Analytics & Monitoring Agent Skills for Codex CLI

Complete methodology for solving multi-step time series forecasting competitions where lag features cause systematic underprediction at…

Analyzes, describes, compares images, and extracts text using multi-provider vision models across standard AI protocols.

Analyze music/audio files locally without external APIs.

Automate Mx Toolbox tasks via Rube MCP (Composio).

Guidance and local audit tools for Python workflows that directly use GeoPandas GeoSeries, GeoDataFrame, spatial operations, or…

Use when packaging datasets, models, prompts, or annotation materials for a NAACL-bound submission — building the artifact around the…

Use when designing or auditing the experimental program of a NAACL submission — matching evidence to language-coverage claims, keeping…

Use when deciding whether a project should target NAACL rather than ACL, EMNLP, EACL, AACL, LREC-COLING, TACL, or an ML venue — weighing…

Guides experimental study designs, manipulation checks, statistical reporting, and causal mechanisms tailored for Nankai Business Review…

Generate images with Google Gemini 3.1 Flash Image Preview (Nano Banana 2) via inference.sh CLI.

Prepare, audit, or revise Nature-ready Data Availability statements, data repository plans, dataset citations, and FAIR metadata…

Use when targeting Nature Machine Intelligence (Nat Mach Intell) or deciding whether an AI, ML, or robotics manuscript fits this venue.

Use when collecting diagnostic data from a Navan API integration \u2014\

MCP server for the nen ability system. Exposes nanika's internal state — findings, learnings, missions, proposals, ko verdicts, scheduler…

NERV - Rapid LocalSend Test with Voice

Diagnose interface errors, drops, CRCs, duplex mismatches, flapping, speed negotiation issues, and counter trends on routers, switches…

Analyze network latency and optimize request patterns for faster communication.

Use when drafting or triaging NeurIPS OpenReview author responses, rebuttals, and discussion-period replies under the current year's…

Use when stress-testing NeurIPS experimental evidence, including baselines, ablations, data splits, compute, negative results, real-world…

Use when positioning a NeurIPS submission against current-cycle ML literature, contemporaneous work, preprints, neighboring conferences…

Use when strengthening NeurIPS reproducibility evidence, aligning Paper Checklist answers with the paper, writing code/data instructions…