energy-calculations/github.svg)](https://agentmods.dev/skills/gptomics/bioskills/free-energy-calculations)
Your own site
<a href="https://agentmods.dev/skills/gptomics/bioskills/free-energy-calculations"><img src="https://agentmods.dev/badge/skills/gptomics/bioskills/free-energy-calculations/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.

agentmods 80×15 button for bio-free-energy-calculations

Your own site · 80×15
<a href="https://agentmods.dev/skills/gptomics/bioskills/free-energy-calculations"><img src="https://agentmods.dev/badge/skills/gptomics/bioskills/free-energy-calculations.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 117 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 4,299 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00117 $0.04299
Opus 5 $0.00059 $0.02150
Sonnet 5 $0.00023 $0.00860
Haiku 4.5 $0.00012 $0.00430

Measured 12d ago against content hash 2fc3c1aa8f18, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

Grade A, and why

bio-free-energy-calculations 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.

The scan reads SKILL.md. This mod also ships 1 executable file (examples/openfe_rbfe.py), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

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.

Origin

Copies of this mod

1 near-identical copy found in the catalogue:

chemoinformatics/free-energy-calculations/SKILL.md · 302 lines

How it starts

The opening of the file, as written. The whole thing — 302 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Version Compatibility

Reference examples tested with: OpenFE 1.7+, OpenMM 8.1+, GROMACS 2024+, AMBER pmemd 22+, alchemlyb 2.1+, pymbar 4.0+, RDKit 2024.09+.

Before using code patterns, verify installed versions match. If versions differ:

  • Python: pip show <package> then help(module.function) to check signatures
  • CLI: openfe --version; gmx --version; pmemd.cuda --version

If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.

Free Energy Calculations

Predict binding free-energy differences (RBFE) or standard binding free energies (ABFE) using alchemical methods. FEP+ is a commercial workflow and OpenFE is an open-source framework. Accuracy and cost vary substantially with system, perturbation, force field, setup, sampling, and evaluation design; report the protocol and benchmark relevant to the intended decision. The Boltz-2 report includes benchmark-specific comparisons with FEP methods but does not replace prospective validation on the project chemistry.

For docking input poses, see chemoinformatics/virtual-screening. For pose validation before FEP, see chemoinformatics/pose-validation. For ML alternatives, see chemoinformatics/ml-docking-rescoring.

FEP Method Taxonomy

Method Cost / pair Accuracy Use case Fails when
FEP+ (Schrödinger) System- and protocol-dependent GPU cost Published commercial RBFE workflow Commercial lead optimization License and reproducibility constraints
OpenFE RBFE System- and protocol-dependent GPU cost Open-source RBFE with documented protocols Open-source campaigns Mapping/setup/sampling require review
OpenFE ABFE Generally more setup and sampling than one RBFE edge Standard binding free energy No congeneric reference ligand required Restraints and end-state corrections
GROMACS / AMBER RBFE Implementation-dependent Custom alchemical workflows Expert-controlled setup Manual validation burden
FEP-SPell-ABFE Protocol/system-dependent Automated ABFE workflow Evaluate published and project benchmarks Limited adoption
QligFEP v2.1 Protocol/system-dependent Q-based ligand FEP Evaluate published and project benchmarks Different approximations/tooling
MM/PBSA / MM/GBSA Lower-cost endpoint analysis Approximate endpoint score Exploratory within-series comparison Entropy, sampling, and model dependence
Boltz-2 affinity seconds GPU 0.66 Pearson on reported FEP benchmark subset ML alternative; reported >=1000x lower cost Novel chemotypes
ALEPB / EE-AMBER Protocol/system-dependent Specialized methods Evaluate matched evidence Limited tooling

Read the full file on GitHub · 302 lines

Files

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.

Changes

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.

  1. 12d ago First seen · 302 lines · 117 tokens per session scan A 2fc3c1aa8f18

Subscribe to this mod's changes

bio-free-energy-calculations is a skill published in the GitHub repository GPTomics/bioSkills (1,201 stars, last pushed 27d ago), licensed MIT. It adds 117 tokens to every session and 4,299 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.

Related

Other skills, from other repositories

instrument-data-to-allotrope

Convert laboratory instrument output files (PDF, CSV, Excel, TXT) to Allotrope Simple Model (ASM) JSON format or flattened 2D CSV. Use this skill when scientists need to standardize instrument data for LIMS systems, data lakes, or downstream analysis. Supports auto-detection of instrument types. Outputs include full…

anthropics/knowledge-work-plugins · 123 tokens

exploratory-data-analysis

Perform bounded, local exploratory analysis of explicitly supported scientific files. Use for redacted CSV/TSV/JSON profiles; optional NumPy, HDF5, FASTA/FASTQ, and basic image metadata inspection; missingness/leakage audits; outlier and transformation sensitivity; and rigorous EDA report scaffolds. Other domain…

K-Dense-AI/scientific-agent-skills · 83 tokens

matlab

Build, review, migrate, and safely plan MATLAB or GNU Octave numerical workflows, including arrays, tabular/time data, tests, projects, graphics, MAT files, and explicit Python interoperability.

K-Dense-AI/scientific-agent-skills · 42 tokens

phylogenetics

Build and analyze phylogenetic trees using MAFFT (multiple alignment), IQ-TREE 2 (maximum likelihood), and FastTree (fast NJ/ML). Visualize with ETE3 or FigTree. For evolutionary analysis, microbial genomics, viral phylodynamics, protein family analysis, and molecular clock studies.

K-Dense-AI/scientific-agent-skills · 68 tokens

research-engineer

An uncompromising Academic Research Engineer. Operates with absolute scientific rigor, objective criticism, and zero flair. Focuses on theoretical correctness, formal verification, and optimal implementation across any required technology.

davila7/claude-code-templates · 43 tokens

mapping-to-snomed

Maps clinical concept spans extracted by OpenMed to SNOMED CT concepts through a USER-SUPPLIED terminology server (the user's own Ontoserver, Snowstorm, or UMLS/UTS), never a bundled vocabulary. Use when the user wants to code findings, disorders, procedures, body structures, or substances to SNOMED CT, run an ECL…

maziyarpanahi/openmed · 205 tokens