Skill UI

Provides comprehensive methods for handling sensitive customer data within Webflow, including detecting and redacting PII from form submissions and e-commerce orders. This guide ensures data processing adheres to global regulations like GDPR and CCPA, and establishes robust data retention policies for secure development.

Analyzes Screenpipe history windows to detect repeated research workflows, match existing skills, and stage new skill drafts for review. Requires local Screenpipe daemon on localhost:3030 with optional LLM backend. Privacy-focused with data redaction before LLM processing. Use only when user explicitly requests workflow analysis.

A comprehensive playbook detailing advanced techniques for exploiting misconfigured Active Directory Access Control Lists (ACLs). It covers BloodHound enumeration, abusing dangerous ACE types (GenericAll, WriteDACL), conducting DCSync attacks, leveraging shadow credentials, and exploiting GPO/LAPS weaknesses for privilege escalation and lateral movement in a deep dive for security professionals.

This utility detects and redacts sensitive personally identifiable information (PII) from screenshots and images. It can mask various data types, including IDs, credit card numbers, API keys, phone numbers, and emails. Crucially, all processing is conducted locally and offline, ensuring that sensitive data never leaves the user's machine. It is essential for secure debugging, data sharing, and analysis workflows.

This skill probes website authentication flows to identify security vulnerabilities like CSRF, redirect leaks, weak cookies, and OAuth misconfigurations. It automates login processes, analyzes trajectories, and generates structured findings reports. Suitable for pre-deployment audits, investigating token leaks, and regression testing. Sensitive data is quarantined and never exposed unredacted.

This comprehensive API provides a robust solution for automated document workflows. Users can perform format conversions (PDF, DOCX, XLSX, etc.), extract structured data (text, tables), perform OCR on scanned documents, redact sensitive information (PII), add watermarks, and apply digital signatures. Suitable for building automated, secure document processing pipelines.

Provides guidelines for creating robust, verifiable, and reusable Electronic Design Automation (EDA) artifacts for submission to academic conferences like DAC. The focus shifts from chasing conference badges to ensuring community trust and scientific reproducibility. Key elements include pinning the toolchain, using standard benchmarks, providing clear setup instructions, and archiving the release in a DOI-issuing repository.

A comprehensive guide on structuring responses to peer reviews for top-tier academic conferences (e.g., DAC, ICCAD). It provides strategies for writing rebuttals, transforming reviews into actionable fixes, and adapting the response style for both conference resubmission and journal formats, emphasizing evidence over argument.

A comprehensive guide for authors preparing the final, camera-ready manuscript for major academic conferences. It details crucial post-acceptance steps, including systematically de-anonymizing the paper, restoring author affiliations and funding details, correctly implementing ACM copyright forms and DOI stamps, adhering to strict page budgets, and verifying all metadata for proper archival indexing in the ACM Digital Library.

This guide provides a comprehensive checklist for evaluating Electronic Design Automation (EDA) research manuscripts for top-tier conferences like DAC. It emphasizes the necessity of using standard, recognized benchmark suites (e.g., ISPD, EPFL, TAU), comparing results against the true State-of-the-Art (SOTA), reporting full PPA metrics and runtime, and implementing ablation studies to ensure fairness, reproducibility, and generalizability, especially for ML-based methods.

A detailed guide on how to structure and position the 'Related Work' section for high-stakes academic conferences like DAC, ICCAD, and DATE. It teaches authors to move beyond simple surveys and instead create a 'delta statement,' explicitly defining the limitations of prior art and quantifying the unique advances of the current work in terms of Quality of Results (QoR).

A comprehensive guide for establishing research reproducibility in Electronic Design Automation (EDA). It details best practices for pinning benchmark suites, disclosing specific tool versions (open/commercial), documenting technology nodes (PDKs), and handling stochastic data flows (seeds, variance). Essential for ensuring that semiconductor design claims and ML-for-EDA results are credible and verifiable by the academic community.