A comprehensive framework for producing accurate, defensible software estimates using mathematical models and systematic decomposition.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install estimation-atterns
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 estimation-atterns 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 Estimation Patterns?
Estimation Patterns is a meta-skill designed for the Openclaw Skills ecosystem that provides developers and managers with a structured toolkit for project forecasting. It moves beyond gut-feeling estimates by introducing rigorous methods such as PERT formulas, complexity multipliers, and historical calibration. By adopting these Openclaw Skills, teams can transform vague guesses into high-confidence timeline ranges that account for technical debt, learning curves, and environmental focus ratios.
The skill emphasizes the technical truth that reality is a distribution rather than a single point. It guides users through the lifecycle of an estimate—from initial T-shirt sizing and granular task decomposition to stakeholder communication and post-project accuracy reviews. This systematic approach ensures that every estimate provided is both realistic and actionable, reducing the frequency of missed deadlines and budget overruns.
Estimation Patterns Use Cases
- Estimating features, bug fixes, or entire project roadmaps with high precision.
- Breaking down complex epics into sub-tasks under 4 hours to uncover hidden technical risks.
- Providing stakeholders with probabilistic ranges and confidence levels instead of fixed dates.
- Calibrating team velocity by comparing historical actuals against predicted effort.
- Identifying and mitigating cognitive biases like the planning fallacy during sprint grooming.
How Estimation Patterns Works
- Choose a context-appropriate estimation method, such as Three-Point PERT for uncertainty or Reference Comparison for recurring tasks.
- Execute task decomposition to break work down until every sub-task represents less than 4 hours of effort.
- Apply stackable complexity multipliers to the base estimate to account for legacy code, new technologies, or unclear requirements.
- Calculate the effective estimate by dividing the raw figure by the team's focus ratio and adding appropriate buffers for unknowns.
- Present the final output as a range (e.g., 3-7 days) with a defined confidence level (High, Medium, or Low).
- Record actual time spent upon completion to update the rolling accuracy ratio for future Openclaw Skills calibration.
Estimation Patterns Setup
To add these estimation capabilities to your local environment, install the package via the command line:
npx clawhub@latest install estimation-patterns
After installation, you can reference these patterns during planning sessions or integrate them into your automated agent workflows.
Estimation Patterns Data Schema & Taxonomy
The Estimation Patterns skill utilizes a structured taxonomy to organize forecasting data and risk variables:
| Component | Organization Method | Purpose |
|---|---|---|
| Decomposition Levels | Hierarchical List | Scales work from Epics (weeks) to Atomic Steps (minutes). |
| Complexity Multipliers | Multiplicative Table | Adjusts estimates for factors like Legacy Code (1.5x) or New Tech (1.5x). |
| Buffer Logic | Percentage-based | Adds 20% for known unknowns and 50% for unknown unknowns. |
| Focus Ratios | Decimal Coefficients | Adjusts for meeting loads (0.45) vs. dedicated focus (0.85). |
| PERT Variables | Mathematical Formula | Calculates expected time using Optimistic, Likely, and Pessimistic inputs. |
Estimation Patterns Advanced Features
- Integrated PERT formula support for calculating standard deviations and confidence intervals.
- Multi-factor risk stacking to calculate compound multipliers for complex environment transitions.
- Focus ratio modeling to adjust timelines based on real-world meeting loads and on-call rotations.
- Historical calibration tracking to identify systematic overestimation or underestimation trends.
- Range-based communication templates designed to manage stakeholder expectations and highlight top risks.
SKILL.md
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