Is active-learning safe to install?

SaferSkills independently audited active-learning (Agent Skill) and scored it 100/100 (green). The audit ran 55 deterministic rules across Security, Supply Chain, Maintenance, Transparency, and Community; it found 0 high-severity and 0 lower-severity findings. The full rule-by-rule trace and per-finding evidence are below. Free, methodology-open.

CategoryWeightCategory scoreContribution

Securityprompt, exec, net, exfil, eval

35%

100

35.0 pts

Supply chainhash, typosquat, maintainer, lockfile

20%

100

20.0 pts

Maintenancestaleness, pinning, CI

15%

100

15.0 pts

TransparencySKILL.md, perms, README

15%

100

15.0 pts

Communityinstalls, verify, response

15%

100

15.0 pts

Findings & checks · 0 flagged

Securityscore 100 · 0 findings

✓—All security checks passedNo findings in this category for the latest scan.pass

Supply chainscore 100 · 0 findings

✓—All supply chain checks passedNo findings in this category for the latest scan.pass

Maintenancescore 100 · 0 findings

✓—All maintenance checks passedNo findings in this category for the latest scan.pass

Transparencyscore 100 · 0 findings

✓—All transparency checks passedNo findings in this category for the latest scan.pass

Communityscore 100 · 0 findings

✓—All community checks passedNo findings in this category for the latest scan.pass

The primary manifest — the file an agent reads to learn what this artifact does.

skills/active-learning/SKILL.md · 1 file

skills/active-learning/SKILL.md3.1 KB · Markdown

name: active-learning

description: Use when designing active learning or closed-loop molecular optimization (DMTA cycles). Covers query strategies (UCB/EI/BALD/QBC), batch DPP selection, docking oracles (Vina/Gnina), BEDROC/EF metrics, and Design-Make-Test-Analyze campaign management.

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

Key Principles

Seed diversity matters — MaxMin or clustering on initial pool prevents early bias
Batch mode requires diversity — greedy top-k collapses; use DPP or greedy submodular
Calibrate before querying — miscalibrated uncertainty → wrong queries (use MAPIE/isotonic)
Track enrichment, not accuracy — AL goal is finding actives fast, not global R²
Stopping criterion — plateau in hit rate OR budget exhausted

Integration with ALKYL Skills

Uncertainty estimates: uncertainty-qsar skill (GP, conformal, deep ensembles)
Diversity selection: chem_diversity.py (MaxMin)
Docking oracle: docking skill (Vina/Gnina)
Library design: generative-design skill (REINVENT + AL reward)
Property filtering: chem_filter.py, chem_batch.py

active-learning— agent skill

Is active-learning safe to install?

Active Learning for Drug Discovery

Purpose

When to Use This Skill

Reference Files

Quick Routing

Core Loop Pattern

Key Principles

Integration with ALKYL Skills

Audit the pieces. Scan the whole. Decide.