「fingerprints」の検索結果

Pythonic wrapper around RDKit with simplified interface and sensible defaults. Preferred for standard drug discovery: SMILES parsing, standardization, descriptors, fingerprints, clustering, 3D conformers, parallel processing. Returns native rdkit.Chem.Mol objects. For advanced control or custom parameters, use rdkit directly.

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NGS analysis toolkit. BAM to bigWig conversion, QC (correlation, PCA, fingerprints), heatmaps/profiles (TSS, peaks), for ChIP-seq, RNA-seq, ATAC-seq visualization.

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NGS CLI for ChIP/RNA/ATAC-seq. BAM→bigWig with RPGC/CPM/RPKM, sample correlation/PCA, heatmaps/profiles around features, fingerprints. For alignment use STAR/BWA; for peak calling use MACS2.

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Read, write, and edit ChemDraw CDX/CDXML files with RDKit's rdkit.Chem.rdChemDraw plus direct XML editing, always paired with a rendered PNG. Parse molecules and reactions from .cdxml/.cdx, write structures with good 2D depiction, and hand-build or modify the parts RDKit cannot write: reaction arrows, plus signs, schemes/steps, and text/labels. Use for reaction schemes, synthesis routes, mechanisms, retrosynthesis, or SI figures. Critical: RDKit writes structures only — round-tripping a reaction through a Mol silently drops arrows and text; this skill shows the XML layer that preserves them. For pure molecular analysis (descriptors, fingerprints, SMARTS) use rdkit-cheminformatics; for multi-format 3D conversion use openbabel.

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Molecular featurization hub (100+ featurizers) for ML. SMILES to fingerprints (ECFP, MACCS, MAP4), descriptors (RDKit 2D, Mordred), pretrained embeddings (ChemBERTa, GIN, Graphormer), pharmacophores. Scikit-learn compatible with parallelization/caching. For QSAR, virtual screening, similarity, and molecular DL.

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Deep learning for drug discovery. 60+ models (GCN, GAT, AttentiveFP, MPNN, ChemBERTa, GROVER), 50+ featurizers, MoleculeNet benchmarks, HPO, transfer learning. Unified load-featurize-split-train-evaluate API. For fingerprints use rdkit-cheminformatics; for featurization-only use molfeat.

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Pythonic RDKit wrapper with sensible defaults for drug discovery. SMILES parsing, standardization, descriptors, fingerprints, similarity, clustering, diversity selection, scaffold analysis, BRICS/RECAP fragmentation, 3D conformers, and visualization. Returns native rdkit.Chem.Mol. Prefer datamol for standard workflows; use RDKit directly for advanced control.

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Guides agents using the datamol Python package for RDKit-first molecular IO, preparation, fingerprints, similarity, structure generation, visualization, and utility workflows.

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Use Chemprop 2.2.3 for molecular property prediction: CLI training, prediction, fingerprints, data validation, Python APIs, reaction/atom-bond tasks, uncertainty, hpopt, and conversion workflows.

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This skill should be used when the user asks to "create a style sheet", "style guide", "house style", "keep the voice consistent", "voice drift", "British or American spelling", "character voices", "lint the prose", "prose check", "filter words", "said-bookisms", "overused words", "repeated phrases", "similar character names", "voice fingerprints", or wants to record and enforce the voice and surface conventions of a story project. NOT for rewriting dialogue to make the voices distinct when everyone sounds the same (use line-editing), or a character's profile or arc (use character-management).

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Detects and removes AI writing patterns from text. Use when polishing drafts, cleaning AI-generated content, or ensuring writing sounds authentically human. Invoke with "run through slop detector", "clean this up", or "remove AI fingerprints".

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Think and work like an expert Cheminformatician. Use when a task calls for Cheminformatician judgment. Reasons from Standard InChI identity and RDKit sanitization through ChEMBL pChEMBL harmonization, Morgan/ECFP fingerprints, scaffold splits and applicability domains, while treating tautomer collapse, KNIME-vs-Python canonicalization drift, and random-split QSAR leakage as first-class failure modes.

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Hybrid fingerprint + LLM pipeline for bug classification, deduplication, and ticket generation. Normalizes CI logs, creates stable fingerprints, clusters near-duplicates, then uses LLM for severity classification and ticket writing. Includes bug reporting templates and severity/priority matrix. Use when: "bug triage," "classify bugs," "failure analysis," "auto-classify," "CI failures," "bug report," "defect template." Not for: runtime self-healing of one flaky locator — use test-reliability. Not for: designing new tests from production telemetry — use observability-driven-testing. Related: qa-metrics, qa-dashboard, ci-cd-integration, qa-project-context.

