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Agent skill

Model Summary Usage

by VectorSpaceLab in VectorSpaceLab/AREX-Skill

Use torchsummary.summary and torchsummary.summarystring to inspect PyTorch nn.Module shapes, parameter counts, devices, dtypes, and memory estimates.

MITAuto-check passedAI & LLM Engineering

Install Model Summary Usage

skills CLI
$ npx skills add VectorSpaceLab/AREX-Skill --skill model-summary-usage -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install VectorSpaceLab/AREX-Skill model-summary-usage --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/VectorSpaceLab/AREX-Skill.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/repositories/repo-skills/pytorch-summary/sub-skills/model-summary-usage .claude/skills/model-summary-usage && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
model-summary-usage
GitHub stars
328
Token cost
~847 tokens
SKILL.md length
308 words
Files
5 (incl. scripts, references)
Skills in repo
157
Repo updated
First seen
Licence
MIT

At a glance

Use torchsummary.summary and torchsummary.summarystring to inspect PyTorch nn.Module shapes, parameter counts, devices, dtypes, and memory estimates.

  • Works in 4 steps: Confirm the runtime has torchsummary,… → Import only the public API → For CPU-safe usage, pass the device… → …
  • Tasks that involve Deep learning
  • SKILL.md covers Read this when, Do not use this for, Start here and References, plus 1 more section
  • Runs Python scripts from its folder; calls python

What it does

Model Summary Usage is an agent skill from VectorSpaceLab/AREX-Skill. Use torchsummary.summary and torchsummary.summarystring to inspect PyTorch nn.Module shapes, parameter counts, devices, dtypes, and memory estimates.

Its SKILL.md is about 850 tokens, which your agent loads only when the skill is triggered. The skill folder holds 6 other files, including scripts and reference files (for example `references/api-reference.md`, `references/troubleshooting.md` and `references/workflows.md`).

It sits in AI & LLM Engineering, covering Deep learning. It works with PyTorch. The repository describes itself as: A Skill Library for Automated Machine Learning. The licence is MIT.

When your agent uses it

  • Tasks that involve Deep learning

Example prompts

  • “/model-summary-usage”

Requirements

  • Python 3

Workflow steps

4 steps, taken from the first numbered list in SKILL.md.

  1. Confirm the runtime has torchsummary, torch, and numpy available.
  2. Import only the public API
  3. For CPU-safe usage, pass the device explicitly and move the model yourself
  4. Use the bundled smoke helper from this sub-skill directory when you need a

What it can do on your machine

Read from SKILL.md and the folder at commit ac3fe1a. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships 1 file in scripts/ (Python), which the agent can run.

    Shell commands in SKILL.md call:

    • python

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Model Summary Usage loads about 847 tokens when it runs, and up to ~6.3k if it reads all its reference files. Until then it costs about 43 tokens; SKILL.md has 308 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~43
When it runs · the whole SKILL.md, loaded when a task matches
~847
With references · SKILL.md plus every file in references/, read only if the agent opens them
~6.3k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.

SKILL.md

The full file from VectorSpaceLab/AREX-Skill at commit ac3fe1a, republished under its MIT licence (© VectorSpaceLab). 308 words, ~847 tokens.

Download SKILL.mdSave it as .claude/skills/model-summary-usage/SKILL.md (or your agent's skills folder). This skill also uses 4 other files; get the full folder from GitHub.
name
model-summary-usage
description
Use torchsummary.summary and torchsummary.summary_string to inspect PyTorch nn.Module shapes, parameter counts, devices, dtypes, and memory estimates.
disable-model-invocation
true
metadata.disco-role
operating
license
MIT

model-summary-usage

Use this sub-skill when a task needs to call torchsummary.summary or torchsummary.summary_string for a PyTorch nn.Module without reopening the source repository.

Read this when

  • You need a Keras-style printed summary table for a PyTorch model.
  • You need programmatic total/trainable parameter counts from summary_string.
  • The model has one input, multiple inputs, or per-input dtype requirements.
  • A summary run is failing because of CPU/CUDA placement, dtype, input-size, or shape issues.
  • You need to decide whether this lightweight package is enough or whether to use torchinfo for a newer or more advanced model-inspection task.

Do not use this for

  • Editing package source, changing tests, packaging metadata, or release work; route those tasks to repo-maintenance.
  • General PyTorch debugging unrelated to this package's summary calls.
  • Precise memory profiling or complex input/output model introspection; prefer torchinfo or a PyTorch profiler workflow for those cases.

