# Nvidia Sphinx LLM Resolve Issue > Resolve GitHub Issue - Skill: `tomevault-io/nvidia-sphinx-llm-resolve-issue` (Agent Skill, multi-file: 2 files) - Install (CLI): `npx skillmds@latest add tomevault-io/nvidia-sphinx-llm-resolve-issue` - Raw SKILL.md: https://api.skillmd.com/api/skills/tomevault-io/nvidia-sphinx-llm-resolve-issue/raw - Safety review: pending (external: skill-scanner PASS, skillspector PASS) - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: AI & ML - Author: tomevault-io (https://skillmd.com/u/tomevault-io) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/tomevault-io/nvidia-sphinx-llm-resolve-issue --- # Resolve GitHub Issue This skill guides you through resolving a GitHub issue for the sphinx-llm project using a test-driven development workflow. ## Instructions When the user asks you to resolve a GitHub issue, follow these steps: ### 1. Fetch Issue Information First, retrieve the issue details: **If `gh` CLI is available:** ```bash gh issue view --repo NVIDIA/sphinx-llm ``` **If `gh` CLI is not available:** - Use WebFetch to fetch the issue from `https://github.com/NVIDIA/sphinx-llm/issues/` - Extract the title, body, and labels ### 2. Check for `ready` Label **IMPORTANT:** Before proceeding with implementation, check if the issue has the `ready` label. - **If the `ready` label is present:** Proceed with implementation - **If the `ready` label is missing:** Stop and inform the user: > "This issue does not have the `ready` label, which indicates it likely > requires more discussion before implementation. Please ensure the issue has > been discussed and approved by maintainers before proceeding." ### 3. Test-Driven Development Workflow Once you've confirmed the issue is ready for implementation, follow this TDD workflow: #### Step 3a: Write Tests First - Review the existing test structure in `src/sphinx_llm/tests/` - Write new test(s) that verify the desired functionality described in the issue - The tests should **fail initially** (since the feature isn't implemented yet) - Run the tests to confirm they fail as expected: ```bash uv run pytest src/sphinx_llm/tests/ -v ``` #### Step 3b: Implement the Feature - Implement the minimum code necessary to make the tests pass - Follow the project's architecture patterns (see CLAUDE.md) - Run tests frequently to verify progress: ```bash uv run pytest src/sphinx_llm/tests/ -v ``` - Ensure all tests pass before proceeding #### Step 3c: Run Linting and Formatting - Run pre-commit hooks to ensure code quality: ```bash pre-commit run --all-files ``` - Fix any issues identified by the linters ### 4. Update Documentation After the implementation is complete and tests are passing: - Update relevant documentation in `docs/source/` - If the feature adds new configuration options, document them in the appropriate `.rst` files - If the feature adds new directives or extensions, add examples to the docs - Build the docs locally to verify changes: ```bash uv run --dev sphinx-build docs/source docs/build/html ``` ## Example Usage User: "Resolve issue #42" You should: 1. Run `gh issue view 42 --repo NVIDIA/sphinx-llm` (or use WebFetch) 2. Check for the `ready` label 3. If ready, write tests in `src/sphinx_llm/tests/` 4. Implement the feature 5. Update docs in `docs/source/` 6. Run tests and linting ## Notes - Follow the existing code style and patterns - Keep changes focused on the specific issue - Don't over-engineer - implement only what's needed for the issue --- > Converted and distributed by [TomeVault](https://tomevault.io/claim/nvidia) — claim your Tome and manage your conversions.