A specialized diagnostic skill for identifying why prompt-driven cron tasks return zero results despite successful execution.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install debug-prompt-driven-cron-agent-zero-output
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 debug-prompt-driven-cron-agent-zero-output 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 Cron Agent Zero Output Debugger?
The Cron Agent Zero Output Debugger is a technical diagnostic tool designed for the Openclaw Skills ecosystem. It specifically targets the frustrating scenario where an AI agent or cron job runs successfully but yields empty results, zero samples, or missing daily reports. It helps developers distinguish between downstream classification failures and upstream retrieval gaps.
This skill is particularly effective for tasks driven by natural language prompts (agentTurn) where fixed script logic is absent. By analyzing entry points, run logs, and actual artifacts in platforms like Discord or Obsidian, it pinpoint high-risk breakpoints such as timezone mismatches, Snowflake string comparison errors, and missing thread starter messages.
Cron Agent Zero Output Debugger Use Cases
Use Cases
- Empty Daily Reviews: Troubleshooting why Obsidian daily digests show zero Discord activity despite active participation.
- Prompt Logic Verification: Confirming if a task relies on improvised model logic versus hardcoded retrieval scripts.
- Data Leakage Audits: Identifying if specific message types, such as thread starters or @mentions, are being skipped during sampling.
- Root Cause Analysis: Providing evidence-based diagnosis for cron tasks that report an 'ok' status but produce no data.
How Cron Agent Zero Output Debugger Works
How It Works
- Identify Entry Point: Locates the job definition in
jobs.jsonto determine if the task is a script-based job or a pure prompt-driven agentTurn. - Source Code Audit: Searches the workspace and Openclaw Skills directories for independent JS/TS implementations or templates related to the task.
- Extraction of Declared Logic: Maps out the business logic declared in the prompt, such as Guild IDs, User IDs, and specific time windows.
- Artifact Comparison: Analyzes the final output (e.g., Markdown files) to detect specific phrases like "No samples found" to confirm the failure stage.
- Log Diagnostics: Reviews
.jsonlrun records to check for status codes, model versions, and the presence (or absence) of audit logs. - Final Validation: Cross-references the 'zero' result against known truth (e.g., actual Discord activity) to identify the specific sampling gap.
Cron Agent Zero Output Debugger Setup
Setup
Ensure you have access to the local Openclaw configuration and workspace directories. No separate installation is required if Openclaw Skills is active.
# Navigate to your Openclaw configuration
cd ~/.openclaw/cron/
# Verify the jobs configuration exists
cat jobs.json
# Check recent run logs for the specific job ID
tail -f ./runs/<job-id>.jsonl
Cron Agent Zero Output Debugger Data Schema & Taxonomy
Data Schema
The skill analyzes the following data structures within the Openclaw Skills environment:
| File Type | Path / Key | Purpose |
|---|---|---|
| Job Config | cron/jobs.json |
Defines schedule, timezone, and payload kind. |
| Run Logs | cron/runs/*.jsonl |
Tracks execution status, timestamps, and model usage. |
| Output Artifacts | Documents/Obsidian/... |
The generated content used to verify error messaging. |
| Payload | payload.message |
The raw prompt driving the agent's sampling behavior. |
| Metadata | openclaw.emoji |
Visual identification within the skill registry. |
Cron Agent Zero Output Debugger Advanced Features
Advanced Features
- Snowflake Debugging: Specialized logic for identifying precision errors in Discord Snowflake ID comparisons (Number vs. String).
- Timezone Boundary Mapping: Validates if
Asia/Shanghaior other offsets are correctly applied to the retrieval window. - Thread-Starter Awareness: Detects if the agent is failing to include the original post that initiated a Discord thread.
- Minimal Fix Recommendations: Generates a step-by-step migration plan to move from unstable pure-prompt tasks to auditable script-based workflows.
SKILL.md
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