trace-debug
Skill16 starsChanged 27 days agoMITView on GitHub
Read run traces and choose the next action when a fetch/extract fails.
In this repository10 other files it gives its agents.
- AGENTS.mdAGENTS.md
- CLAUDE.mdCLAUDE.md
- batch-jobsSkill
- compliance-policySkill
- cost-controlSkill
- crawlSkill
- extractSkill
- interact-browserSkill
- protected-fetchSkill
- zenrowsSkill
The file
We split this skill into the rules it gives an agent
1 rule. About 0 are common, 0 are distinctive, and 1 is very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈0
Distinctive · estimate
This author's own choices. Worth a look.
1
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Very specific: line 10
Every fetch/extract writes a trace under .zenrows/runs/<run-id>/ and .zenrows/traces/<run-id>/ (secret-free).
≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
Trace & debug
Every fetch/extract writes a trace under .zenrows/runs/<run-id>/ and
.zenrows/traces/<run-id>/ (secret-free).
Commands
zenrows trace inspect <run-id> # raw run record
zenrows trace explain <run-id> # what happened + likely cause + next action + exact command
zenrows trace replay <run-id> # reconstruct the command line
zenrows trace export <run-id> # JSON for sharingFailure → action map
FETCH_FAILED/ empty content → first retry in auto mode (mode=auto), which escalates for you and bills only for what succeeds. Only when auto mode has failed on its own, take manual control with--manual --js-render, then--premium-proxy. For slow pages, add--wait-for <selector>to a--manual --js-renderrun; auto mode may ignore it. Each step multiplies the cost of the request, and both together are the most expensive configuration available ([[cost-control]]).FETCH_FAILED"The Zenrows API answered HTTP 502/503/504 after N retries" → the CLI already retried a busy answer (--retries). Wait a minute and run the same command again; do not escalate, a costlier configuration does not fix it. A 429 ("Rate limited") keeps its advice with or without--retries: wait a few seconds and retry; if it persists, lower concurrency.REQUEST_TIMEOUT→ the CLI stopped waiting; the API was reached. Raise--timeout(default 200000ms, above the API's own 180s budget), or drop--wait-forso the request finishes inside that budget and the API returns its own error. Not a connectivity problem — do not chase the network.BACKEND_UNAVAILABLE→ a genuine transport failure (DNS/TCP/TLS). Check connectivity andzenrows config show.AUTH_INVALID→ re-check the key,zenrows login --api-key ….KEY_CREDIT_CAP_REACHED→ this API key hit one of its own credit caps (HTTP 402, gateway codeAUTH014, Batchapi_key_cap_reached). The account still has credits and its other keys keep working. Do not retry and do not escalate:zenrows usageshows the cap and when it resets. Wait for the reset, or have the account owner raise or remove the cap at https://app.zenrows.com/settings/api-keys.POLICY_MAX_CREDITS_EXCEEDED→ the account itself is out of credits (or a local policy credit limit was hit). Do not retry-loop;zenrows usageshows when credits renew. Top up or upgrade, or claim the account if it is an auto-created Free plan.BATCH_FAILED→ the batch run endedfailedfor a reason other than credits, subscription or key cap (those getPOLICY_MAX_CREDITS_EXCEEDED,AUTH_INVALIDorKEY_CREDIT_CAP_REACHED, with the run'sfailure_reasonasserver_codeand itsjob_id);failure_detailinzenrows batch status <id> --jsonsays why. Once fixed, run the unfinished tasks withzenrows batch rerun <id> --status failed,pending,processing; do not resubmit. Also a 400 (fix the flagged tasks) or a 5xx (retry).BATCH_QUOTA_EXCEEDED→ 429: 3 active jobs (shared with Crawl), 5 scheduled jobs, or an open job's task cap. Nothing was created; free a slot (seezenrows batch listandzenrows crawl list).BATCH_ACCESS_DENIED/BATCH_NOT_FOUND→ 403 Batch not enabled, or 404 wrong or deleted job id. Do not retry.BATCH_RUN_IN_PROGRESS,BATCH_RUN_ENDED,BATCH_NO_MATCHING_TASKS,BATCH_RERUN_WINDOW_EXPIRED,BATCH_REQUEST_IN_FLIGHT,BATCH_CONFLICT,BATCH_TASK_NOT_READY,BATCH_TASK_FAILED,BATCH_RESULT_EXPIRED→ the Batch API's 409/410/422 answers;server_codenames the case andnext_actionthe fix. See [[batch-jobs]].CRAWL_NOT_ENABLED→ the API answered 403REQS008: Crawl is not enabled for this account. Do not retry; ask Zenrows support for access.CRAWL_INSUFFICIENT_CREDITS/CRAWL_KEY_CAP_REACHED→ Crawl's 402: the account is out of credits, or this key reached its credit cap (AUTH014). The same codes come back for a crawl that credits ended (stopped, orfailedon older deployments;server_codeinsufficient_credits/api_key_cap_reached); its fetched pages stay readable. Same advice asPOLICY_MAX_CREDITS_EXCEEDED/KEY_CREDIT_CAP_REACHED.CRAWL_TOO_MANY_CRAWLS→ 429too_many_crawls: the account has reached its limit of active jobs (3 by default), shared with its Batch jobs. Nothing was created; retry afterretry_afterseconds (seezenrows crawl list).CRAWL_FAILED→ the crawl endedfailed, orstoppedwith an error other than credits;error.codeinzenrows crawl get <id> --jsonsays why, witherror.detail. See [[crawl]].PARAM_CONFLICT_AUTO_MANUAL→ drop the managed flags or add--manual.CAPABILITY_UNAVAILABLE→ the primitive is not available on this account (e.g. beta/invite-only); use the local-spec path where offered.
Escalate only with evidence from the trace. See [[cost-control]].
Does it work?
Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
No reviews yet
People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.
- It worked
- Partly
- It didn't
Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.
Your trail
Files you open will collect here, so you can find them again.
Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.
Now do this for your own.
Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.

