Agent skill · AI & Agents

work-sampling-analyzer

Work sampling analysis skill for activity distribution and utilization studies.

a5c-ai1,642★ · 1 repos on radarProfile →
claude-codecodexcan modify filesMIT
Install
npx skills add a5c-ai/babysitter --skill work-sampling-analyzer --agent claude-code

Same command for any agent — swap --agent for codex, cursor, copilot.

Facts
Files in the skill folder: 1
SKILL.md size: 13 KB
Bundled scripts: none
Version: 1.0.0
Declared author: babysitter-sdk
Allowed tools: Bash(*)ReadWriteEditGlobGrepWebFetch
Path: library/specializations/domains/science/industrial-engineering/skills/work-sampling-analyzer/SKILL.md
Open the folder on GitHub →
Where it comes from
Stars: 1,642
Language: JavaScript

Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.

From the SKILL.md

# work-sampling-analyzer You are **work-sampling-analyzer** - a specialized skill for work sampling studies to analyze activity distribution and equipment/worker utilization. ## Overview This skill enables AI-powered work sampling including: - Random observation scheduling - Sample size determination - Activity categorization - Statistical confidence intervals - Control chart monitoring - Multi-activity studies - Standard time development from sampling - Utilization analysis ## Capabilities ### 1. Sample Size Determination ```python import numpy as np from scipy import stats import random from datetime import datetime, timedelta def determine_sample_size_binomial(estimated_proportion: float, desired_accuracy: float, confidence_level: float = 0.95): """ Determine required sample size for work sampling estimated_proportion: estimated percentage of time in activity (as decimal) desired_accuracy: desired accuracy (e.g., 0.05 for ±5%) confidence_level: statistical confidence (typically 0.95) """ p = estimated_proportion e = desired_accuracy z = stats.norm.ppf(1 - (1 - confidence_level) / 2) # n = (z² × p × (1-p)) / e² n = (z ** 2 * p * (1 - p)) / (e ** 2) return { "required_observations

What's inside
Steps it walks through
  1. Overview
  2. Capabilities
  3. 1. Sample Size Determination
  4. 2. Random Observation Scheduling
  5. 3. Activity Analysis
  6. 4. Control Chart for Work Sampling
  7. 5. Standard Time from Work Sampling
  8. 6. Multi-Activity Study
  9. Process Integration
  10. Output Format
  11. Best Practices
  12. Constraints
More from babysitter
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About this skill
What does the work-sampling-analyzer skill do?

Work sampling analysis skill for activity distribution and utilization studies.

How do I install it?

Run `npx skills add a5c-ai/babysitter --skill work-sampling-analyzer --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.

Where does this skill come from?

From a5c-ai/babysitter, a repository with 1,642 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.

Is a popular skill a good skill?

Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.

Keep going