gtm-demo-validation-cache-regression-repair

Diagnose and repair GTM demo validation failures caused by legacy cache files missing intermediate chart data, especially in nested digitalmodel demo scripts using --from-cache.

5 stars

Best use case

gtm-demo-validation-cache-regression-repair is best used when you need a repeatable AI agent workflow instead of a one-off prompt.

Diagnose and repair GTM demo validation failures caused by legacy cache files missing intermediate chart data, especially in nested digitalmodel demo scripts using --from-cache.

Teams using gtm-demo-validation-cache-regression-repair should expect a more consistent output, faster repeated execution, less prompt rewriting.

When to use this skill

  • You want a reusable workflow that can be run more than once with consistent structure.

When not to use this skill

  • You only need a quick one-off answer and do not need a reusable workflow.
  • You cannot install or maintain the underlying files, dependencies, or repository context.

Installation

Claude Code / Cursor / Codex

$curl -o ~/.claude/skills/gtm-demo-validation-cache-regression-repair/SKILL.md --create-dirs "https://raw.githubusercontent.com/vamseeachanta/workspace-hub/main/.agents/skills/workspace_hub_learned/gtm-demo-validation-cache-regression-repair/SKILL.md"

Manual Installation

  1. Download SKILL.md from GitHub
  2. Place it in .claude/skills/gtm-demo-validation-cache-regression-repair/SKILL.md inside your project
  3. Restart your AI agent — it will auto-discover the skill

How gtm-demo-validation-cache-regression-repair Compares

Feature / Agentgtm-demo-validation-cache-regression-repairStandard Approach
Platform SupportNot specifiedLimited / Varies
Context Awareness High Baseline
Installation ComplexityUnknownN/A

Frequently Asked Questions

What does this skill do?

Diagnose and repair GTM demo validation failures caused by legacy cache files missing intermediate chart data, especially in nested digitalmodel demo scripts using --from-cache.

Where can I find the source code?

You can find the source code on GitHub using the link provided at the top of the page.

SKILL.md Source

# GTM demo validation cache regression repair

Use when `digitalmodel/examples/demos/gtm/tests/test_gtm_demos.py` fails on a `--from-cache` smoke test after a demo script was retrofitted to cache more intermediate chart data.

## Trigger pattern

Typical symptom:
- `PYTHONPATH=examples/demos/gtm:src uv run pytest examples/demos/gtm/tests/test_gtm_demos.py -q`
- one failing demo, often Demo 2 wall thickness
- error from cached path like `NameError: PipeDefinition is not defined`

Root cause pattern:
- the script's cached mode assumes newly added intermediate keys exist in old committed JSON
- legacy cache only contains core keys like `metadata/results/summary`
- chart builders fall through into engineering-calculation code paths, which require symbols that cached mode never initialized

## Proven workflow

1. Reproduce in the nested repo, not only the outer workspace-hub repo.
   - `cd /mnt/local-analysis/workspace-hub/digitalmodel`
   - `PYTHONPATH=examples/demos/gtm:src uv run pytest examples/demos/gtm/tests/test_gtm_demos.py -q`

2. Inspect the failing script and the committed cache JSON together.
   - confirm which intermediate keys the script now expects
   - inspect the current results JSON to see whether those keys actually exist

3. Prefer a compatibility fix over forcing cache deletion.
   - add a helper like `_cache_has_intermediate_data(cached)`
   - if `--from-cache` loads a legacy JSON without required intermediate keys, log a clear message and fall back to full recalculation
   - also initialize any constants/imports still needed by downstream chart builders even in cache/regeneration mode

4. Re-run both:
   - full GTM test suite
   - targeted failing subset, e.g. `-k wall_thickness`

5. Clean generated artifact churn before committing.
   - GTM tests can rewrite tracked HTML/JSON outputs
   - revert unrelated regenerated files with `git checkout -- ...`
   - commit only the code fix unless output regeneration is intentionally part of the change

## Minimal repair pattern

In the script:
- define required cache keys
- detect whether loaded JSON has them
- if not, switch from cache mode to full-calc mode
- initialize code-name constants/imported enums for both cache and full modes when chart builders depend on them

## Verification standard

Required:
- `PYTHONPATH=examples/demos/gtm:src uv run pytest examples/demos/gtm/tests/test_gtm_demos.py -q` passes
- targeted regression subset passes
- nested repo `git status` is clean except for intended code changes before commit

## Important notes

- `digitalmodel` is a nested git repo under `workspace-hub`; status/history/commits must be checked there.
- A green GTM pytest suite clears the test-suite blocker for workspace-hub issue tracking, but does not by itself satisfy higher-level GTM approval gates like browser validation or hand checks.

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