2K skills ・ Works with: OpenAI · sorted by popularity
Test CodexBar against real provider services, check application settings, and verify menu contents. Use command-line checks and visual evidence to distinguish authentication problems, API mismatches, and display issues before a release.
Retrieve current Chinese-language AI news from AIHOT to summarize daily or weekly developments, follow company and product announcements, and explain trending events. Access is anonymous and read-only, with no API key required.
Guides migration of existing AI inference workloads to self-hosted GKE, Google’s managed Kubernetes service. Covers requirements, GPU sizing, deployment configuration, model storage, validation, and traffic cutover using gcloud and kubectl.
Call Gemini and OpenMaaS models such as Llama, DeepSeek, and Qwen through Google Cloud Agent Platform. Prepare authentication and SDK code, run prompts, configure connection endpoints, and troubleshoot common request errors.
Delegate coding, analysis, refactoring, or reviews from Claude Code to Codex CLI. The skill explains unattended execution, permission settings, output capture, parallel tasks, and independent checks of changed files and test results.
Create blog illustrations, technical diagrams, comparisons, comics, and icons through workflows tailored to each format. The skill guides composition, color, and model selection, then stages generated images for review before they are used in a project.
Import existing notes and agent instructions into LifeOS by splitting content into sections and proposing destinations. Review uncertain classifications, approve selected groups, and retain source information alongside the imported material.
Audit content for AI search by checking clear answers, supporting sources, author and brand information, and crawler access. Receive a readiness report with prioritized improvements and specific suggestions for rewriting passages.
Audit the technical factors that affect how search engines discover and index a website. Check crawl access, URLs, security, mobile pages, loading performance, and JavaScript rendering, then report measured results with prioritized fixes.
Add DeepEval tracing to Python AI applications so execution steps appear in Confident AI. Choose a supported integration or manual @observe instrumentation, then capture inputs, outputs, step types, tags, and metadata.
Answer questions about videos from URLs or local files using timestamped visual and spoken evidence. Choose extracted frames and transcripts, or let Gemini view the full video with a configured API key.
Review content for citation in AI search answers. Use checklists covering clear definitions, sourced data, author information, FAQs, and structured data, then plan how to monitor citations, brand mentions, and AI-referred traffic.
Prepare video clips with yt-dlp, whisper, and ffmpeg, covering downloads, speech transcription, subtitle files, trimming, and vertical formatting. Also includes methods for adding generated speech and sending finished videos through messaging APIs.
Write and review launch prompts for agents working autonomously for hours or days. Define successful deliverables, excluded partial results, stopping rules, worker handoffs, and evidence-based review requirements so completion can be assessed against explicit criteria.
Get critical feedback on research ideas, papers, and experimental results from an external AI reviewer. Repeated discussions help identify unsupported claims, plan missing experiments, and document agreed improvements with priorities and estimated computing costs.
Improve research code, results, and paper drafts through repeated review by a second Codex agent. The workflow implements fixes, checks them again, and records assessments and unresolved issues across up to four rounds.
Run repeated reviews of research work, implement fixes, and request another assessment for up to four rounds by default. The workflow records progress and stops when both the review score and verdict meet its acceptance rules.
Checks a research idea against recent literature, comparing its main claims with the closest published work. Produces a structured report explaining overlap, meaningful differences, risks, and whether to proceed, proceed cautiously, or abandon the idea.
Turn research ideas and literature into a structured funding proposal. Supports KAKENHI and other funding programs, guiding literature positioning, research aims, methods, timelines, and budget justification, with external review to identify weaknesses in the draft.
Improve research through up to four rounds of external language model review, fixes, and reassessment. Use an OpenAI-compatible API to identify weaknesses, prioritize changes, and keep a cumulative record of reviewer responses, actions, and experiment results.
Explore a broad research direction through literature searches, idea generation, novelty checks, small pilot experiments, and critical review. Receive ranked ideas plus a refined proposal and experiment plan for the leading candidate, with evidence recorded across stages.
Choose and configure AI tools for Web3 security work across web applications, APIs, and Solidity contracts. Follow setup examples for reconnaissance, vulnerability validation, and proof-of-concept generation, then combine automated findings with manual review.
Style materials to match the OpenAI and Codex look using specified colors and fonts. Apply distinct formatting to headings, body text, code, and shapes, with fallback fonts when the preferred ones are unavailable.
Diagnose failed model requests from OpenShell sandboxes to hosted APIs or local servers such as Ollama and vLLM. Check provider attachment, access rules, credentials, network layout, and client settings to identify the narrowest configuration change needed.