Setup & Installation
Or with the ClawHub CLI, for registry-managed skill folders outside a full OpenClaw workspace:
What This Skill Does
Estimates AI agent task effort using tool-call rounds as the primary unit instead of human developer time. Decomposes tasks into modules, applies risk coefficients, and converts to wallclock time only at the final step. Structured output includes a module breakdown table, risk factors, and a time range.
Avoids the systematic overestimation that occurs when agents anchor to human developer timelines from training data, producing estimates grounded in actual agent iteration speed.
When to use it
- Scoping a coding task before starting an agent session
- Comparing two implementation approaches by round cost
- Setting realistic expectations with a client on agent-assisted work
- Identifying which modules carry the most schedule risk
- Deciding whether a task fits in one session or needs to be split
Example Workflow
Here's how your AI assistant might use this skill in practice.
User asks: How long will it take to build a custom OAuth2 integration with a third-party API?
- 1Decompose the task into modules: token exchange flow, refresh logic, API client wrapper, error handling
- 2Estimate base rounds per module using complexity anchors from the skill table
- 3Assign risk coefficients based on API documentation quality and platform quirks
- 4Sum effective rounds across modules and add 10-20% for integration
- 5Convert total rounds to wallclock time at 3 min/round and output the structured markdown table
Markdown estimation table with per-module round counts, risk levels, integration overhead, and a final wallclock range of 45-60 minutes