kaggle

Generate Kaggle competition notebook script, Jupytext # %% format.

Follows user's ML research style distilled from past notebooks:

• PTL always for DNN training (PyTorch Lightning + torchmetrics) — even simple baselines
• Tool agnostic — best-fit library for problem; PTL when training loop needed
• Stages with lenses — each major stage: quick sanity check cell (show one batch, print shapes, verify submission format)
• # ! bash over subprocess — package installs, nvidia-smi, ls -lh, # ! head submission.csv
• EDA is visual — distribution plots, sample grids, dimension scatters before any model
• Inference included — model save pattern + separate load-and-infer cells
• CSVLogger + seaborn — metrics plotted from metrics.csv after every training run

NOT for writing Python packages, modules, production code — notebook scripts only. NOT research literature survey — use /research:topic for SOTA literature search.

• $ARGUMENTS: one of:
  • <competition-name> — short slug for output filename; generates blank template
  • <competition-name> <url> — fetches competition overview from URL before generating
  • <competition-name> "<description>" — inline description of problem and data
  • --type <type> — hint: classification, regression, segmentation, detection, tabular (auto-detected when omitted)
  • --eda-only — generate only EDA sections (no model/training/submission); always online (no offline setup)
  • --inference-only — generate inference notebook from checkpoint (no EDA, no training); always offline (frozen packages pattern); loads checkpoint from PATH_CHECKPOINT constant; output suffix -inference.py
  • --offline-setup — include offline package setup (frozen_packages pattern) in setup cell; auto-applied when --inference-only; ignored when --eda-only (EDA always online)
  • --resume <path> — read existing .py script, extend/improve it

Output: .experiments/kaggle/<competition-name>.py

OUTPUT_DIR:    .experiments/kaggle/
CELL_MARK:     "# %%"
MD_CELL_MARK:  "# %% [markdown]"
# NOTE: documentation-only — not referenced as shell vars across separate Bash() calls (state doesn't persist); keep values in sync with literal use sites (Steps 1, 3, 4).

Key boundary: end of Step 3 — notebook script generated by foundry:sw-engineer, written to OUTFILE. Preserve: OUTFILE path (derived from TMPDIR keys), COMPETITION_NAME (TMPDIR key), mode flags (EDA_ONLY, INFERENCE_ONLY, OFFLINE_SETUP). Clear at Step 1 start (stale prior run) and after Step 4 package-distillation gate resolves.

Task hygiene: call TaskList first; close orphaned tasks. Create tasks per phase.

Step 1: Parse arguments and gather context

# loads: compaction-contract.md
ARGS="$ARGUMENTS"
COMPETITION_NAME=$(echo "$ARGS" | awk '{print $1}')
RESUME_FLAG=""
EDA_ONLY=false
INFERENCE_ONLY=false
OFFLINE_SETUP=false
PROBLEM_TYPE=""

[[ "$ARGS" == *"--eda-only"* ]]      && EDA_ONLY=true
[[ "$ARGS" == *"--inference-only"* ]] && INFERENCE_ONLY=true
[[ "$ARGS" == *"--offline-setup"* ]]  && OFFLINE_SETUP=true
[[ "$ARGS" =~ --type[[:space:]]([a-z]+) ]] && PROBLEM_TYPE="${BASH_REMATCH[1]}"
[[ "$ARGS" =~ --resume[[:space:]]([^[:space:]]+) ]] && RESUME_FLAG="${BASH_REMATCH[1]}"

# inference always offline; EDA always online (overrides --offline-setup)
[ "$INFERENCE_ONLY" = "true" ] && OFFLINE_SETUP=true
[ "$EDA_ONLY" = "true" ]       && OFFLINE_SETUP=false

echo "Competition: $COMPETITION_NAME"
echo "Type: ${PROBLEM_TYPE:-auto-detect}"
echo "EDA only: $EDA_ONLY | Inference only: $INFERENCE_ONLY | Offline setup: $OFFLINE_SETUP"

