Machine learning on FPGAs using HLS

Open-source Linux performance suite for engineers—profiling and tuning workloads and system configurations.

🤖 Provide 70+ ready-to-use, platform-agnostic AI agent skills for improving tasks like code review, security, and data analysis.

Libva is an implementation for VA-API (Video Acceleration API)

Concise Binary Object Representation (CBOR) Library

Streamline FPGA development with 8 Vivado/Vitis skills for HLS, RTL, synthesis, constraints, timing, and debug workflows

61 ready-to-use AI agent skills for go-to-market teams, installable in Claude Code with a bootstrap script and onboarding flow.

Cyber threat intelligence and OSINT analysis toolkit. Runs structured investigations and delivers analyst-grade intelligence products with sourced, trust-scored findings. Use for OSINT and CTI cases, digital-footprint and exposure review, domain/subdomain/DNS/certificate recon, web-infrastructure pivoting (favicon hashes, tracker IDs, TLS certs, phishing-kit fingerprinting, campaign clustering), username/email/phone enumeration, breach and infostealer-log triage, image forensics, geolocation, crypto-wallet and IBAN/bank-account tracing, darknet search, M365/Azure and SaaS tenant recon, China/Sinophone recon (ICP filings, PRC corporate registries, Baidu/FOFA/Quake/ZoomEye), vulnerability and ransomware lookup, threat modeling, PII redaction, and structured reporting. Commands include /case, /sweep, /query, /webpivot, /username, /phone, /email-deep, /breach-deep, /icp, /cn-corp, /iban, /stealer-log, /exposure, /threat-model, /report, /brief, /redact, /apikeys.

This repository intends to enable autonomous drone delivery with the Intel Aero RTF drone and PX4 autopilot. The code can be executed both on the real drone or simulated on a PC using Gazebo. Its core is a robot operating system (ROS) node, which communicates with the PX4 autopilot through mavros. It uses SVO 2.0 for visual odometry, WhyCon for visual marker localization and Ewok for trajectoy planning with collision avoidance.

Post-interview review — reconstructs the Q&A from transcript or memory, captures interviewer intel and next-round forecasts, and turns the round into a revision list for the script. Use when the user says they just finished an interview, 刚面完 / 复盘一下 / 面试录音转文字给你 / 这轮被问了什么, or pastes an interview transcript or recollection. Do NOT use for practice sessions (mock-interview) or pre-interview research (job-intel).

Runs a realistic mock interview in chat — builds an interviewer persona from the intel file, asks one question at a time with pressure follow-up chains, then scores the candidate on structured delivery, evidence density, role fit, follow-up resilience and recitation-smell, writing a report to the workspace. Use when the user wants practice, says 模拟面试 / 陪我练一下 / 当一回面试官 / 压力面 / mock interview, or when an interview is within 48 hours and the script is ready. Do NOT use for writing answers (interview-script) or reviewing a real past interview (debrief).

Deconstructs a job description and builds interview intelligence — JD-to-evidence matching, company and product research, interviewer profiling, recruiter-vs-team discrepancy checks, and next-round question forecasting. Use when the user shares a JD or job link, names a company they will interview with, asks to analyze a position (拆解 JD / 分析这个岗位 / 查一下这家公司 / 面试官是谁 / 下一轮会考什么), or after a round when the next interviewer is known. Do NOT use for writing the answer script itself (use interview-script) or post-interview review (use debrief).