القائمة
Anthropometric data analysis skill for workstation design and accommodation.
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npx skills add https://github.com/a5c-ai/babysitter --skill anthropometric-analyzerيبقى الأمر في سطر واحد. مرّر أفقيًا لمراجعته كاملًا قبل النسخ.
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Reference for querying the Atlas knowledge graph through its MCP tools — the SECONDARY enrichment/comparison layer that adds best-practice context to systems you have ALREADY scanned from your real sources (`az`, repos, dirs). Use when you need to look up nodes, edges, kinds, clusters, stats, or wiki pages in Atlas to compare against your real inventory. (atlas graph, query atlas, atlas mcp, search the graph, graph neighbors, atlas record, atlas kinds, enrichment layer)
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This skill should be used when the user asks to "find skills in the wild", "assimilate popular workflows", "discover SKILL.md files in repos", "research external skills", "find workflow patterns", "survey the skill landscape", "what skills exist out there", or wants to investigate public repositories for extractable processes, babysitter plugins, and reusable procedural insights. Searches GitHub for SKILL.md files, classifies repos by archetype, and maintains structured research under docs/reference-repos/.
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| name | anthropometric-analyzer |
| description | Anthropometric data analysis skill for workstation design and accommodation. |
| allowed-tools | Bash(*) Read Write Edit Glob Grep WebFetch |
| metadata | {"author":"babysitter-sdk","version":"1.0.0","category":"ergonomics","backlog-id":"SK-IE-022"} |
| graph | {"domains":["domain:industrial-engineering"],"skillAreas":["skill-area:statistical-analysis","skill-area:organizational-design","skill-area:data-analysis"],"roles":["role:operations-analyst","role:research-engineer"]} |
You are anthropometric-analyzer - a specialized skill for using anthropometric data to design accommodating workspaces.
This skill enables AI-powered anthropometric analysis including:
# US Adult Anthropometric Data (in inches)
# Based on NHANES and military surveys
ANTHROPOMETRIC_DATA = {
"stature": {
"male": {"5th": 64.0, "50th": 69.1, "95th": 74.4},
"female": {"5th": 59.0, "50th": 63.8, "95th": 68.5}
},
"sitting_height": {
"male": {"5th": 33.5, "50th": 35.7, "95th": 38.0},
"female": {"5th": 31.1, "50th": 33.5, "95th": 35.6}
},
"eye_height_sitting": {
"male": {"5th": 28.7, "50th": 31.0, "95th": 33.3},
"female": {"5th": 26.6, "50th": 28.9, "95th": 31.2}
},
"shoulder_height_sitting": {
"male": {"5th": 21.3, "50th": 23.3, "95th": 25.5},
"female": {"5th": 19.5, "50th": 21.5, "95th": 23.6}
},
"elbow_height_sitting": {
"male": {"5th": 7.4, "50th": 9.2, "95th": 11.0},
"female": {"5th": 6.8, "50th": 8.5, "95th": 10.2}
},
"thigh_clearance": {
"male": {"5th": 5.2, "50th": 6.0, "95th": 7.0},
"female": {"5th": 4.8, "50th": 5.7, "95th": 6.8}
},
"knee_height_sitting": {
"male": {"5th": 19.3, "50th": 21.4, "95th": 23.4},
"female": {"5th": 17.8, "50th": 19.6, "95th": 21.4}
},
"popliteal_height": { # Height to back of knee
"male": {"5th": 15.5, "50th": 17.3, "95th": 19.1},
"female": {"5th": 14.0, "50th": 15.7, "95th": 17.5}
},
"buttock_knee_length": {
"male": {"5th": 21.4, "50th": 23.4, "95th": 25.4},
"female": {"5th": 20.5, "50th": 22.4, "95th": 24.4}
},
"buttock_popliteal_length": {
"male": {"5th": 17.3, "50th": 19.2, "95th": 21.1},
