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/fbddnpx skills add Kdevos12/ALKYL --skill fbddgit clone --depth 1 https://github.com/Kdevos12/ALKYLWhat 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 | $0.00064 | $0.00874 |
| Opus 5 | $0.00032 | $0.00437 |
| Sonnet 5 | $0.00013 | $0.00175 |
| Haiku 4.5 | $0.00006 | $0.00087 |
Grade A, and why
fbdd 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 2d 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 — 73 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Fragment-Based Drug Design (FBDD)
Purpose
Design and analyze fragment libraries, compute ligand efficiency metrics, perform fragment docking, and execute fragment-to-lead elaboration (growing, linking, merging) with computational support.
When to Use This Skill
- Building or filtering a fragment library
- Computing LE/LLE/LLEAT efficiency metrics
- Docking fragments into a target (weak binding, requires special settings)
- Growing a fragment hit toward lead-like compounds
- Merging two fragment hits sharing a common substructure
- Analyzing X-ray fragment screening data
Reference Files
| File | Content |
|---|---|
references/fbdd-theory.md |
Fragment rules (Rule of 3), LE/LLE/LLEAT/BEI/SEI, Hann complexity model, fragment-to-lead strategies (grow/link/merge), success stories |
references/fragment-library.md |
Library design: RDKit filters (Ro3/PAINS/flatness/rigidity), 3D sp3 character, commercial sources, diversity selection, quality checks |
references/fragment-docking.md |
Low-MW docking pitfalls, Vina fragment settings, ROCS shape screening, Smina fragment mode, pose clustering, hotspot validation |
references/fragment-growing.md |
Scaffold growing (R-group enumeration, MMPA vectors), fragment merging (MCS-based), FBDD-aware REINVENT, SynthesizabilityOracle, elaboration scoring |
references/efficiency-metrics.md |
LE/LLE/LLEAT/BEI/SEI formulas, efficiency evolution plots, Abad-Zapatero plots, LELP, GE (group efficiency), metric-driven SAR |
Quick Routing
"Build a fragment library" → fragment-library.md
"Dock fragments into my target" → fragment-docking.md
"I have a fragment hit, want to grow it" → fragment-growing.md
"Track efficiency as I optimize" → efficiency-metrics.md
"What makes a good fragment?" → fbdd-theory.md
Core Concept: Rule of 3
| Property | Fragment (Ro3) | Lead-like | Drug-like (Ro5) |
|---|---|---|---|
| MW | ≤ 300 Da | ≤ 400 Da | ≤ 500 Da |
| cLogP | ≤ 3 | ≤ 4 | ≤ 5 |
| HBD | ≤ 3 | ≤ 4 | ≤ 5 |
| HBA | ≤ 3 | ≤ 8 | ≤ 10 |
| PSA | — | ≤ 120 Ų | — |
| Rotatable bonds | ≤ 3 | ≤ 7 | ≤ 10 |
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.
- 2d ago First seen · 73 lines · 0 tokens per session scan A 5a7d317999d2
fbdd is a skill published in the GitHub repository Kdevos12/ALKYL (6 stars, last pushed 5mo ago), licensed MIT. It adds 64 tokens to every session and 874 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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