# Persist for Steps 3+4 (bash state lost across Bash() calls)
echo "$COMPETITION_NAME" > "${TMPDIR:-/tmp}/kaggle-competition-name"
echo "$EDA_ONLY"         > "${TMPDIR:-/tmp}/kaggle-eda-only"
echo "$INFERENCE_ONLY"   > "${TMPDIR:-/tmp}/kaggle-inference-only"
echo "$OFFLINE_SETUP"    > "${TMPDIR:-/tmp}/kaggle-offline-setup"

mkdir -p .experiments/kaggle/  # timeout: 3000
KEEP_ITEMS=""
if [[ "$ARGS" =~ --keep[[:space:]]\"([^\"]+)\" ]]; then
    KEEP_ITEMS="${BASH_REMATCH[1]}"
fi
# Clear stale contract from any prior incomplete run (compaction-contract.md §Lifecycle)
rm -f .claude/state/skill-contract.md  # timeout: 5000
echo "${KEEP_ITEMS:-}" > "${TMPDIR:-/tmp}/kaggle-keep-items"  # persist for Step 3 contract write

Flag mutual-exclusion check — if EDA_ONLY and INFERENCE_ONLY are both true (both --eda-only and --inference-only passed): print ! Conflicting flags: `--eda-only` and `--inference-only` are mutually exclusive (`--eda-only` is always-online with no training; `--inference-only` is always-offline/frozen-package with no EDA — see `foundation.md`). Pick one. then invoke AskUserQuestion — (a) Abort · (b) Continue ignoring both (falls back to full mode: neither eda-only nor inference-only applied). On Abort: stop.

Unsupported flag check — scan $ARGUMENTS for remaining --<token> tokens after supported flags extracted (--eda-only, --inference-only, --offline-setup, --type, --resume, --keep). Found: print ! Unknown flag(s): `--<token>`. Supported: `--eda-only`, `--inference-only`, `--offline-setup`, `--type <type>`, `--resume <path>`, `--keep "<items>"`. then invoke AskUserQuestion — (a) Abort · (b) Continue ignoring. On Abort: stop.

Context collection — run in parallel:

1. URL provided in args: WebFetch competition page; extract problem description, target metric, data format, evaluation — read and quote actual text, never paraphrase from training knowledge
2. --resume: read existing script (Read tool)
3. Scan .experiments/kaggle/ (Glob pattern *.py) for prior scripts; read first 30 lines of each — find similar past competitions, use as structural reference
4. Check resources/competitors/ for .ipynb/.py files — found: read each, summarise approach (model choice, preprocessing, feature engineering, augmentation). Use findings to inform detection method and domain-specific preprocessing decisions in Step 2.

Grounding protocol — mandatory before Step 2:

Build fact table. Each fact needs source: [fetched], [user], [past-notebook:<file>], or [inferred-from:<fact>]. Never mark fact [inferred] without citing prior fact it derives from.

Fact	Value	Source
problem_type	?	?
input_modality	?	?
output_format	?	?
eval_metric	?	?
data schema (CSV columns / image format)	?	?
submission format	?	?

Gaps — ask before generating:

After building fact table, count facts still marked ? or [inferred] without prior grounded fact. Any of these unknown:

• input_modality — cannot generate Dataset class
• eval_metric — cannot choose torchmetric
• submission format — cannot generate Submission section

Invoke AskUserQuestion with up to 4 questions covering all unknown required facts. Never guess or hallucinate competition-specific details (column names, file paths, data schema). State "unknown — will use placeholder" if user skips.

Acknowledge past-notebook similarity explicitly: "Found similar past notebook: <file> — reusing <pattern> from it."

Step 2: Determine problem profile

From gathered context, determine:

Property	Value
problem_type	classification / regression / segmentation / detection / tabular
input_modality	image-2d / image-3d / tabular / time-series / point-cloud / mixed
output_format	label / scalar / mask / bboxes / rle
eval_metric	AUC / F1 / RMSE / Dice / IoU / mAP / ...
recommended_model	see §Model selection below
use_ptl	true if DNN training; false for pure XGBoost/sklearn pipelines

Model selection rules (best-fit, not default):

• Image classification → timm.create_model (EfficientNetV2, ConvNeXt, ViT-B) + PTL
• Image regression → timm.create_model backbone (num_classes=0) + PTL regression head
• Image segmentation → segmentation_models_pytorch (UNet/UNet++) + PTL; MONAI for 3D
• Object detection → torchvision.models.detection or ultralytics YOLO + PTL wrapper if needed
• Tabular → xgboost.XGBClassifier/Regressor with sklearn Pipeline; PTL only if DNN features needed
• Point cloud → MONAI or pytorch3d; PTL always
• Time series → torch.nn.LSTM or tsfresh features + XGBoost; PTL when DNN

PTL rule: use PTL whenever training loop needed — even simple single-layer models. Exception: pure sklearn/XGBoost pipelines, no neural network component.

