Skill UI

A powerful rewriting editor designed to remove telltale signs of AI-generated text, making writing sound genuinely natural and human. It detects and corrects common AI patterns, such as inflated symbolism, vague attributions, repetitive structures, and overuse of passive voice. Furthermore, it can analyze and match a user's specific writing voice and inject personality and rhythm when required, making it ideal for professional editing, academic review, and personal essay writing.

This skill outlines the principle of mandatory, evidence-based verification before declaring any work (such as a feature, bug fix, or task) complete or successful. It mandates that assertions of success must be backed by repeatable evidence, such as passing test outputs, clean build logs, or successful execution reports. It is critical for PR reviews, task closure, and maintaining high engineering rigor.

This technique is designed for advanced academic research, enabling users to search for theses by running two parallel tracks: a Topic Track (focusing on discipline, methodology, and materials) and a People Track (focusing on university, advisor, degree type, and year). By switching between these two methodologies, users can cross-validate results and significantly improve the accuracy and completeness of their literature review.

A comprehensive mentor for graduate students and early-career researchers, guiding the entire research lifecycle. This skill covers formulating rigorous research questions, designing studies (quantitative, qualitative, mixed-methods), conducting literature reviews, analyzing data, and writing publishable papers. It emphasizes methodological rigor, critical thinking, and ethical considerations.

A structured, rigorous framework for conducting peer reviews of scientific manuscripts, grant proposals, and research papers. This skill systematically assesses methodology, statistical validity, experimental design rigor, and adherence to reporting standards (e.g., CONSORT, STROBE). It guides users through comprehensive evaluations of abstract, introduction, methods, results, and discussion sections, providing actionable, constructive feedback to enhance scientific quality and reproducibility.

This skill provides a systematic framework for evaluating the rigor and reliability of scientific claims. It guides users through assessing experimental design validity, identifying various forms of bias (selection, cognitive, measurement), and applying established evidence grading frameworks like GRADE or Cochrane Risk of Bias. Essential for academic peer review, systematic reviews, and critical analysis.

A comprehensive mentor guiding users through the entire lifecycle of academic literature reviews. It teaches systematic search strategies (PICO/PCC), critical appraisal methods, and advanced synthesis techniques. The skill covers searching major English and Chinese databases (e.g., PubMed, Web of Science, CNKI), organizing findings, and effectively utilizing AI research tools like Elicit and Semantic Scholar to build robust, evidence-based research papers.

A multi-phase workflow designed to guide users through complex problem analysis and solution design. Instead of providing immediate answers, it systematically enforces deep thinking by reviewing the problem's validity, assessing information gaps, performing root cause analysis, and iterating through solution vetting (self-check and final review). Ideal for complex technical troubleshooting, decision-making, or theoretical discussions requiring rigorous methodology.

This skill automates the complex process of resubmitting an already polished academic paper to a different conference or journal. It is designed for scenarios where the core content, experiments, and bibliography must remain untouched, but the paper needs micro-edits to accommodate new venue constraints, page limits, or prior reviewer feedback. It ensures the new submission is generated as a separate, sibling directory to maintain the integrity of prior submissions.

This skill serves as the central entry point for comprehensive API security testing. It guides testers to systematically categorize and prioritize potential vulnerabilities by focusing on core dimensions such as documentation review (Recon), object-level authorization (BOLA), token integrity (JWT), and complex data structures (GraphQL/Hidden Params). Use this as the initial step when assessing a new REST or GraphQL endpoint.

This meta-skill orchestrates complex, multi-step job search workflows, moving beyond simple advice. It is designed for concrete tasks such as tailoring resumes to specific job descriptions (JDs), building complete application packs, preparing for named interviews, comparing multiple job roles, or maintaining an organized application tracker. It focuses on producing reviewable artifacts and never performs automatic applications.