Agent skill · data analytics · jeremylongshore
apify-debug-bundle
Collect Apify debug evidence for support tickets and troubleshooting. Use when an Actor run has failed, is stuck, or produced empty output and you need to gather run metadata, logs, dataset samples, and environment info before opening a support ticket. Trigger with "apify debug", "apify support bundle", "collect apify logs", "apify diagnostic", "apify run failed why".
Why this skill is useful
Provides a specific script for collecting Apify run diagnostics that the AI wouldn't generate on its own.
What it needs
Requires apify-client installed locally. Requires apify account access. About 3k tokens when loaded. Last updated 2026-08-07. 2,604 stars on the source repository.
What this skill does
Apify Debug Bundle Overview Collect all diagnostic information needed to troubleshoot failed Actor runs and prepare Apify support tickets. Pulls run metadata, logs, dataset samples, and environment info into a single bundle so a support engineer (or you) can diagnose the failure without live access to your account. Prerequisites apify-client installed APIFYTOKEN configured A failed or problematic run ID to investigate Authentication All API calls authenticate with the APIFYTOKEN as a Bearer header (Authorization: Bearer $APIFYTOKEN), and the SDK reads the same token from process.env.APIFYTOKEN. Get the token from the Apify Console under Settings → Integrations → Personal API tokens. Never commit it — the bundle script redacts any local .env before packaging, and the platform auto-redacts secrets inside run logs. Instructions The workflow has four steps. The skeleton below is enough to run it; each step's full implementation lives in implementation.md. 1. Investigate the failed run — pull run summary, dataset stats, and the log tail via the SDK. The core call: 2. Create the debug bundle — run apify-debug-bundle.sh <RUNID>. It collects environment info, run details, log, a 5-item dataset sample, key-value store keys, a redacted .env, and platform health, then packages everything into a timestamped .tar.gz. Full script in implementation.md. 3. Compare against a good run (optional) — diff a successful and failed run field-by-field to spot the delta (compareRuns(successId, failId)). 4. Live-tail a running Actor (optional) — stream logs when the final log is not yet available. For copy-pasteable code for every step, see implementation.md. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills jeremylongshore/apify-debug-bundle