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 Kdevos12/ALKYL --skill active-learninggit 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/active-learning)<a href="https://agentmods.dev/skills/kdevos12/alkyl/active-learning"><img src="https://agentmods.dev/badge/skills/kdevos12/alkyl/active-learning/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/kdevos12/alkyl/active-learning"><img src="https://agentmods.dev/badge/skills/kdevos12/alkyl/active-learning.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00770 |
| Opus 5.5 | $0.00025 | $0.00308 |
| Sonnet 5.5 | $0.00013 | $0.00154 |
| Haiku 4.5 | $0.00006 | $0.00077 |
Grade A, and why
active-learning 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 2mo 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 — 71 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Active Learning for Drug Discovery
Purpose
Closed-loop molecular optimization: iteratively query the most informative compounds, label with assay/oracle, retrain model. Accelerates hit-to-lead and lead optimization by minimizing wet-lab experiments.
When to Use This Skill
- Building a surrogate model to replace expensive docking/assay calls
- Running a DMTA (Design-Make-Test-Analyze) loop
- Accelerating virtual screening with AL-ranked acquisition
- Combining QSAR uncertainty with experimental prioritization
Reference Files
Load specific references on demand:
| File | Content |
|---|---|
references/al-theory.md |
Query strategies, acquisition functions, convergence, pool vs stream |
references/molecular-al.md |
Molecular representations, batch AL, diversity-reweighted sampling |
references/uncertainty-integration.md |
GP/conformal/ensemble signals → acquisition, calibration |
references/docking-al.md |
Surrogate docking oracle, VS acceleration, Logloss/BEDROC metrics |
references/dmta-loop.md |
Full DMTA cycle, stopping criteria, experiment prioritization, case studies |
Quick Routing
"I want to find actives with fewest assay calls"
→ al-theory.md (query strategy) + molecular-al.md (batch AL)
"I want to accelerate a docking campaign"
→ docking-al.md (surrogate oracle)
"I have GP/conformal uncertainty, want to plug into AL loop"
→ uncertainty-integration.md
"I'm running a real DMTA cycle with a CRO"
→ dmta-loop.md
Core Loop Pattern
# Canonical active learning loop
labeled_pool = initial_dataset # seed: 50–200 diverse cpds
unlabeled_pool = virtual_library # 10k–1M candidates
for round in range(n_rounds):
model.fit(labeled_pool.X, labeled_pool.y)
scores = acquisition_fn(model, unlabeled_pool.X) # uncertainty / EI / UCB
batch = select_batch(unlabeled_pool, scores, k=batch_size)
labels = oracle(batch) # assay / docking / human expert
labeled_pool = labeled_pool + (batch, labels)
unlabeled_pool = unlabeled_pool - batch
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.
- 2mo ago First seen · 71 lines · 63 tokens per session scan A e8d19859110b
active-learning is a skill published in the GitHub repository Kdevos12/ALKYL (7 stars, last pushed 7mo ago), licensed MIT. It adds 63 tokens to every session and 770 once invoked, about $0.0003 per session on Opus 5.5. 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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