A comprehensive diagnostic skill for AK Data that unifies task and crawler timeout reporting with automated log enrichment and gap analysis.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install ak-data-daily-timeout-report
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install ak-data-daily-timeout-report using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is AK Data Daily Timeout Report?
The AK Data Daily Timeout Report is a specialized skill designed for deep-dive analysis of data processing latencies. It integrates two critical perspectives: task-level timeouts (from creation to break) and crawler-level timeouts (from departure to arrival). By providing a synchronized view, this skill allows developers using Openclaw Skills to distinguish between crawling bottlenecks and downstream delivery issues.
This tool is essential for maintaining high-performance data pipelines, offering not just raw metrics but also intelligent diagnostic conclusions based on the gap between different processing stages. It streamlines the investigation process by automatically enriching timeout samples with request payloads and session metadata, enabling rapid troubleshooting for complex job types like AmazonListingJob.
AK Data Daily Timeout Report Use Cases
- Comparing task vs. crawler timeout rates to identify specific pipeline bottlenecks.
- Generating daily health snapshots for AK Data job types.
- Investigating market-specific latency risks (e.g., assessing why Japan or Poland markets are lagging).
- Retrieving enriched logs and request samples for specific timeout events to debug failures.
- Monitoring multi-day timeout trends to identify systemic performance degradation.
How AK Data Daily Timeout Report Works
- The user triggers the skill via a CLI command specifying the date, job type, and report scope.
- The system concurrently executes sub-skills for task-created reports and crawler-departure reports.
- Data is aggregated into a unified JSON report where task and crawler metrics are aligned by market and state.
- An automated diagnostic engine calculates the 'gap' between task and crawler timeout rates to suggest the likely source of delay.
- If requested, the skill performs a drill-down operation to fetch and enrich specific samples with full log states and request data.
- The final output is presented as a structured report with actionable insights and file paths for further inspection.
AK Data Daily Timeout Report Setup
To use this skill within the Openclaw Skills framework, ensure your environment is configured for AK Data access and run the following commands:
# For a single day report
./run.sh day --date-bjt 2026-04-05 --job-type AmazonListingJob
# For a trend report across a date range
./run.sh trend --start-date-bjt 2026-04-01 --end-date-bjt 2026-04-05 --job-type AmazonListingJob
AK Data Daily Timeout Report Data Schema & Taxonomy
The skill organizes data into a structured hierarchy to facilitate both overview and deep-dive analysis:
| Object | Description |
|---|---|
taskTimeout |
Contains baseline metrics for tasks created within the BJT natural day window. |
crawlerTimeout |
Tracks crawler duration metrics based on departure time windows. |
comparison |
Calculates gap percentage and provides a diagnostic string (e.g., crawler vs. delivery issues). |
samples |
Enriched records including req_ssn, log_state, market, and the full request payload. |
diagnosis |
Automated logic identifying if risks are highly overlapped across markets. |
AK Data Daily Timeout Report Advanced Features
- Intelligent Gap Diagnosis: Automatically detects if timeouts are driven by the crawler layer or the delivery tail-end.
- Automated Log Enrichment: Supplements raw timeout data with session IDs, log tables, and truncated request JSON.
- Market-Specific Deep Dives: Supports splitting metrics by market and identifying top-risk regions.
- Flexible Drill-Down: Allows users to request specific samples by status (e.g., PENDING, WAITING) with configurable limits.
- Robust Error Handling: Provides partial results if one reporting layer fails, ensuring continuous observability.
SKILL.md
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