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 agentmods add skills/kdevos12/alkyl/free-energynpx skills add Kdevos12/ALKYL --skill free-energygit clone --depth 1 https://github.com/Kdevos12/ALKYLWrote 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/kdevos12/alkyl/free-energy)<a href="https://agentmods.dev/skills/kdevos12/alkyl/free-energy"><img src="https://agentmods.dev/badge/skills/kdevos12/alkyl/free-energy.svg" alt="Measured on agentmods" 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 | $0.00063 | $0.01106 |
| Opus 5 | $0.00032 | $0.00553 |
| Sonnet 5 | $0.00013 | $0.00221 |
| Haiku 4.5 | $0.00006 | $0.00111 |
Grade A, and why
free-energy 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.
How it starts
The opening of the file, as written. The whole thing — 92 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Free Energy Calculations
Compute ΔG of binding, solvation, or mutation via alchemical transformations — coupling/decoupling atoms along a λ pathway. Gold standard for lead optimization in drug discovery: accuracy ~1 kcal/mol for congeneric series.
When to Use This Skill
- Predict ΔΔG_bind between two ligands (RBFE / lead optimization)
- Compute absolute ΔG_bind of a ligand to a protein (ABFE)
- Calculate ΔG_solvation or ΔG_hydration for ADME
- Rank compounds from a small congeneric series (~5-50 molecules)
- Validate force field parameters against experimental affinities
- Analyze convergence of FEP simulations (MBAR, overlap matrix)
Key Methods
| Method | Estimator | Windows | Notes |
|---|---|---|---|
| FEP (Zwanzig) | Exponential avg | Any | High variance; avoid for large ΔG |
| TI | Numerical integration of ⟨∂H/∂λ⟩ | 10-20 | Requires smooth integrand |
| BAR | Bennett Acceptance Ratio | Adjacent pairs | Better than TI for same data |
| MBAR | Multistate BAR | All pairs | Best variance; recommended |
| RBFE | Relative: A→B via alchemical | 12-24 λ | Lead optimization |
| ABFE | Absolute: ligand → unbound | ~20 λ | More expensive, independent |
Accuracy Expectations
| System | Typical error | Sim. time per edge |
|---|---|---|
| Congeneric RBFE (neutral) | 0.5-1.5 kcal/mol | 5-10 ns/window |
| RBFE with charge change | 1-3 kcal/mol | 10-20 ns/window |
| ABFE | 1-3 kcal/mol | 20-50 ns/window |
| Solvation ΔG | 0.3-1.0 kcal/mol | 2-5 ns/window |
Quick Start
# pymbar: MBAR from energy matrix (u_kln)
import numpy as np
from pymbar import MBAR
# u_kln[k, l, n] = u_l(x_n^k) / kBT
# k: state from which sample was drawn
# l: state at which energy is evaluated
# n: sample index
K = 12 # number of lambda windows
N_k = np.array([1000] * K) # samples per window
# u_kln shape: (K, K, max(N_k))
mbar = MBAR(u_kln, N_k)
results = mbar.compute_free_energy_differences()
dG = results['Delta_f'][0, -1] # ΔG (kBT units)
ddG = results['dDelta_f'][0, -1] # uncertainty
kBT = 0.5961 # kcal/mol at 298 K
print(f"ΔG = {dG * kBT:.2f} ± {ddG * kBT:.2f} kcal/mol")
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.
- 6d ago First seen · 92 lines · 63 tokens per session scan A c10f6f3a51a7
free-energy is a skill published in the GitHub repository Kdevos12/ALKYL (6 stars, last pushed 5mo ago), licensed MIT. It adds 63 tokens to every session and 1,106 once invoked, about $0.0003 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 2026-08-31.
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