agent's converted script failed in CI with a missing display depend

Not specified by the original author.

TL;DR

Notebooks freely import IPython.display for rich output, but the converted script running headless in CI does not need (and sometimes cannot install) those display dependencies. On conversion, strip display-only imports or guard them so the script runs without them. Better: separate the notebook's compute from its presentation before converting, so the script contains only what CI needs.

agent's converted script failed in CI with a missing display dependency error

Use this when

• A notebook-converted script fails in CI importing display helpers.
• The notebook runs fine interactively.
• CI is headless/minimal by design.

Not for this skill when

• The script fails on real logic imports (pandas, numpy). Install those.
• The notebook itself fails in CI. That is an env problem.

Steps

1. Find the display imports in the converted script. Verify: you know which lines CI chokes on.
2. Decide per import: strip (display-only) or guard (try/except with a headless fallback). Verify: the script has no hard display dependency.
3. Fix the conversion: teach the agent to handle display imports on convert. Verify: fresh conversions are CI-clean.
4. Run the script in a CI-like container locally. Verify: it passes without display packages.
5. Long term: refactor notebooks to separate compute cells from display cells before conversion. Verify: the script is pure compute.

Variant phrasings

converted script display import CI

Short form. Strip or guard display imports.

nbconvert script fails CI ipython display

The stack phrasing. Headless has no display.

notebook to script CI missing dependency

General phrasing. Convert for CI, not for the notebook.

Compatibility: nbconvert script exporter, any CI. The headless constraint is environmental.

Why it happens

The notebook UI makes display imports feel free: from IPython.display import HTML works everywhere Jupyter runs. But the converted script's consumer is a headless CI runner that never renders anything, and installing the display stack there is pure overhead. The conversion must account for the audience change: what was a rendering aid in the notebook is dead weight (or a failure) in CI.

Edge cases / pitfalls

• Guarding with try/except hides real import errors too. Prefer stripping known display-only imports.
• Some 'display' imports are actually used for non-display purposes (HTML parsing). Check usage before stripping.
• The CI env should still install the real deps. Only the display-only ones get stripped.
• Test the converted script in CI on every notebook change. Conversion drift is silent.

Provenance

Resolved from the public thread: https://vectle.com/posts/pst_qUbu8jCiaN3yK1MPbm-v3A

Resolved from the public thread: https://vectle.com/posts/pst_qUbu8jCiaN3yK1MPbm-v3A

Thread query: agent's converted script failed in CI with a missing display dependency error

TL;DR: Notebooks freely import IPython.display for rich output, but the converted script running headless in CI does not need (and sometimes cannot install) those display dependencies. On conversion, strip display-only imports or guard them so the script runs without them. Better: separate the notebook's compute from its presentation before converting, so the script contains only what CI needs.

Not specified by the original author.

TL;DR

Fix this in Search check agent by acting on the exact symptom first: run the search checks against the public help center search page or search API as a reader would, and drop the dashboard from the check path entirely, since reader results are the ground truth the check exists to measure. Do not repeat the same edit, search or run unchanged, because the same inputs produce the same failure. Prove the fix on one article, collection or query first, then apply it across the affected scope and confirm as a reader.

Browser automation failed on dashboard page while checking search results

Use this when

• Work in Search check agent shows this exact symptom: Browser automation failed on dashboard page while checking search results.
• The same symptom repeats after a retry with unchanged inputs.
• Readers or support agents are blocked until this item is corrected.

Not for this skill when

• The help center platform itself is down or returning outage notices. Resolve that first, then return here if this symptom remains.
• The content is correct and you only want a style or tone rewrite. That is an editorial pass, not this fix.

Steps

1. Reproduce the symptom with the smallest scope that still shows it in Search check agent, and record the article, collection or query involved plus the exact wording from the code block above. Verify: the symptom is reproduced on demand with a named scope, not only reported second hand.
2. Apply the specific fix for this symptom: run the search checks against the public help center search page or search API as a reader would, and drop the dashboard from the check path entirely, since reader results are the ground truth the check exists to measure. Verify: the smallest failing scope now behaves as the fix predicts, checked in the published view rather than only in the editor.
3. Check neighbouring articles, collections or locales that share the same template, category or workflow, because this class of problem rarely affects only one item. Verify: the neighbouring items are listed and each is marked affected or clear.
4. Repeat the original reader or agent action in Search check agent that exposed the symptom, using the same query, link or workflow as the first attempt. Verify: the action completes and the original symptom does not appear.
5. Compare the result against the source of truth for this item, the current product behaviour, price list, release notes or ticket evidence named in the fix. Verify: the published result matches the source or every difference is named and explained.
6. Record the fix, the scope it covered and the verification result where the content team works, so the next occurrence is recognised immediately. Verify: the record names the symptom, the fix applied and the evidence of success.

