A specialized estimation framework that allows AI agents to calculate project effort based on internal operational units rather than human developer timelines.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install agent-estimation
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 agent-estimation 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 Agent Work Estimation?
The Agent Work Estimation skill is a technical protocol designed to solve the human-time anchoring bias common in large language models. When agents estimate tasks, they often replicate timelines found in human developer forums, leading to massive overestimations for tasks they can complete in minutes. By utilizing this framework from the Openclaw Skills library, agents focus on tool-call rounds—the specific cycles of reasoning, coding, and verification—to provide a realistic technical effort assessment.
This skill forces a bottom-up calculation that translates abstract complexity into concrete operational units. By adopting this approach, teams can better align AI agent workflows with actual project requirements, ensuring that every estimate is backed by technical logic rather than generic training data vibes. It is an essential component for any developer looking to integrate AI agents into professional project management pipelines via Openclaw Skills.
Agent Work Estimation Use Cases
- Planning new feature development where the agent needs to provide a delivery timeline.
- Scoping complex refactoring tasks to identify potential logic bottlenecks.
- Evaluating technical debt by breaking down the number of execution rounds needed for cleanup.
- Communicating realistic expectations to human stakeholders during collaborative coding sessions.
How Agent Work Estimation Works
- Decomposition: The agent breaks the primary task into independent, functional modules that can be built and tested separately.
- Base Round Estimation: Each module is assigned a number of tool-call rounds based on complexity patterns, ranging from boilerplate tasks (1-2 rounds) to high-uncertainty projects (8-15 rounds).
- Risk Assignment: A risk coefficient (1.0 to 2.0) is applied to each module to account for documentation gaps, platform quirks, or integration unknowns.
- Total Calculation: The agent sums the effective rounds and adds an integration buffer of 10-20% to account for wiring modules together.
- Wallclock Conversion: The final round count is multiplied by a time-per-round factor (typically 3 minutes) to provide a human-readable duration.
Agent Work Estimation Setup
To implement this logic within your agent environment, include the estimation procedure in your system instructions or reference it as a utility skill. There are no external dependencies required other than the following prompt logic:
# Activate the agent-estimation framework
# Define the atomic 'Round' unit for the agent
# Set the default wallclock conversion to 3 minutes
Agent Work Estimation Data Schema & Taxonomy
The skill organizes estimation data using a structured taxonomy to ensure consistency across different Openclaw Skills implementations:
| Attribute | Type | Description |
|---|---|---|
| Round | Unit | The atomic cycle: Think -> Write -> Execute -> Verify -> Fix. |
| Module | Component | A functional unit composed of 2-15 rounds. |
| Risk Coefficient | Float | Multiplier (1.0 - 2.0) based on ecosystem maturity and documentation. |
| Integration Factor | Percentage | A 10-20% overhead added to the base total for module wiring. |
| Wallclock Time | Duration | The final conversion of rounds into human minutes. |
Agent Work Estimation Advanced Features
- Context-Aware Calibration: Adjust the minutes-per-round parameter based on whether the environment requires manual user testing or fast iteration.
- Risk-Adjusted Totals: Automatically inflates estimates for under-documented APIs or platform-specific permissions issues.
- Anti-Pattern Detection: Actively prevents anchoring to human developer timelines or padding estimates by 'vibes'.
- Structured Reporting: Generates standardized markdown tables for clear integration with other Openclaw Skills and project management tools.
SKILL.md
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