griffing52/vdicon-core/data-conventions

Claude Code and agent skill by griffing52/vdicon-core

Trust tier 3
  • community
Source check pending

Checking that the SKILL.md file is still in the repository.

Data format conventions for datasets and plmodules

Install

npx vskill@latest install griffing52/vdicon-core/data-conventions

SKILL.md

Data Conventions

Pytree as Data Structure

Data is passed around as pytrees — nested Python containers (lists, tuples, dicts). Every leaf must be a torch.Tensor or np.ndarray with batch size as the leading dimension.

  • Use torch.Tensor for data that will be sent to devices (model inputs, labels).
  • Use np.ndarray for data that won't be sent to devices (e.g., string descriptions use NumPy string arrays).

Dataset Item Format

The outermost pytree is typically a dict with these top-level keys:

  • description — Human-readable explanation of the sample. Highly encouraged for debugging.
  • data — Model input. Never include labels here.
  • label (optional) — Ground truth.
Operator Learning Example
python
{
    "description": np.array(['...'] * batch_size, dtype=np.dtypes.StringDType()),
    "data": {
        "fx": torch.randn(batch_size, f_len, fx_dim),  # operator input function values
        "fy": torch.randn(batch_size, f_len, fy_dim),  # operator input function inputs
        "fm": torch.ones(batch_size, f_len, dtype=torch.bool),  # mask
        "gx": torch.randn(batch_size, g_len, gx_dim),  # operator output query points
        "gm": torch.ones(batch_size, g_len, dtype=torch.bool),
    },
    "label": torch.randn(batch_size, g_len, gy_dim),
}

f = operator input function, g = operator output function, x = function input, y = function output, m = mask. gy is excluded from data since it is the label.

In-Context Operator Learning Example
python
{
    "description": np.array(['...'] * batch_size, dtype=np.dtypes.StringDType()),
    "data": {
        "ex_fx": torch.randn(batch_size, ex_num, f_len, fx_dim),  # example input functions
        "ex_fy": torch.randn(batch_size, ex_num, f_len, fy_dim),
        "ex_fm": torch.ones(batch_size, ex_num, f_len, dtype=torch.bool),
        "ex_gx": torch.randn(batch_size, ex_num, g_len, gx_dim),
        "ex_gy": torch.randn(batch_size, ex_num, g_len, gy_dim),
        "ex_gm": torch.ones(batch_size, ex_num, g_len, dtype=torch.bool),
        "qn_fx": torch.randn(batch_size, qn_num, f_len, fx_dim),  # query input functions
        "qn_fy": torch.randn(batch_size, qn_num, f_len, fy_dim),
        "qn_fm": torch.ones(batch_size, qn_num, f_len, dtype=torch.bool),
        "qn_gx": torch.randn(batch_size, qn_num, g_len, gx_dim),
        "qn_gm": torch.ones(batch_size, qn_num, g_len, dtype=torch.bool),
    },
    "label": torch.randn(batch_size, qn_num, g_len, gy_dim),
}

ex = example, qn = question. qn_gy is excluded from data since it is the label.

validation_step Return Format

validation_step in plmodules should return a pytree. Each leaf should be a torch.Tensor or np.ndarray, keeping the batch dimension (do not pool over batch).

Suggested top-level keys:

  • preds — Model predictions (e.g., images or tensors).
  • errors — Detailed prediction errors (e.g., per-sample error maps).
  • metrics — Scalar-ish evaluation metrics with shape (batch_size, ...). The flattened size of ... should remain small.

The return value is passed directly as outputs to on_validation_batch_end in all callbacks.

Security

  • Security scanPassed, 90 of 100Pattern checks on the skill's files.
  • Model reviewPassed

See the full security report

Quality evaluation

  • Task evaluationNot runNo evaluation yet. Run one in Skill Studio.

See evaluation results

Source

Popularity

7-day trend score
0

Not refreshed yet.

Works with

Any agent vskill installs to: 56 targets in all. These 39 have a page on this site.

  • Claude Code
  • Cursor
  • GitHub Copilot
  • Windsurf
  • Codex
  • Gemini CLI
  • Cline
  • Amp
  • Roo Code
  • Goose
  • Continue
  • Kilo Code
  • Junie
  • Trae
  • OpenHands
  • Qwen Code
  • AI Agent
  • Tabnine Chat
  • Sourcegraph Cody
  • Amazon Q
  • Firebase Genkit
  • Sweep
  • Devin
  • Phind
  • Mentat
  • Claude Desktop
  • Aider
  • GPT Pilot
  • Plandex
  • bolt.new
  • v0
  • Replit Agent
  • Anse
  • TypingMind
  • BetterChatGPT
  • LibreChat
  • Warp AI
  • Pieces
  • Fig AI
Embed badge
vskill VERIFIED badge
[![vskill VERIFIED](https://verified-skill.com/api/v1/skills/griffing52/vdicon-core/data-conventions/badge)](https://verified-skill.com/skills/griffing52/vdicon-core/data-conventions)

Something wrong with this skill or its page? Tell us.