"female": {"5th": 16.7, "50th": 18.5, "95th": 20.4}
},
"forward_reach": {
"male": {"5th": 29.5, "50th": 32.6, "95th": 35.7},
"female": {"5th": 26.3, "50th": 29.2, "95th": 32.1}
},
"shoulder_breadth": {
"male": {"5th": 16.7, "50th": 18.3, "95th": 19.9},
"female": {"5th": 14.5, "50th": 16.0, "95th": 17.6}
},
"hip_breadth_sitting": {
"male": {"5th": 12.6, "50th": 14.2, "95th": 16.1},
"female": {"5th": 13.0, "50th": 15.0, "95th": 17.4}
}
}
def get_anthropometric_value(dimension: str, percentile: str,
gender: str = "combined"):
"""
Get anthropometric value for a dimension
"""
if gender == "combined":
male_val = ANTHROPOMETRIC_DATA[dimension]["male"][percentile]
female_val = ANTHROPOMETRIC_DATA[dimension]["female"][percentile]
return (male_val + female_val) / 2
else:
return ANTHROPOMETRIC_DATA[dimension][gender.lower()][percentile]
def design_for_percentile(dimension: str, design_type: str, gender: str = "combined"):
"""
Recommend design dimension based on type of design
design_type:
- "reach": Use 5th percentile (shortest reach)
- "clearance": Use 95th percentile (largest dimension)
- "adjustable": Provide range 5th to 95th
"""
if design_type == "reach":
value = get_anthropometric_value(dimension, "5th", gender)
explanation = "Design to 5th percentile to accommodate shortest reach"
elif design_type == "clearance":
value = get_anthropometric_value(dimension, "95th", gender)
explanation = "Design to 95th percentile to accommodate largest users"
elif design_type == "adjustable":
low = get_anthropometric_value(dimension, "5th", gender)
high = get_anthropometric_value(dimension, "95th", gender)
value = {"min": low, "max": high}
explanation = "Provide adjustability from 5th to 95th percentile"
else:
value = get_anthropometric_value(dimension, "50th", gender)
explanation = "Using 50th percentile (average)"
return {
"dimension": dimension,
"design_type": design_type,
"value": value,
"unit": "inches",
"explanation": explanation
}
def calculate_reach_zones(forward_reach: float, shoulder_height: float):
"""
Calculate primary, secondary, and tertiary reach zones
"""
return {
"primary_zone": {
"description": "Frequent reaching, most comfortable",
"horizontal_radius": forward_reach * 0.4, # ~40% of max reach
"height_range": {
"min": shoulder_height * 0.7,
"max": shoulder_height * 1.1
},
"angle": "0-15 degrees from centerline"
},
"secondary_zone": {
"description": "Occasional reaching",
"horizontal_radius": forward_reach * 0.6, # ~60% of max reach
"height_range": {
"min": shoulder_height * 0.5,
"max": shoulder_height * 1.2
},
"angle": "15-45 degrees from centerline"
},
"tertiary_zone": {
"description": "Infrequent reaching only",
"horizontal_radius": forward_reach * 0.9, # ~90% of max reach
"height_range": {
"min": shoulder_height * 0.3,
"max": shoulder_height *
},
:
}
}
():
forward_reach = get_anthropometric_value(, , worker_gender)
shoulder_height = get_anthropometric_value(, , worker_gender)
zones = calculate_reach_zones(forward_reach, shoulder_height)
item_frequency == :
zone =
item_frequency == :
zone =
:
zone =
{
: zone,
: zones[zone],
:
}
def recommend_work_surface_height(task_type: str, posture: str = "sitting",
worker_gender: str = "combined"):
"""
Recommend work surface height based on task type
task_type: "precision", "light_assembly", "heavy_work"
posture: "sitting" or "standing"
"""
if posture == "sitting":
elbow_height = get_anthropometric_value("elbow_height_sitting", "50th", worker_gender)
seat_height = get_anthropometric_value("popliteal_height", "50th", worker_gender)
base_height = seat_height + elbow_height
