agent-estimation

What This Skill Does

The agent-estimation skill revolutionizes how OpenClaw agents scope tasks by decoupling AI effort from human-centric developer timelines. Traditional models consistently inflate time estimates because they pattern-match against human forum posts, which account for coffee breaks, meetings, and context switching. This skill forces the agent to break down tasks into 'Rounds'—the atomic unit of an AI's operation (think, write, execute, verify, fix). By using a structured, algorithmic approach to estimation, the agent provides a granular breakdown that maps its internal workflow to a realistic wallclock duration, preventing the common failure of massive, inaccurate overestimation.

Installation

You can install the skill directly via the ClawHub CLI with the following command: clawhub install openclaw/skills/skills/hjw21century/agent-estimation

Use Cases

This skill is essential whenever you need a predictable, reliable timeline for development tasks. It is best used for:

• Sprint Planning: Converting high-level requirements into an estimate of agent-active operational time.
• Task Scoping: Determining if a requested feature is a quick iteration or an exploratory deep-dive.
• Efficiency Audits: Evaluating whether an agent's predicted time aligns with its actual performance metrics.
• Resource Allocation: Managing budget or agent-token spend by understanding the projected complexity of specific coding modules.

Example Prompts

1. "I need to build a new CRUD API endpoint for user profiles. Can you use agent-estimation to scope the work?"
2. "We need to integrate a new third-party payment provider into our app. Please perform an estimation, break down the modules, and tell me how many rounds this will take."
3. "Estimate the effort to migrate our frontend from standard CSS to Tailwind. Account for the risk of undocumented UI edge cases."

Tips & Limitations

• Calibration: Always trust the Round counts over subjective time. A single round represents roughly 2-4 minutes of agent operation.
• Granularity: The more you decompose the task into distinct modules, the higher the accuracy of the final estimate.
• Risk Coefficients: Don't be afraid to apply a 2.0 coefficient if you know the library documentation is sparse or the codebase is legacy.
• Limitations: This skill estimates agent operational capacity. It does not account for human latency, external system response times, or hardware resource constraints that may slow down the agent's environment execution.