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De-slop pass for any text. Scans for the statistical fingerprints of AI writing (negative parallelism / "not X but Y", em-dash density, rule-of-three, false ranges, puffery vocabulary, uniform cadence, hedged both-sidesing), rewrites by meaning, then re-scans until the text is clean and sits in the right register for its genre: academic article, tweet, reddit post, email, blog, docs, marketing. Use when the user says "deslopify", "deslop", "de-slop this", "remove the AI tells", "humanize this", "make this not sound like AI", or invokes /deslopify. Also use before publishing any agent-drafted prose.

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Cheminformatics toolkit catalog for small molecules. Covers RDKit/OpenChemLib calculations, descriptors, fingerprints, similarity/substructure search, R-group, MMP, scaffold tree, chemical space, MPO, reactions, and ADMET predictions. Open this skill when the user asks about computing molecular properties, comparing/searching molecules, or predicting ADMET/tox.

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Probe subdomain list with httpx/dnsx/naabu to identify live hosts, open ports, status codes, titles, and basic fingerprints. Use after subdomain-enum produces a sub list. Output feeds into fingerprinting and content discovery.

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Cheminformatics toolkit for fine-grained molecular control. Parse and write SMILES, SDF, MOL and InChI; compute descriptors (MW, LogP, TPSA, QED, Bertz); build fingerprints (Morgan/ECFP, RDKit, MACCS, atom pair, torsion) and score Tanimoto, Dice or cosine similarity; run SMARTS substructure search and reaction SMARTS; generate 2D depictions and ETKDG 3D conformers; extract Murcko scaffolds and canonical hashes; control sanitization and stereochemistry directly. Also trigger on rdkit, Chem.MolFromSmiles, rdFingerprintGenerator, SDMolSupplier, SMARTS query, ETKDG, or FilterCatalog. For standard workflows with a simpler interface use the datamol skill, which wraps RDKit; use rdkit for advanced control, custom sanitization, and specialized algorithms.

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Molecular featurization hub with one consistent interface over 100+ featurizers. Fingerprints (ECFP/Morgan, MACCS, atom pair, topological torsion, Avalon, RDKit, ERG), RDKit and Mordred descriptor sets, pharmacophore and 3D shape descriptors, scaffold keys, and pretrained embeddings (ChemBERTa, ChemGPT, MolT5, GIN, Graphormer) through a common transformer API with caching and parallelism. Use this skill to convert SMILES into model-ready feature matrices for QSAR, virtual screening, and molecular ML, and to choose between featurizer families. Also trigger on molfeat, MoleculeTransformer, FPVecTransformer, PretrainedHFTransformer, molfeat model store, or featurizer selection.

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Pythonic wrapper around RDKit with a simplified interface and sensible defaults. Preferred for standard drug discovery work — SMILES/SELFIES/InChI conversion, molecule standardization and sanitization, descriptors, ECFP and other fingerprints, Tanimoto distance matrices, Butina clustering and diverse subset picking, Bemis-Murcko scaffolds and scaffold splits, BRICS/RECAP fragmentation, 3D conformer generation, SDF/CSV/Excel and cloud I/O, and parallel processing via n_jobs. Returns native rdkit.Chem.Mol objects, so it composes with RDKit throughout. Also trigger on datamol, `import datamol as dm`, dm.to_mol, dm.standardize_mol, dm.cluster_mols, or dm.pick_diverse. For advanced control or custom parameters, use the rdkit skill directly.

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Develops comprehensive threat actor profiles for APT groups, criminal organizations, and hacktivist collectives by aggregating TTP documentation, historical campaign data, tooling fingerprints, and attribution indicators from multiple intelligence sources. Use when briefing executives on sector-specific threats, updating threat model assumptions, or prioritizing defensive controls against specific adversaries. Activates for requests involving MITRE ATT&CK Groups, Mandiant APT profiles, CrowdStrike adversary naming, or sector-specific threat briefings.

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Re-read pinned on-chain facts and watch documentary source fingerprints behind failure scenarios monthly, reporting drift without approving or changing scenario records automatically.

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Computes cryptographic and fuzzy fingerprints for malware samples: MD5/SHA-1/SHA-256 for exact identity, plus import hashing (imphash), section hashing, and fuzzy hashes (ssdeep, TLSH) to cluster related variants. Activates for requests to hash a sample, compute imphash, or fingerprint files for similarity clustering.

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Fingerprints unknown binaries using rabin2 — detects architecture (ARM, x86, MIPS), ABI, libc, dependencies, and strings of interest. Use when first encountering an unknown binary, ELF file, or firmware blob, or when you need architecture or ABI information before committing to deeper analysis.

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Use when editing Chinese web fiction, narrative prose, or story drafts that sound AI-generated, overly polished, formulaic, explanatory, or too symmetrical; use to detect AI-writing fingerprints and revise only wording while preserving plot, facts, characters, names, and intent.

日本語の概要は準備中です。原文の説明を表示しています。