Estimate task durations using category mapping and planning heuristics.
npx skills add https://github.com/aviz85/claude-tasks --skill time-estimation
Please help me install this Agent Skill. Skill: time-estimation Source: https://github.com/aviz85/claude-tasks/tree/main/.claude/skills/time-estimation Command: npx skills add https://github.com/aviz85/claude-tasks --skill time-estimation
Time estimation for tasks is often manual, inconsistent, and error-prone, leading to overcommitment or underplanning.
To start using time-estimation, tag tasks with an estimated duration (e.g., - [ ] Implement login page ~1h) and reference the rule set to refine estimates. You can apply estimates when creating items, during reviews, or while planning your day.
High-intent search queries and answers about installing and using this skill.
Task duration estimation maps backlog items to structured categories like Quick, Short, Medium, Long, and Deep Work. It applies planning heuristics such as the 2x rule and task breakdowns to produce realistic sprint schedules.
The 2x rule is a planning heuristic that suggests doubling your initial time estimate to account for unforeseen complexities. It helps prevent overcommitment and underplanning during backlog grooming and standups.
You tag tasks by appending an estimated duration directly to the task item, such as writing - [ ] Implement login page ~1h. This enforces structured category mapping for a realistic day plan during reviews.
Yes, time estimation applies to backlog work across projects by mapping tasks to duration categories. It provides rule-driven guidance for standups, iterations, and backlog grooming regardless of the project scope.
The scheduling categories are Quick, Short, Medium, Long, and Deep Work. This structured mapping ensures consistent duration estimates and prevents error-prone manual guessing during project planning.
Time estimation prevents overcommitment by replacing manual guessing with structured rules like category mapping and the 2x rule. It enforces task breakdowns to keep iteration schedules realistic and manageable.
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