Results for “squashfs”

Molecular featurization for ML (100+ featurizers). ECFP, MACCS, descriptors, pretrained models (ChemBERTa), convert SMILES to features, for QSAR and molecular ML.

Generates complete, production-ready SaaS project boilerplate including authentication, database schemas, billing integration, API routes, and a working dashboard using Next.js 14+ App Router, TypeScript, Tailwind CSS, shadcn/ui, Drizzle ORM, and Stripe. Use when the user wants to create a new SaaS app, start a subscription-based web project, scaffold a Next.js application, or mentions terms like starter template, boilerplate, new project, or wiring up auth and payments.

Csrf reference tool. Use when working with csrf in devtools contexts.

Generates a complete, production-ready SaaS project boilerplate with Next.js 14+, TypeScript, Tailwind CSS, shadcn/ui, Drizzle ORM, and Stripe, including authentication, database schemas, billing integration, API routes, and a working dashboard.

Molecular featurization for ML (100+ featurizers). ECFP, MACCS, descriptors, pretrained models (ChemBERTa), convert SMILES to features, for QSAR and molecular ML.

Featurização molecular para ML (100+ featurizadores). ECFP, MACCS, descritores, modelos pré-treinados (ChemBERTa), converter SMILES em features, para QSAR e ML molecular.

Designs and maintains versioned reusable task sequences in Financial Services Cloud using Action Plan templates for client onboarding, account opening, annual review preparation, and compliance tasks.

Analyze WAF logs from ModSecurity, AWS WAF, or Cloudflare to detect SQL injection attack campaigns, classify injection types, and generate incident reports with OWASP classification.

Use when annotating VCF files with flanking sequence information (INFO/FS tag) or masking regions/variants in flanking sequences.

Featurizes molecules for machine learning with molfeat (100+ featurizers) — ECFP/MACCS/MAP4 fingerprints, RDKit and Mordred physicochemical descriptors, and pretrained embeddings (ChemBERTa, ChemGPT, GIN) exposed as scikit-learn transformers that convert SMILES into feature vectors. Use when turning molecules into ML-ready feature matrices for QSAR/QSPR or virtual screening, or benchmarking fingerprint against descriptor and embedding representations; for training models and MoleculeNet benchmarks on those features prefer alterlab-deepchem, and for low-level fingerprint or descriptor primitives prefer alterlab-rdkit. Part of the AlterLab Academic Skills suite.

Explains the Financial Services Cloud data model, covering managed-package and Core FSC object structures, household relationships, financial account ownership, and the rollup framework, with SOQL query patterns.

Dcf reference tool. Use when working with dcf in finance contexts.

Scaffold a new ACF Composer block: ACF block, ACF Composer, block registration, Gutenberg block, block view, Blade block, InnerBlocks, block.json, block scaffold, acf:block, custom block, block category, block icon, block supports, block variants, block styles, is-style, create-block, enqueue block, block CSS, block JS, custom element, block preview parity — full custom element architecture with scoped CSS and theme variations.

Molecular featurization for ML (100+ featurizers). ECFP, MACCS, descriptors, pretrained models (ChemBERTa), convert SMILES to features, for QSAR and molecular ML.

Use when implementing sqs functionality with production-grade patterns and safeguards.

Molecular featurization for ML (100+ featurizers). ECFP, MACCS, descriptors, pretrained models (ChemBERTa), convert SMILES to features, for QSAR and molecular ML.

Convert chemical structures (SMILES or RDKit molecules) into numerical representations for machine learning, covering 100+ featurizers including ECFP, MACCS, descriptors, and pretrained models like ChemBERTa, with support for QSAR modeling and virtual screening.

Analyze the structure, boot chain, and update mechanism of a firmware image, then trace the shortest path to the decisive artifact or secret.

Flash FPGA firmware on HSB Lattice boards and Leopard Imaging VB1940 cameras connected to NVIDIA devkits, with safety checks and multi-step upgrade/downgrade procedures.

Traces host-to-host pivot chains in Windows CTF challenges by recovering Kerberos tickets, credential material, and privilege edges across WinRM, SMB, and RDP.

Specialized CTF workflow for reverse engineering, malware analysis, DFIR, firmware, pwnable, and native exploit challenges under sandbox assumptions.

Facebook's library for efficient similarity search and clustering of dense vectors. Supports billions of vectors, GPU acceleration, and various index types (Flat, IVF, HNSW). Use for fast k-NN search, large-scale vector retrieval, or when you need pure similarity search without metadata. Best for high-performance applications.

Routes finance requests to the appropriate skill: financial-analyst for ratio analysis, DCF valuation, and budget variance, or saas-metrics-coach for ARR/MRR, churn, CAC/LTV, and NRR.

RICE feature prioritization with scoring and capacity planning. Usage: /rice prioritize <features.csv> [options]

Foundational SPARQL patterns covering RDF querying, triple patterns, graph patterns, and semantic web fundamentals. Use when querying RDF data or working with knowledge graphs. This is the entry point for SPARQL development.

Facebook's library for efficient similarity search and clustering of dense vectors. Supports billions of vectors, GPU acceleration, and various index types (Flat, IVF, HNSW). Use for fast k-NN search, large-scale vector retrieval, or when you need pure similarity search without metadata. Best for high-performance applications.

Scaffold a new mobile-first page using StyleSeed Toss layout patterns, section rhythm, and existing shell components.

Use when planning a USENIX FAST project timeline from venue fit through choosing a Spring or Fall deadline, double-blind submission, the author-response period, shepherding or a one-shot revision, artifact evaluation, and the open-access camera-ready, with backward-planning offsets tuned to storage evaluation and honest handling of the two-deadline cycle.

Send a push notification via ntfy.sh or Pushover

Parse FCS (Flow Cytometry Standard) files v2.0-3.1. Extract events as NumPy arrays, read metadata/channels, convert to CSV/DataFrame, for flow cytometry data preprocessing.

Facebook's library for efficient similarity search and clustering of dense vectors. Supports billions of vectors, GPU acceleration, and various index types (Flat, IVF, HNSW). Use for fast k-NN search, large-scale vector retrieval, or when you need pure similarity search without metadata. Best for high-performance applications.

【司库】SKILL.md — 财务技能系统 v1.0

Detect and exploit Server-Side Request Forgery vulnerabilities by identifying user-controlled URL parameters, testing for internal service access, cloud metadata endpoints, and file scheme reads, with bypass techniques for common filters.

Parse FCS (Flow Cytometry Standard) files v2.0-3.1. Extract events as NumPy arrays, read metadata/channels, convert to CSV/DataFrame, for flow cytometry data preprocessing.

Detect and exploit Cross-Site Request Forgery vulnerabilities by testing for missing or predictable CSRF tokens, absent SameSite cookie attributes, and JSON endpoints accepting text/plain Content-Type, with payloads and bypass techniques for security testing.