Research Tracker

What This Skill Does

The Research Tracker by julian1645 is a robust CLI-based state management system designed to oversee autonomous AI research agents. It utilizes a SQLite-backed append-only ledger to maintain transparency and persistence throughout long-running investigation tasks. By providing a structured way to log progress, heartbeat signals, and inter-agent communication, this skill ensures that agents remain aligned with user objectives, even when performing complex, multi-step research. It essentially acts as a 'control plane' for background AI workers, preventing context loss and providing an audit trail for autonomous decision-making.

Installation

To install this skill, use the ClawHub CLI: clawhub install openclaw/skills/skills/julian1645/research-tracker

For the underlying CLI utility, you must have the research tracker binary installed on your system. You can install it via Homebrew (brew tap 1645labs/tap && brew install julians-research-tracker) or directly via Go (go install github.com/1645labs/julians-research-tracker/cmd/research@latest). Ensure the binary is in your PATH so OpenClaw can invoke it during agent execution.

Use Cases

• Autonomous Investigation: When an agent needs to perform deep-web analysis over several hours, use the tracker to log steps and checkpoints.
• Multi-Agent Handoffs: Coordinate between different agents by pushing instructions into the pending queue, allowing a primary agent to assign tasks to a worker agent.
• Background Oversight: Monitor the 'liveness' of background agents via heartbeats to detect if a process has hung or crashed.
• Research Audit: Maintain a permanent record of what steps were taken and when, enabling you to inspect the reasoning path after a project is finished.

Example Prompts

1. "Initialize a new research project for market analysis of the renewable energy sector and set the objective to identify top 5 competitors."
2. "Check the status of the current market analysis agent. If it is blocked, show me the reason for the blockage so I can provide guidance."
3. "Send an urgent instruction to the background agent to pivot its focus toward enterprise-level pricing models instead of consumer retail."

Tips & Limitations

• Persistence: Because this tool uses SQLite, the state is persisted even if your shell session closes. This makes it ideal for background cron-job style research.
• Granularity: Use STEP_BEGIN and STEP_COMPLETE consistently. The more granular your logging, the easier it is to debug if an agent deviates from the objective.
• Graceful Shutdown: Always use research stop-signal rather than killing the process to allow the agent to clean up its current state and save progress to the database.
• Limit: This is not a primary data storage solution for large datasets. Keep the payload sizes modest; use external databases or files for raw research data.