explore-dnn-model
CommunityExplore DNN checkpoints with reproducible runs.
System Documentation
What problem does it solve?
This Skill enables manual invocation and guided exploration of how to run a DNN model checkpoint in the current Python environment. It helps locate weights and upstream source code, resolve dependencies with user confirmation, run reproducible experiments under tmp/, and produce structured reports detailing I/O contracts, timing, and profiling.
Core Features & Use Cases
- Artifact discovery: locate model weights and upstream source code within the workspace and nearby directories.
- Environment reconciliation: detect dependencies, propose installation paths, and use user confirmation to install or configure environments.
- Experiment automation: create a tmp/<experiment-dir>/ workspace, clone upstream code into refs, and run lightweight inference/validation to produce outputs and reports.
Quick Start
Provide a local checkpoint path and optional upstream info to begin exploration; the system will search for the implementation, prepare the environment, and generate experiment reports in tmp/.
Dependency Matrix
Required Modules
None requiredComponents
Standard package💻 Claude Code Installation
Recommended: Let Claude install automatically. Simply copy and paste the text below to Claude Code.
Please help me install this Skill: Name: explore-dnn-model Download link: https://github.com/igamenovoer/magic-context/archive/main.zip#explore-dnn-model Please download this .zip file, extract it, and install it in the .claude/skills/ directory.
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