explore-dnn-model
Manual invocation only; use only when the user explicitly requests `explore-dnn-model` by name. Explore how to run a given DNN model checkpoint in the current Python environment by locating weights + upstream source code, resolving dependencies with user confirmation, running reproducible experiments under `tmp/`, and producing reports about I/O contracts, timing, and profiling.
npx skills add majiayu000/claude-skill-registry --skill explore-dnn-model --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Explore DNN Model ## Minimum Required Inputs (Hard Requirement) To use this skill, the user must provide: - A model checkpoint / model file(s) as a **local** file or directory path (it may be outside the workspace). If the user provides only the checkpoint path (no model name, repo link, or source code), proceed by: 1) Attempting to identify the model name/family from the checkpoint file/dir itself (filenames, adjacent configs/README, embedded metadata, `state_dict` key patterns, etc.). 2) Searching for the implementation in the workspace and/or alongside the checkpoint directory (e.g., nearby Python packages, inference scripts, config files). 3) If still not found, using the best-guess model name/family to search online for the canonical implementation, then cloning the upstream source into `tmp/<experiment-dir>/refs/` for investigation (prefer shallow clone; record URL + commit/tag used). ## Goals This skill has three goals: 1) Verify that the given DNN model can work (inference or training; default focus is **inference**) in the *current* Python environment of the workspace. 2) Determine how to use it (inference or training; default is **inference**) by reading the upstream so
- Minimum Required Inputs (Hard Requirement)
- Goals
- Dependency Policy (Ask Once, Then Apply)
- Version Strategy
- Preferred Options (in order)
- Inputs to Collect (ask if missing)
- Core Workflow
- 0) Confirm artifacts and pick the target environment
- 1) Locate and read the upstream source code/docs
- 2) Derive required dependencies
- 3) Resolve missing dependencies (with user choice)
- 4) Ensure the checkpoint exists locally
- 5) Create an experiment workspace under tmp/
- 6) Collect or synthesize inputs
What does the explore-dnn-model skill do?
Manual invocation only; use only when the user explicitly requests `explore-dnn-model` by name. Explore how to run a given DNN model checkpoint in the current Python environment by locating weights + upstream source code, resolving dependencies with user confirmation, running reproducible experiments under `tmp/`, and producing reports about I/O contracts, timing, and profiling.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill explore-dnn-model --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.