nvidia-skill-finder
Use for NVIDIA-related requests where an NVIDIA skill might help, even if the user did not ask for a skill. Trigger on NVIDIA products, hardware, software,…
Use for non-failing HoloHub app work with ./holohub: scaffold, build, run, test, visual evidence, lint, and flow benchmarking.
$ npx -y skills add NVIDIA/skills --skill holohub-app-lifecycle --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
/holohub-app-lifecycleContext preview
The summary Claude sees to decide when to auto-load this skill.
Use for non-failing HoloHub app work with ./holohub: scaffold, build, run, test, visual evidence, lint, and flow benchmarking.
name: holohub-app-lifecycle
description: "Use for non-failing HoloHub app work with ./holohub: scaffold, build, run, test, visual evidence, lint, and flow benchmarking."
license: Apache-2.0
metadata:
author: "Holoscan Team <holoscan-team@nvidia.com>"
compatibility: "holoscan-cli>=4.5.0"
github-url: "https://github.com/nvidia-holoscan/holohub"
tags:
- holoscan
- holohub
- application-developmentTake a non-failing application request from checkout selection to reviewable, finite evidence through the public `./holohub` workflow.
Require the task, checkout or starting workspace, and finite acceptance check. Take remaining values from the request or selected checkout; do not guess data rights or sensitive-data constraints. Benchmark details are optional unless performance work is requested.
operator-plus-demo, tutorial, or fix;
data constraints;
Route a concrete failing or wrong `./holohub` command to `holohub-debug-build-run`, reusable Module or DEB/WHEEL work to `holohub-module-lifecycle`, and first-time SDK host installation to `holoscan-setup`. If the matching skill is unavailable, preserve the handoff context and name the skill to install instead of improvising its workflow.
workspace resolution, input handling, scaffolding, metadata, implementation, tests, evidence, and review.
performance work is requested.
The selected checkout's `AGENTS.md`, local `./holohub` help, schemas, and contribution guide are the live technical authority where they do not conflict with user, system, or safety constraints.
At any step, a failing effect-bearing wrapper command ends this happy path; follow Troubleshooting with its exact context. Parse read-only diagnostic results such as `env-check --json` and stop only when a failed capability is required by the selected project's documented needs or the requested proof.
1. **Resolve one safe checkout.** Preserve the starting workspace. Reuse one validated checkout at its current revision. An auto-discovered checkout must be clean. Proceed in a dirty checkout only when the user explicitly selected it and comparing the requested paths with the existing working-tree changes proves they do not overlap. If scope is uncertain, preserve the checkout and request authorization for the documented project-local clone fallback. Never overwrite a workspace or coerce an existing checkout to the contract's evidence snapshot. 2. **Preserve and orient.** Record both roots, provenance, full HEAD, and concise status. Create a task branch before editing a new app only in a clean checkout. In an explicitly selected dirty checkout, switch branches only with user authorization; otherwise request authorization for the fallback. Run wrapper commands from the checkout root and confirm syntax with local help. 3. **Define the proof.** Confirm the contribution type, licensed inputs, input integrity/schema when applicable, and a verdict bounded by an explicit frame/message count, timeout, or artifact completion. Include visual evidence when relevant and state claims the evidence cannot support. 4. **Select strong local examples.** Choose two or three relevant applications for graph/domain, language/build/test, and data/Holoviz/benchmark patterns. Record what will be reused; do not copy an application wholesale. 5. **Scaffold only when needed.** For a new app, preview template setup, inspect its host dependency installation, and obtain explicit user authorization before the real setup. Only after setup succeeds, preview and run a non-interactive, language-explicit `create`. Treat preview as potentially mutating. Obtain any repository-required approval for parent CMake registration; if denied or setup fails, stop before creation. Do not replace an existing app. 6. **Implement the smallest complete path.** Validate metadata, keep automated modes finite, register deterministic tests, exclude generated/data/model artifacts from Git, and emit an observable verdict or artifact. 7. **Preview, act, and verify.** Keep project, mode, language, inputs, and other effect-bearing options identical between each preview and real build, run, and test, while treating the preview itself as potentially mutating. Use the container-first path. Require process success plus the finite verdict, intended tests, and visual or recording inspection when applicable. 8. **Shorten only a proved loop.** Reuse an unchanged image with `--no-docker-build` only after one matching build/run. Use `--no-local-build` only when current artifacts or mounted-source execution are proved sufficient. Rebuild after image or setup changes. 9. **Finish reviewably.** Benchmark only after correctness, then restore normal source/build state. Run focused and wrapper tests, `git diff --check`, and final status. In an explicitly selected dirty checkout, restrict auto-fixing lint to task paths; before a requested commit, validate the exact candidate change with the repository-required full lint in a clean disposable checkout rather than rewriting unrelated work. Do not commit or push unless requested.
If a wrapper command begins failing, stop the happy path and hand off its exact command, revision, dirty state, inputs, and observed result to `hol
Official, NVIDIA-verified Agent Skills for Claude Code, Codex, and other coding agents.
Use for NVIDIA-related requests where an NVIDIA skill might help, even if the user did not ask for a skill. Trigger on NVIDIA products, hardware, software,…
Official NVIDIA-authored guidance for NVIDIA cuDF GPU DataFrames, pandas acceleration, dask-cuDF, ETL, joins, groupby, CSV/Parquet I/O, nullable semantics, and…
Use when asked to install, deploy, run, validate, troubleshoot, or stop NVIDIA AI-Q Blueprint infrastructure.
Use when asked to run deep research or AI-Q research through a reachable NVIDIA AI-Q Blueprint backend.
Calibrate a new dataset from live RTSP camera streams via the AutoMagicCalib REST API. Use when the user provides RTSP URLs or asks to calibrate live cameras;…
Run end-to-end calibration on the shipped sample dataset (sdg_08_2_sample_data_010926.zip) against a running AMC microservice. Use when user says 'test sample…