Start here

  1. Confirm the runtime has torchsummary, torch, and numpy available.

  2. Import only the public API:

    python
    from torchsummary import summary, summary_string

  3. For CPU-safe usage, pass the device explicitly and move the model yourself:

    python
    import torch

device = torch.device("cpu")
model = model.to(device)
summary(model, input_size=(channels, height, width), device=device)

  4. Use the bundled smoke helper from this sub-skill directory when you need a quick verification of the installed package:

    bash
    python scripts/smoke_summary.py --help
python scripts/smoke_summary.py --case all --device cpu

    From the root generated skill directory, use:

    bash
    python sub-skills/model-summary-usage/scripts/smoke_summary.py --case all --device cpu

References

  • API reference: exact signatures, return values, input_size, batch_size, device, dtype, hook, and memory-estimate semantics.
  • Workflows: single-input, multiple-input, dtype, device, summary_string, and output-interpretation recipes.
  • Troubleshooting: workflow-specific failures and fixes.
  • Root shared troubleshooting: package install/import and cross-cutting backend issues shared with other sub-skills.

Key facts to preserve

  • Distribution/package name: torchsummary; version evidenced for this skill: 1.5.1.
  • Public exports: summary and summary_string from torchsummary.
  • summary(...) prints the formatted table and returns the parameter-info tuple produced by summary_string(...).
  • summary_string(...) returns (summary_str, (total_params, trainable_params)).
  • input_size excludes the batch dimension. A tuple means one input; a list of tuples means multiple inputs.
  • The default device is CUDA (cuda:0), so CPU-only calls should pass device="cpu" or torch.device("cpu").

© VectorSpaceLab, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 4 other files (scripts, references) in skills/repositories/repo-skills/pytorch-summary/sub-skills/model-summary-usage of VectorSpaceLab/AREX-Skill.

  • SKILL.md
  • references/api-reference.md
  • references/troubleshooting.md
  • references/workflows.md
  • scripts/smoke_summary.py

Open the folder on GitHubat commit ac3fe1a

Compare with similar skills

Model Summary Usage next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Model Summary Usage compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Model Summary Usage this skillVectorSpaceLab/AREX-Skill328—~847Automated safety check: PassMIT
Add Uint Supportpytorch/pytorch104k2 repos~2.3kAutomated safety check: PassCustom licence
CLIP Image-Text MatchingOrchestra-Research/AI-Research-SKILLs13k8 repos~1.7kAutomated safety check: PassMIT
Add Torch Shapes Examplefacebook/pyrefly7.1k—~1.3kAutomated safety check: PassMIT
Interview Cheatsheetwanshuiyin/ARIS-in-AI-Offer5801 repos~3.4kAutomated safety check: NotesMIT
Ghstack CIpytorch/pytorch104k—~1.4kAutomated safety check: PassCustom licence

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Works with

Categories

Questions about Model Summary Usage

What does Model Summary Usage do?

Use torchsummary.summary and torchsummary.summarystring to inspect PyTorch nn.Module shapes, parameter counts, devices, dtypes, and memory estimates. Model Summary Usage is an agent skill from VectorSpaceLab/AREX-Skill.Module shapes, parameter counts, devices, dtypes, and memory estimates.

When should I use Model Summary Usage?

Model Summary Usage fits situations like: tasks that involve Deep learning.

How do I install Model Summary Usage in Claude Code?

Run `npx skills add VectorSpaceLab/AREX-Skill --skill model-summary-usage -a claude-code`. Or copy the skill folder (skills/repositories/repo-skills/pytorch-summary/sub-skills/model-summary-usage in VectorSpaceLab/AREX-Skill) into .claude/skills/model-summary-usage in your project. Claude Code loads it when a task matches its description.

How do I install Model Summary Usage in Codex?

Run `npx skills add VectorSpaceLab/AREX-Skill --skill model-summary-usage -a codex`. Or copy the skill folder (skills/repositories/repo-skills/pytorch-summary/sub-skills/model-summary-usage in VectorSpaceLab/AREX-Skill) into .agents/skills/model-summary-usage in your project. Codex loads it when a task matches its description.

Can I use Model Summary Usage in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add VectorSpaceLab/AREX-Skill --skill model-summary-usage -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/model-summary-usage, .gemini/skills/model-summary-usage, .github/skills/model-summary-usage and .opencode/skills/model-summary-usage in your project.

What does Model Summary Usage need to run?

Going by SKILL.md and its folder, Model Summary Usage needs Python for the scripts in its folder and the command-line tools its instructions call (python). Our summary lists: Python 3.

Does Model Summary Usage access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Model Summary Usage safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Model Summary Usage use?

Model Summary Usage is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Model Summary Usage use?

About 847 tokens (SKILL.md is roughly 3.4k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 5.5k tokens, read only when the agent opens those files.

What are the alternatives to Model Summary Usage?

Skills that share tags, products or a category with Model Summary Usage: Add Uint Support (pytorch/pytorch, 104k stars), CLIP Image-Text Matching (Orchestra-Research/AI-Research-SKILLs, 13k stars), Add Torch Shapes Example (facebook/pyrefly, 7.1k stars) and Interview Cheatsheet (wanshuiyin/ARIS-in-AI-Offer, 580 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Model Summary Usage?

VectorSpaceLab (a GitHub organization) maintains it in VectorSpaceLab/AREX-Skill, which has 328 GitHub stars. The repository holds 157 skills in this directory. The repository was last updated on September 3, 2026.

Source: VectorSpaceLab/AREX-Skill on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.