Research and draft a response to a GitHub issue or question from an external contributor.
npx skills add https://github.com/NVIDIA/Megatron-LM --skill respond-to-issue
Help a maintainer draft a high-quality response to a GitHub issue from an external contributor.
gh issue view <number> --repo NVIDIA/Megatron-LM --json title,body,comments,labels,state.git log --oneline -20 -- <relevant-files> to see if there have been recent changes that address or relate to the issue.git log -S "<symbol>" --oneline to trace when code was added or removed — this is especially useful for questions about unused/deprecated code or missing features.gh pr list --repo NVIDIA/Megatron-LM --search "<keywords>" --limit 5.Before including specific details in the response, verify them:
git show <hash> --stat).Write a response that:
If the issue identifies something cleanly actionable (dead code to remove, a small bug fix, a missing feature), tell the maintainer and offer to create a branch and PR to address it — don't just draft a comment.
Show the drafted response to the user (the maintainer) for review. Do NOT post it to GitHub automatically. The maintainer will decide whether to post it, edit it, or ask for changes.
Format the draft as a quoted markdown block so it's easy to copy.
gh issue list --repo NVIDIA/Megatron-LM --search "<keywords>" --limit 5.Comprehensive toolkit for creating, analyzing, and visualizing complex networks and graphs in Python. Use when working with network/graph data structures, analyzing relationships between entities, computing graph algorithms (shortest paths, centrality, clustering), detecting communities, generating synthetic networks, or visualizing network topologies. Applicable to social networks, biological networks, transportation systems, citation networks, and any domain involving pairwise relationships.
Interact with Zotero reference management libraries using the pyzotero Python client. Retrieve, create, update, and delete items, collections, tags, and attachments via the Zotero Web API v3. Use this skill when working with Zotero libraries programmatically, managing bibliographic references, exporting citations, searching library contents, uploading PDF attachments, or building research automation workflows that integrate with Zotero.
Comprehensive toolkit for creating, analyzing, and visualizing complex networks and graphs in Python. Use when working with network/graph data structures, analyzing relationships between entities, computing graph algorithms (shortest paths, centrality, clustering), detecting communities, generating synthetic networks, or visualizing network topologies. Applicable to social networks, biological networks, transportation systems, citation networks, and any domain involving pairwise relationships.
Create, analyze, and visualize complex networks and graphs in Python with NetworkX. Use when working with network/graph data structures, computing graph algorithms (shortest paths, centrality, clustering), detecting communities, generating synthetic networks (random, scale-free, small-world), reading/writing graph file formats, or drawing network topologies. Common applications include social, biological, transportation, and citation networks.
Use NeuroKit2 to build or audit reproducible research workflows for physiological time-series preprocessing, event/interval analysis, multimodal alignment, variability, and complexity. Trigger when code imports neurokit2 or needs its current APIs, schemas, and method-aware validation—not for diagnosis or device validation.
Interact with Zotero reference management libraries using the pyzotero Python client. Retrieve, create, update, and delete items, collections, tags, and attachments via the Zotero Web API v3. Use this skill when working with Zotero libraries programmatically, managing bibliographic references, exporting citations, searching library contents, uploading PDF attachments, or building research automation workflows that integrate with Zotero.
Comprehensive toolkit for creating, analyzing, and visualizing complex networks and graphs in Python. Use when working with network/graph data structures, analyzing relationships between entities, computing graph algorithms (shortest paths, centrality, clustering), detecting communities, generating synthetic networks, or visualizing network topologies. Applicable to social networks, biological networks, transportation systems, citation networks, and any domain involving pairwise relationships.
A practical, jargon-free guide to fp-ts functional programming - the 80/20 approach that gets results without the academic overhead. Use when writing TypeScript with fp-ts library.
Take nvidia/respond-to-issue from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.