Skill UI

Tool to embed C2PA (Content Provenance) manifests into AI-generated multimedia assets (image, video, audio, PDF). This mechanism adds a machine-readable provenance trail detailing the generator, prompt, and review history. Essential for complying with global regulations like the EU AI Act Article 50, ensuring verifiable digital authenticity, and building brand trust.

A comprehensive tool for managing PR crises and reputational threats. It provides rapid crisis assessment, classifies severity levels, identifies key stakeholders, and generates a structured response plan. The output includes tailored messaging for customers, media, and employees, a detailed communication timeline, channel deployment strategy, and a full recovery roadmap to rebuild trust.

This skill provides comprehensive guidance for developing and executing digital PR strategies. It covers end-to-end processes, including crafting optimized press releases, managing journalist outreach, establishing thought leadership, and building E-E-A-T (Experience, Expertise, Authoritativeness, Trustworthiness) to maximize media coverage and brand credibility. Ideal for media relations and strategic communications planning.

A comprehensive tool designed to audit and ensure the consistency of brand entity data across critical online knowledge sources, including Wikidata, Google Knowledge Panel, and Wikipedia. It systematically checks key properties like founding date, HQ location, and official website against the brand's source of truth profile, flagging discrepancies to protect AI engine trust and maximize digital visibility. Essential for global brand managers and digital marketers.

A comprehensive, scored audit tool for evaluating landing pages across critical conversion dimensions. It analyzes above-fold clarity, trust signals, form friction, message match, mobile UX, and page speed. The output provides actionable, prioritized fixes and detailed recommendations to systematically boost your conversion rate.

A comprehensive guide for authors submitting academic papers, detailing how to proactively provide reproducible code, datasets, and experimental scripts. This ensures that research results are verifiable and trustworthy, significantly enhancing the credibility of findings in high-tier journals despite the absence of formal artifact evaluation badges.

This guide details the best practices for preparing and submitting supplementary materials for CSCW submissions. It clarifies the difference between main papers, appendices, and separate files, emphasizing that anything needed to *trust* the method belongs here. Crucially, it provides deep guidelines on data anonymization, metadata scrubbing, and version control for review rounds.

This guide details best practices for enhancing the credibility of academic manuscripts submitted to scholarly journals, particularly addressing code and data availability. It teaches authors how to proactively provide stable links, checksums, comprehensive reproducibility scripts, and clear licensing statements. The focus is on bolstering trustworthiness for single-blind review processes, even when the journal does not mandate formal artifact badges.

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 for authors submitting storage-related research to top-tier conferences (e.g., USENIX FAST). It dictates how to structure the paper, emphasizing leading with the specific storage cost addressed (e.g., write amplification, tail latency, endurance) and grounding all claims in proportional, real-device evidence rather than generic performance statements. Essential for ensuring technical rigor and reviewer trust.

A comprehensive guide for researchers designing and auditing empirical evaluations for data mining papers (e.g., ICDM). It teaches how to structure a trustworthy evaluation by defining operational tasks, conducting ablation studies to isolate mechanisms, proving scalability, and implementing known-truth checks to ensure findings are valid and not mere artifacts.

This guide details the rigorous process of preparing research artifacts for formal evaluation at major academic conferences like ICSE. It covers achieving ACM badges (Available, Reusable, Reproduced/Replicated) by ensuring proper DOI archiving, open licensing, robust documentation, and verifiable reproducibility for external reviewers, transforming raw research into publishable, trustworthy assets.