estimation-patterns
Practical estimation techniques for software tasks — methods comparison, decomposition, complexity multipliers, buffer calculation, bias awareness, and communication strategies. Use when estimating features, sprint planning, or presenting timelines to stakeholders.
Why use this skill?
Learn to estimate software tasks accurately with expert techniques. Explore T-Shirt sizing, PERT analysis, and task decomposition to build defensible project timelines.
Install via CLI (Recommended)
clawhub install openclaw/skills/skills/wpank/estimation-atternsWhat This Skill Does
The estimation-patterns skill serves as an analytical framework for software professionals and project managers, providing a structured approach to quantifying work effort. It moves beyond intuition-based guessing by implementing proven methodologies like PERT (Program Evaluation and Review Technique), T-Shirt sizing, and granular task decomposition. By systematically applying complexity multipliers and calculating buffers for uncertainty, this skill helps users generate estimates that are not only defensible but also grounded in empirical risk assessment. It acts as an advisory partner, prompting the user to acknowledge cognitive biases and breaking down monolithic tasks into manageable, high-confidence units.
Installation
To integrate this skill into your environment, use the OpenClaw package manager:
npx clawhub@latest install estimation-patterns
Use Cases
This skill is designed for scenarios where accuracy and stakeholder trust are paramount:
- Sprint Planning: When a team needs to determine velocity based on previous performance data.
- Roadmap Forecasting: Mapping out feature development timelines for stakeholders using T-shirt sizing and relative comparison.
- High-Uncertainty Projects: Applying the Three-Point (PERT) method to R&D tasks or legacy code refactoring where unknowns are high.
- Communication Calibration: Helping developers justify timeline extensions or defend estimates during status meetings.
- Process Improvement: Analyzing historical accuracy to identify patterns of over-optimism or scope creep in previous development cycles.
Example Prompts
- "I need to estimate the migration of our legacy API to GraphQL. Use the three-point method to give me an optimistic, most likely, and pessimistic timeline, and show me how to calculate the buffer for risk."
- "My team is struggling with task decomposition. Help me break down 'Implement User Profile Management' into sub-tasks that are each under 4 hours of effort."
- "We are planning a new feature. Compare T-Shirt sizing versus Story Points for our current roadmap and explain which is safer given our current high-turnover environment."
Tips & Limitations
Estimation is fundamentally an exercise in risk management, not prophecy. The quality of the output is directly dependent on the quality of the input. Always aim for transparency regarding your confidence intervals. Note that this skill requires human judgment to calibrate; automated math cannot account for hidden technical debt or team morale. Use the 'spike' approach mentioned in the documentation if a task remains fuzzy after decomposition. Most importantly, do not treat the resulting estimates as binding contracts, but as probability models that should be updated as more information becomes available during the implementation lifecycle.
Metadata
Not sure this is the right skill?
Describe what you want to build — we'll match you to the best skill from 16,000+ options.
Find the right skillPaste this into your clawhub.json to enable this plugin.
{
"plugins": {
"official-wpank-estimation-atterns": {
"enabled": true,
"auto_update": true
}
}
}Tags(AI)
Related Skills
mermaid-diagrams
Create software diagrams using Mermaid syntax. Use when users need to create, visualize, or document software through diagrams including class diagrams, sequence diagrams, flowcharts, ERDs, C4 architecture diagrams, state diagrams, git graphs, and other diagram types. Triggers include requests to diagram, visualize, model, map out, or show the flow of a system.
api-design-principles
Skill by wpank
auto-context
Automatically read relevant context before major actions. Loads TODO.md, roadmap.md, handoffs, task plans, and other project context files so the AI operates with full situational awareness. Use when starting a task, implementing a feature, refactoring, debugging, planning, or resuming a session.
clear-writing
Write clear, concise prose for humans — documentation, READMEs, API docs, commit messages, error messages, UI text, reports, and explanations. Combines Strunk's rules for clearer prose with technical documentation patterns, structure templates, and review checklists.
track-performance
Track the performance of Uniswap LP positions over time — check which positions need attention, are out of range, or have uncollected fees. Use when the user asks how their positions are doing.