Step 3: Generate notebook script

Foundry availability check — verify before spawning:

FOUNDRY_AVAILABLE=$(ls -td ~/.claude/plugins/cache/borda-ai-rig/foundry/*/agents/sw-engineer.md 2>/dev/null | head -1)  # timeout: 5000
[ -z "$FOUNDRY_AVAILABLE" ] && { printf "⚠ foundry plugin not available — kaggle notebook generation requires foundry:sw-engineer\nInstall: claude plugin install foundry@borda-ai-rig\n"; exit 1; }

Spawn prompt assembled from the inline problem profile below plus exactly one resolved composition row:

# Re-hydrate flags persisted in Step 1 (bash state lost between Bash calls)
COMPETITION_NAME=$(cat "${TMPDIR:-/tmp}/kaggle-competition-name" 2>/dev/null || echo "$COMPETITION_NAME")
EDA_ONLY=$(cat "${TMPDIR:-/tmp}/kaggle-eda-only" 2>/dev/null || echo "false")
INFERENCE_ONLY=$(cat "${TMPDIR:-/tmp}/kaggle-inference-only" 2>/dev/null || echo "false")
_KAGGLE_MODES="${CLAUDE_PLUGIN_ROOT:-plugins/research}/skills/kaggle/modes"
COMPOSITION_FILE="$_KAGGLE_MODES/composition.md"
MODE="full"
[ "$EDA_ONLY" = "true" ] && MODE="eda-only"
[ "$INFERENCE_ONLY" = "true" ] && MODE="inference-only"

# Derive output filename from mode — must match the composition contract before spawning
OUTPUT_SUFFIX=""
[ "$INFERENCE_ONLY" = "true" ] && OUTPUT_SUFFIX="-inference"
OUTFILE=".experiments/kaggle/${COMPETITION_NAME}${OUTPUT_SUFFIX}.py"
echo "$MODE" > "${TMPDIR:-/tmp}/kaggle-mode"
echo "Mode: $MODE · Output: $OUTFILE"

Read $COMPOSITION_FILE and select the exact $MODE row. Then read each named contract once, from left to right, and read style-rules.md once. Read modality-dispatch.md only when a selected section requests a modality branch. Do not load unselected section contracts. Pass the selected row and resolved contract contents to foundry:sw-engineer after the problem profile block below.

Spawn foundry:sw-engineer with this prompt preamble (inline, then continue with the resolved composition contracts):

Write a complete Kaggle competition notebook script to `<OUTFILE>` (substitute expanded path from bash block above).

Format: Jupytext `# %%` Python script — every cell separated by `# %%` (code) or `# %% [markdown]` (markdown).

## Problem profile
- Competition: <competition-name>
- Problem type: <problem_type>
- Input: <input_modality>
- Output: <output_format>
- Metric: <eval_metric>
- Model: <recommended_model>
- Use PTL: <use_ptl>
- Description: <competition description if available>

[Continue with the selected row from composition.md, followed by the resolved section contracts in order, style-rules.md, and the selected modality branch when applicable.]

## Completion

Write `<OUTFILE>`. Return only:

{"status":"done","file":"<OUTFILE>","lines":N,"sections":N,"problem_type":"<type>","mode":"<MODE>","confidence":0.N}

Synchronous spawn note: foundry:sw-engineer spawned synchronously (not run_in_background=true), so CLAUDE.md §6 poll-based monitoring unreachable mid-call. After Agent() returns, check agent's output under .experiments/kaggle/; missing or empty → treat as timed out, surface with ⏱ marker — never silently omit.