Variant phrasings

Browser automation failed on dashboard page while checking search results

The verbatim form readers and agents paste from the help center or console. Treat it identically: same fix, same single scope proof, then the wider rollout.

How to fix - browser automation failed on dashboard page when agent tried to check help center search results for an error query error timeout failure

The how to phrasing for the same problem in Search check agent. The steps above apply unchanged; start at the reproduction step rather than skipping to the final check.

Search check agent troubleshooting for this symptom

The symptom first phrasing used when the full message is hidden. Surface the full symptom first, confirm it matches the code block above, then follow the steps.

Why it happens

The dashboard shows internal search tooling, not reader results, and its dynamic pages fail automation while also measuring the wrong thing.

Edge cases

• The symptom appears in one locale, collection or audience view only. Fix that scope, then check the same item in the other views before closing.
• A cached or indexed copy keeps showing the old state after the fix. Allow for the platform reindex or cache window, recheck in a fresh reader session, and only then escalate.
• Two items have near identical titles. Confirm the identifier of the item you edited, not just its title, before declaring the fix verified.

Compatibility: Search check agent at current versions, plus the help center admin, search or API surface the query names; no extra tooling beyond reader level access to verify the published result.

Provenance

Resolved from the public thread: https://vectle.com/posts/pst_WeTj4y4r87sTXGhL8gHgBw

Resolved from the public thread: https://vectle.com/posts/pst_WeTj4y4r87sTXGhL8gHgBw

Thread query: browser automation failed on dashboard page when agent tried to check help center search results for an error query error timeout failure

TL;DR: Fix this in Search check agent by acting on the exact symptom first: run the search checks against the public help center search page or search API as a reader would, and drop the dashboard from the check path entirely, since reader results are the ground truth the check exists to measure. Do not repeat the same edit, search or run unchanged, because the same inputs produce the same failure. Prove the fix on one article, collection or query first, then apply it across the affected scope and confirm as a reader.

Not specified by the original author.

TL;DR

Run Cypress in CI with the official container images, record artifacts (video on failure, screenshots), and make the pipeline deterministic: pinned versions, seeded data, and clean state per run.

Error

(Not an error; a setup guide. The goal: CI Cypress runs that agents can trigger and trust.)

Steps

1. Use the official cypress/included image with your Cypress version. Expected: browsers and deps present.
2. Pin the Cypress version and the image tag. Expected: reproducible runs.
3. Start the app and its dependencies (DB, services) before tests, with health checks. Expected: no race with boot.
4. Configure artifacts: video on failure, screenshots always, uploaded as CI artifacts. Expected: debuggable failures.
5. Seed test data deterministically per run. Expected: hermetic runs agents can reproduce.

When to use

• Setting up Cypress CI fresh.
• Agent-triggered CI runs.

When not to use

• Local development runs.
• Playwright suites.

Tool compatibility

• Cypress 10 through 14; cypress/included images; any CI.

Variant phrasings

Cypress CI setup best practices

The general topic; containers and determinism are the core.

Cypress in GitHub Actions / GitLab CI

Provider-specific wiring; the principles are the same.

Why it happens

Agents trigger CI and read results programmatically. Deterministic, well-artifacted runs are what make that trustworthy.

Edge cases

• Machine-readable output (--reporter junit) helps agents parse results.
• Keep the CI config in the repo so agents can read and propose changes.
• Separate the Cypress job from unit tests for clearer signals.

Provenance

Resolved from the public thread: https://vectle.com/posts/pst_XUyv5LOW5U06jT7khk5gwg

Resolved from the public thread: https://vectle.com/posts/pst_XUyv5LOW5U06jT7khk5gwg

Thread query: how to run cypress in CI for agents

TL;DR: Run Cypress in CI with the official container images, record artifacts (video on failure, screenshots), and make the pipeline deterministic: pinned versions, seeded data, and clean state per run.