Results for “pe-parsing”

Detect suspicious Windows service installations (MITRE ATT&CK T1543.003) by parsing System event logs for Event ID 7045, analyzing service binary paths, and identifying indicators of persistence mechanisms.

Deploy and operate CAPEv2 sandbox for automated malware analysis with behavioral monitoring, payload extraction, configuration parsing, and anti-evasion capabilities.

Performs static analysis of Windows PE malware samples using PEStudio to examine file headers, imports, strings, resources, and indicators without executing the binary.

Build and debug Retrieval-Augmented Generation pipelines — chunking, embedding, retrieval, reranking

Perform static analysis of malicious PDF documents using peepdf, pdfid, and pdf-parser to extract embedded JavaScript, shellcode, and suspicious objects.

基于论文文本的通用读论文助手。用户提供论文文本（文件路径或直接粘贴），解答各类读论文需求——总结、精读、内容问答、概念解释、批判性分析等，并将结果以 Markdown 写入当前工作目录。触发词：读论文、论文总结、精读这篇论文、帮我分析这篇论文、这篇论文讲了什么、论文问答、论文笔记。输入为纯文本/Markdown 论文内容；不做论文检索下载、不做扫描件 OCR、不做论文写作降重。

Reviews parsers, deserialization, uploads, archives, YAML/JSON/XML/pickle, paths, templates, SSRF, and unsafe loaders.

Parse PDFs, Office files, and images locally with layout-preserved text, bounding boxes, OCR, and page screenshots for RAG and multimodal agents.

Benchmarks automatic post-editing (APE) models on WMT'18 SMT, SubEdits, and MLQE-PE datasets, reporting BLEU, ChrF, and TER scores computed with SacreBLEU and TERCOM.

Use when converting `PubmedArticle` XML from EDirect into APA-style citation text or APA-structured XML for downstream parsing.

Write, review, debug, test, and optimize Python Polars code with version-grounded object types, schemas, and execution boundaries.

Personal protective equipment tracker. Use when json ppe tasks, csv ppe tasks, checking ppe status.

Fine-tune large language models by training less than 1% of parameters using LoRA, QLoRA, and 25+ adapter methods, enabling efficient adaptation on limited GPU memory.

Import object detection models from HuggingFace or NVIDIA NGC into a DeepStream pipeline with automated ONNX download, TensorRT engine build, custom parser, multi-stream benchmark, and PDF report generation.

Evaluates RAG pipelines using a filesystem-based benchmark with corpus/ and train.json, running evaluate_rag.py to tune retrieval and generation flags and interpret RAGAS metrics.

Train and analyze Sparse Autoencoders (SAEs) using SAELens to decompose neural network activations into interpretable features for mechanistic interpretability research.

Configure and manage - Parse email parser operations. Auto-activating skill for Business Automation. Triggers on: email parser, email parser Part of the Business Automation skill category. Use when working with email parser functionality. Trigger with phrases like "email parser", "email parser", "email".

Mine proof-heavy papers, notes, PDFs, Markdown, or LaTeX sources into source-indexed proof-material files. Use when the user wants to extract technically nontrivial lemmas, estimates, definitions, dependencies, reductions, constructions, or proof strategies, preserve paper locations and stable material IDs, run DeepSeek screening/backtests, and update the proof-material index rather than writing directly to proof-usage.

Parameter-efficient fine-tuning for LLMs using LoRA, QLoRA, and 25+ methods. Use when fine-tuning large models (7B-70B) with limited GPU memory, when you need to train <1% of parameters with minimal accuracy loss, or for multi-adapter serving. HuggingFace's official library integrated with transformers ecosystem.

Read, extract, merge, split, rotate, watermark, encrypt, and OCR PDF files using Python libraries and command-line tools.

Provides nine skills for Nature-journal-family academic publishing, covering figure creation, prose polishing, manuscript writing, citation formatting, data availability statements, paper reading, reviewer responses, paper-to-PPT conversion, and academic search via an MCP server.

Exploit-phase writing — shapes raw material into an article paragraph by paragraph. Use when the user has a raw-material pile and wants the final article written.

Expert patterns for Neon serverless Postgres, branching, connection pooling, and Prisma/Drizzle integration Use when: neon database, serverless postgres, database branching, neon postgres, postgres serverless.

Comprehensive guide to bpe. Master the concepts, implementation, best practices, and real-world applications of bpe in professional environments.

Parameter-efficient fine-tuning for LLMs using LoRA, QLoRA, and 25+ methods. Use when fine-tuning large models (7B-70B) with limited GPU memory, when you need to train <1% of parameters with minimal accuracy loss, or for multi-adapter serving. HuggingFace's official library integrated with transformers ecosystem.

Provides guidance for training and analyzing Sparse Autoencoders (SAEs) using SAELens to decompose neural network activations into interpretable features. Use when discovering interpretable features, analyzing superposition, or studying monosemantic representations in language models.

Post-draft pass that expands a drafted article to capture the FULL keyword family — keywords the page already or could rank for, plus the Ahrefs Content Gap (keywords competitors rank for but we don't) — by weaving the worthwhile ones in as natural added paragraphs/sections. NOT keyword stuffing. Triggered after /draft, before /quality-check.

Provides guidance for training and analyzing Sparse Autoencoders (SAEs) using SAELens to decompose neural network activations into interpretable features. Use when discovering interpretable features, analyzing superposition, or studying monosemantic representations in language models.

Runs an evidence-first, read-only workflow over large codebases via an external JSONL REPL server, retrieving only minimal file slices and greps to produce cited architecture and audit reports.

Fast in-memory DataFrame library for datasets that fit in RAM. Use when pandas is too slow but data still fits in memory. Lazy evaluation, parallel execution, Apache Arrow backend. Best for 1-100GB datasets, ETL pipelines, faster pandas replacement. For larger-than-RAM data use dask or vaex.

Parameter-efficient fine-tuning for LLMs using LoRA, QLoRA, and 25+ methods. Use when fine-tuning large models (7B-70B) with limited GPU memory, when you need to train <1% of parameters with minimal accuracy loss, or for multi-adapter serving. HuggingFace's official library integrated with transformers ecosystem.

Process data with high-performance DataFrames using Polars' expression-based API, lazy evaluation, and parallel execution for ETL, analytics, and pandas migration.