Apify Debug Bundle Skill

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
APIFY_TOKEN configured
A failed or problematic run ID to investigate

Authentication

All API calls authenticate with the APIFY_TOKEN as a Bearer header (Authorization: Bearer $APIFY_TOKEN), and the SDK reads the same token from process.env.APIFY_TOKEN. 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.

Investigate the failed run — pull run summary, dataset stats, and the log tail via the SDK. The core call:

const client = new ApifyClient({ token: process.env.APIFY_TOKEN });
const run = await client.run(runId).get();
const log = await client.run(runId).log().get();

Create the debug bundle — run apify-debug-bundle.sh <RUN_ID>. 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.
Compare against a good run (optional) — diff a successful and failed run field-by-field to spot the delta (compareRuns(successId, failId)).
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.

Output

A single timestamped tarball, apify-debug-YYYYMMDD-HHMMSS.tar.gz, containing:

| File | Contents | |------|----------| | environment.txt | Node/npm versions, installed Apify packages, CLI version | | run-details.json | Run status, options, stats, usage, cost | | run-log.txt | Full run log (secrets auto-redacted by the platform) | | dataset-sample.json | First 5 dataset items | | kv-store-keys.json | Key-value store key listing | | env-redacted.txt | Local .env with all values redacted | | platform-health.json | Apify platform health snapshot |

Attach the tarball directly to an Apify support ticket.

Sensitive Data Handling

Always redact before sharing:

API tokens (apify_api_*)
Proxy passwords
PII (emails, names, IPs)
Custom environment variables

Safe to include:

Run IDs, Actor IDs, dataset IDs
Error messages and stack traces
Run configuration (memory, timeout)
Platform health status

Escalation Path

Check run log for stack trace
Compare with a successful run
Check Apify Status for outages
Create debug bundle
Submit to Apify Support with bundle attached

Error Handling

| Issue | Cause | Solution | |-------|-------|----------| | Run not found | Invalid run ID or expired | Unnamed runs expire after 7 days | | Log unavailable | Run still in progress | Wait for completion or stream live | | Empty dataset | Actor produced no output | Check failedRequestHandler in code | | High CU usage | Memory too high or slow execution | Reduce memory, optimize code |

Examples

Four worked scenarios — a plain FAILED run, an "it worked yesterday" regression diff, an empty-dataset investigation, and live-tailing a hung run — are in examples.md. The quickest path:

export APIFY_TOKEN="apify_api_..."
./apify-debug-bundle.sh abc123DEF          # → apify-debug-20260717-142530.tar.gz
tar -xzf apify-debug-*.tar.gz && tail -40 apify-debug-*/run-log.txt

See examples.md for the full walkthroughs, including reading the comparison output and interpreting a live tail.

Resources

Next Steps

For rate limit issues, see the apify-rate-limits skill.

Agent Skills: Apify Debug Bundle

Install this agent skill to your local

Skill Files