adjustments = {
"precision": {
"adjustment": +4, # Above elbow for close viewing
"range": (base_height + 2, base_height + 6),
"reason": "Higher for visual tasks requiring close work"
},
"light_assembly": {
"adjustment": 0, # At elbow height
"range": (base_height - 2, base_height + 2),
"reason": "At elbow height for typical hand work"
},
"heavy_work": {
"adjustment": -4, # Below elbow for force application
"range": (base_height - 6, base_height - 2),
"reason":
}
}
:
stature = get_anthropometric_value(, , worker_gender)
elbow_height = stature *
adjustments = {
: {
: +,
: (elbow_height + , elbow_height + ),
:
},
: {
: -,
: (elbow_height - , elbow_height),
:
},
: {
: -,
: (elbow_height - , elbow_height - ),
:
}
}
task_adjustment = adjustments.get(task_type, adjustments[])
recommended_height = (elbow_height posture ==
seat_height + elbow_height) + task_adjustment[]
{
: task_type,
: posture,
: (recommended_height, ),
: task_adjustment[],
: task_adjustment[],
:
}
def calculate_clearances(posture: str = "sitting", worker_gender: str = "combined"):
"""
Calculate minimum clearance dimensions
"""
if posture == "sitting":
clearances = {
"vertical_clearance_under_surface": {
"minimum": get_anthropometric_value("thigh_clearance", "95th", worker_gender) +
get_anthropometric_value("popliteal_height", "95th", worker_gender) + 2,
"dimension": "From floor to underside of work surface",
"percentile_used": "95th",
"reason": "Accommodate largest thigh + knee height"
},
"knee_room_depth": {
"minimum": get_anthropometric_value("buttock_knee_length", "95th", worker_gender) + 2,
"dimension": "Depth for knees under surface",
"percentile_used": "95th"
},
"seat_width": {
"minimum": get_anthropometric_value("hip_breadth_sitting", "95th", worker_gender) + 2,
"dimension": "Seat pan width",
"percentile_used": "95th"
},
"passage_width": {
: get_anthropometric_value(, , ) + ,
: ,
:
}
}
:
clearances = {
: {
: get_anthropometric_value(, , ) + ,
: ,
:
},
: {
: ,
: ,
:
},
: {
: ,
: ,
:
}
}
clearances
def calculate_adjustability_range(dimension: str, accommodation_level: float = 0.90):
"""
Calculate required adjustability range to accommodate population percentage
accommodation_level: 0.90 for 90% (5th-95th), 0.95 for 95% (2.5th-97.5th)
"""
# Get male and female data
male_5th = ANTHROPOMETRIC_DATA[dimension]["male"]["5th"]
male_95th = ANTHROPOMETRIC_DATA[dimension]["male"]["95th"]
female_5th = ANTHROPOMETRIC_DATA[dimension]["female"]["5th"]
female_95th = ANTHROPOMETRIC_DATA[dimension]["female"]["95th"]
# Combined population range
population_min = min(female_5th, male_5th)
population_max = max(female_95th, male_95th)
# Standard deviation estimate
male_sd = (male_95th - male_5th) / 3.29 # 5th to 95th is 3.29 SDs
female_sd = (female_95th - female_5th) / 3.29
return {
"dimension": dimension,
"accommodation_level": f"{accommodation_level * 100}%",
"required_range": {
"min": round(population_min, 1),
"max": round(population_max, 1)
},
"adjustment_span": round(population_max - population_min, 1),
"male_range": {"5th": male_5th, "95th": male_95th},
: {: female_5th, : female_95th},
:
}
This skill integrates with the following processes:
workstation-design-optimization.jsergonomic-risk-assessment.js{
"design_type": "Seated Workstation",
"work_surface_height": {
"recommended": 28.5,
"adjustable_range": [26, 31],
"unit": "inches"
},
"reach_zones": {
"primary_radius": 11.8,
"secondary_radius": 17.7
},
"clearances": {
"under_surface": 25.5,
"knee_depth": 27.4
},
"accommodation": "90% of mixed population"
}