# Compaction contract — boundary: after Step 3 notebook generated (compaction-contract.md §Lifecycle)
_COMPETITION=$(cat "${TMPDIR:-/tmp}/kaggle-competition-name" 2>/dev/null || echo "")
_INF=$(cat "${TMPDIR:-/tmp}/kaggle-inference-only" 2>/dev/null || echo "false")
_KEEP=$(cat "${TMPDIR:-/tmp}/kaggle-keep-items" 2>/dev/null || echo "")
_SUFFIX=""; [ "$_INF" = "true" ] && _SUFFIX="-inference"
_OUTFILE=".experiments/kaggle/${_COMPETITION}${_SUFFIX}.py"
_KEEP_APPEND=""; [ -n "$_KEEP" ] && _KEEP_APPEND="; user-keep: $_KEEP"
mkdir -p .claude/state  # timeout: 5000
{
    echo "## Active Skill Contract"
    echo "- skill: research:kaggle · phase: verify (after Step 3 notebook generated)"
    echo "- run-dir: .experiments/kaggle"
    echo "- preserve: outfile=${_OUTFILE}, competition=${_COMPETITION}${_KEEP_APPEND}"
    echo "- next: Step 4 verify structure, follow-up gate, package distillation"
} > .claude/state/skill-contract.md  # timeout: 5000

Step 4: Verify and report

After agent completes:

1. Read first 30 lines of generated file to verify # %% structure
2. Count cell markers: grep -c "^# %%" .experiments/kaggle/<name>.py
3. Resolve the current row from composition.md; verify every listed section is present and no unlisted section was generated

# Re-derive OUTFILE from flags persisted in Step 1 (bash state lost between steps)
COMPETITION_NAME=$(cat "${TMPDIR:-/tmp}/kaggle-competition-name" 2>/dev/null || echo "$COMPETITION_NAME")
INFERENCE_ONLY=$(cat "${TMPDIR:-/tmp}/kaggle-inference-only" 2>/dev/null || echo "false")
MODE=$(cat "${TMPDIR:-/tmp}/kaggle-mode" 2>/dev/null || echo "full")
OUTPUT_SUFFIX=""; [ "$INFERENCE_ONLY" = "true" ] && OUTPUT_SUFFIX="-inference"
OUTFILE=".experiments/kaggle/${COMPETITION_NAME}${OUTPUT_SUFFIX}.py"
echo "=== Composition ==="; echo "$MODE"
echo "=== Cell count ==="; grep -c "^# %%" "$OUTFILE"  # timeout: 5000
echo "=== Sections ===";   grep "^# %% \[markdown\]" "$OUTFILE"  # timeout: 5000
echo "=== File size ===";  wc -l "$OUTFILE"  # timeout: 5000

Print to terminal:

• Output path ($OUTFILE)
• Mode + resolved composition contracts
• Problem type + recommended model
• Cell count and section list
• Missing required sections flagged with ⚠

Invoke AskUserQuestion as follow-up gate:

• (a) Open in editor — ! code $OUTFILE
• (b) Extend with additional sections
• (c) Regenerate with different model/approach
• (d) Done

On (a): run ! code "$OUTFILE" via Bash. On (b): re-enter Step 3 with extension directive. On (c): re-enter Step 2 with user-specified changes.

Package distillation gate — invoke after follow-up gate resolves to Done:

Benefits to state before asking: shared helpers tested once, used everywhere; wheel attachment on Kaggle faster than re-inlining; subsequent notebooks shorter; package tests catch regressions before submission.

Invoke AskUserQuestion:

• (a) Yes — scaffold src/<package>/ with extracted helpers + tests
• (b) Skip — keep everything inlined for now

If (a):

1. Identify every function in notebook with no hardcoded paths, no plt.show(), no tqdm calls
2. Write each to src/<package>/<module>.py with full Google-style docstring + Example: block — all standard coding patterns apply (doctests for pure functions, Args:/Returns: sections, full if __name__ == "__main__": guards where appropriate); these are package modules, not notebook cells
3. Create tests/test_<module>.py covering each function
4. Create notebooks/01_<competition-name>_pkg.py — inline definitions replaced by package imports; never modify validated baseline $OUTFILE

If (b): skip; repeat this gate offer after next notebook written.

rm -f .claude/state/skill-contract.md  # clear contract — kaggle notebook complete (compaction-contract.md §Lifecycle)  # timeout: 5000