Results for “pseudo-c”

Interprets shorthand, quasi-code, and natural language descriptions to produce production-quality code, bridging gaps between varying technical expertise levels.

Performs differential expression analysis on bulk RNA-seq or pseudo-bulk count matrices with QC, PCA, and contrast testing.

Analyzes production code to find gaps in existing tests by reasoning about hypothetical mutations and checking whether tests would catch them.

Design experiments and studies before data collection — choosing a design, randomizing, blocking, and laying out treatment combinations so results are interpretable.

Extracts protocol message flow from source code, RFCs, academic papers, pseudocode, or formal models (ProVerif/Tamarin) and generates Mermaid sequence diagrams with cryptographic annotations.

Reverse engineering assistant that exposes Ghidra capabilities over a Unix domain socket for binary analysis, decompilation, and vulnerability research via CLI commands.

Implement technical and organizational measures required by GDPR, including data mapping, DPIAs, data subject rights management, breach notification, and cross-border transfer mechanisms.

Decodes and replays MariaDB binary logs with correct flags: commented pseudo-SQL vs. executable BINLOG blobs, ANNOTATE_ROWS events, flashback undo SQL, and safe replay via the mariadb client.

Reverse engineer malware binaries using NSA's Ghidra disassembler and decompiler to understand internal logic, cryptographic routines, C2 protocols, and evasion techniques at the assembly and pseudo-C level.

Framework for computational fluid dynamics simulations using Python. Use when running fluid dynamics simulations including Navier-Stokes equations (2D/3D), shallow water equations, stratified flows, or when analyzing turbulence, vortex dynamics, or geophysical flows. Provides pseudospectral methods with FFT, HPC support, and comprehensive output analysis.

Build and operate a Havoc C2 framework for authorized red-team engagements, including team server deployment, evasive Demon agent generation, and post-exploitation.

Exploit Active Directory Certificate Services (AD CS) misconfigurations, specifically ESC1. By requesting a certificate based on a overly permissive template that allows the enrollee to supply a Subject Alternative Name (SAN), an attacker can impersonate highly privileged users (like Domain Admins) and seamlessly escalate privileges across the entire AD environment.

Takes a proofread or apsa-style report file (or any markdown file using **Original:** / **Recommended:** syntax) and rewrites the original source file with git merge conflict markers so the user can accept or reject each suggested edit using VS Code or Positron's built-in merge conflict UI. Branch names are "original" and "claude-edits". Use when asked to apply edits, insert conflict markers, or set up merge resolution for a copy-edit report. Supports an optional @sec-label argument to restrict markers to one section.

Framework for computational fluid dynamics simulations using Python. Use when running fluid dynamics simulations including Navier-Stokes equations (2D/3D), shallow water equations, stratified flows, or when analyzing turbulence, vortex dynamics, or geophysical flows. Provides pseudospectral methods with FFT, HPC support, and comprehensive output analysis.

Simulator.Company Smart Form (CDU) STYLING specialist — authoring complex Less/CSS for Smart Forms: theme tokens, page/form/section layout, component re-skinning, reusable style patterns, responsive and design-system approaches. Use when the user wants to STYLE or RESTYLE an existing Smart Form / CDU app — change its look, build a theme, style a table / sidebar / modal / form, add a design system, fix spacing/colors/fonts, or apply a complex visual design. This skill owns the `style` / `styles/` layer; it reuses the Smart Form tools (pullSmartForm / pushSmartForm / deploySmartForm) but does NOT create form templates — for the data-schema form template use `simulator-forms`, and for page layout / viewModel / backend logic use `simulator-smart-forms` / `simulator-smart-forms-logic`. Activate on: "style a smart form", "CDU styles", "theme the form", "restyle", "custom CSS/Less for the app", "style the table/sidebar/modal/button", "design system for the smart form", "make it look like …", "застилізувати смартформ

Content Security Policy configuration, nonces, and reporting.

Write safe C avoiding memory corruption, buffer overflows, and undefined behavior traps.

Simulates a NeurIPS/SC/ICSE-style peer review with five reviewer personas (HPC, ML, Stats, Reproducibility, Devil's Advocate) that verify every claim against actual result data before submission.

ScepticAgent UI and styles reference. Use whenever the user wants to change the side panel UI, modify highlight colours, update the markdown renderer, work with CSS architecture, add or change category colours, or understand how the sidepanel renders analysis output.

Write safe C code by avoiding memory corruption, buffer overflows, and undefined behavior traps.

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.

沈墨（悬疑剧虚构）认知与表达框架（压缩蒸馏）：创伤反杀叙事、时代灰雾、钢琴意象 触发：漫长的季节 等。虚构；禁止犯罪模仿

Automates sequencing quality control by running FastQC and MultiQC, extracting quality metrics, and generating publication-ready visualizations via a CLI or MCP stdio server.

贺涵（都市剧虚构）认知与表达框架（压缩蒸馏）：导师型男主、职场金句、情感争议 触发：我的前半生 等。虚构；忌情感操控教程

Streams change data capture from Google Cloud Spanner into Redpanda or Kafka using Redpanda Connect's gcp_spanner_cdc input, with partition-aware watermarked delivery and support for Redpanda Enterprise features.

High-fidelity ASCII/Braille rendering via the Risomorphism-1911 pipeline — edge-aware downsampling, presets, quality gates, and eikon mirror workflows

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

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.

Styles web pages with CSS, covering selectors, flexbox, grid, custom properties, and animations for responsive UI components.

Connects AI agents to SurrealDB via built-in MCP (SurrealDB 3.1+) or standalone surrealmcp for database operations.