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johnatas-henrique
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estimate

Generate structured software task effort estimates with timelines and risk assessments.

Updated Jul 16, 2026
Search Tags:#estimation#project-planning#sprint-planning#risk-analysis#code-complexity#velocity-tracking#task-effort
One-click install
npx skills add https://github.com/johnatas-henrique/overdrive --skill estimate-johnatas-henrique
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: estimate
Source: https://github.com/johnatas-henrique/overdrive/tree/main/.opencode/skills/estimate
Command: npx skills add https://github.com/johnatas-henrique/overdrive --skill estimate-johnatas-henrique

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Unstructured, ad-hoc task estimates often lead to missed deadlines, overallocated teams, and unreliable sprint commitments. This skill eliminates that problem by providing a standardized, data-driven workflow for generating accurate effort estimates for development tasks.

Core Features & Use Cases

  • Multi-Factor Complexity Analysis: Evaluates code size, dependency coupling, cyclomatic complexity, and integration points to assess task difficulty.
  • Historical Data Integration: Leverages past sprint velocity and completed similar tasks to ground estimates in real project performance data.
  • Structured Output Generation: Produces consistent estimate documents with optimistic, expected, and pessimistic timelines, confidence levels, risk assessments, and task breakdowns.
  • Use Case: For example, when planning a new gameplay feature for the Overdrive racing game, use this skill to generate a realistic effort estimate to inform sprint planning and resource allocation.

Quick Start

Use the estimate skill to generate a structured effort estimate for the task of adding a new car model with custom physics to the game.

Frequently Asked Questions about estimate

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I generate accurate software task effort estimates for sprint planning?▼

Software task effort estimates are generated by analyzing code complexity, dependency coupling, and historical sprint velocity to produce structured timelines. This workflow outputs optimistic, expected, and pessimistic estimates alongside confidence levels and risk assessments to support informed sprint planning and resource allocation.

What is multi-factor complexity analysis in software project planning?▼

Multi-factor complexity analysis in project planning evaluates code size, dependency coupling, cyclomatic complexity, and integration points to assess task difficulty. This mechanism grounds effort estimates in actual code structure and integration challenges rather than relying on ad-hoc guesses.

How do I calculate optimistic, expected, and pessimistic timelines for feature scoping?▼

Calculating optimistic, expected, and pessimistic timelines for feature scoping involves assessing historical velocity, code complexity, and risk factors. This produces a structured estimate document that helps engineering teams avoid missed deadlines and overallocation during resource allocation.

Does historical sprint velocity tracking improve task estimation accuracy?▼

Historical sprint velocity tracking improves task estimation accuracy by grounding predictions in real project performance data. By integrating past completed similar tasks with code complexity analysis, engineering teams eliminate unstructured ad-hoc estimates and achieve reliable sprint commitments.

Can I use risk analysis and confidence levels for engineering resource allocation?▼

Risk analysis and confidence levels can be used for engineering resource allocation by providing structured estimate documents that highlight potential delays. Assessing task dependencies and code complexity helps teams allocate resources effectively and avoid overallocation during sprint planning.

Why do unstructured task estimates lead to missed deadlines in software development?▼

Unstructured task estimates lead to missed deadlines because they lack data-driven analysis of code complexity and historical velocity. Without standardized effort estimates that include risk assessments and confidence levels, engineering teams make unreliable sprint commitments and face team overallocation.

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