What does creator-data-audit do?
Batch verify creator identity, platform data, and recent work performance, generating a Codex Skill with date evidence and support for re-read acceptance.
Batch verify creator identity, platform data, and recent work performance, generating a Codex Skill with date evidence and support for re-read acceptance.
This skill is designed for AI agents to batch verify creator identity, platform data, and recent work performance. It generates a comprehensive audit report with date-stamped evidence, making it ideal for tasks like influencer marketing due diligence, content moderation, or competitive analysis. The skill supports the Codex platform and ensures that audit results can be re-read and verified later. It works by systematically fetching and validating creator information, platform analytics, and recent post metrics, then compiling them into a structured, evidence-backed document.
npx skills add bigjun1-art/creator-data-auditInstalled? Explore more Research & Data Analysis skills: affaan-m/uspto-database, affaan-m/scholar-evaluation, affaan-m/literature-review · View all 6 →
Batch verify creator identity, platform data, and recent work performance, generating a Codex Skill with date evidence and support for re-read acceptance.
USPTO patent and trademark data workflow for official record lookup, PatentSearch queries, TSDR checks, assignment data, and reproducible IP research logs.
Structured scholarly-work evaluation for papers, proposals, literature reviews, methods sections, evidence quality, citation support, and research-writing feedback.
Systematic literature-review workflow for academic, biomedical, technical, and scientific topics, including search planning, source screening, synthesis, citation checks, and evidence logging.
The agent harness performance optimization system. Skills, instincts, memory, security, and research-first development for Claude Code, Codex, Opencode, Cursor and beyond.
Agent skill repository discovered by 10x-chat research.
Review backend code for quality, security, maintainability, and best practices based on established checklist rules. Use when the user requests a review, analysis, or improvement of backend files (e.g., `.py`) under the `api/` directory. Do NOT use for frontend files (e.g., `.tsx`, `.ts`, `.js`). Supports pending-change review, code snippets review, and file-focused review.