Getting it into your agent
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npx skills add datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction --skill capacity-planninggit clone --depth 1 https://github.com/datadrivenconstruction/DDC_Skills_for_AI_Agents_in_ConstructionWrote this? Show the measurements
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[](https://agentmods.dev/skills/datadrivenconstruction/ddc_skills_for_ai_agents_in_construction/capacity-planning)<a href="https://agentmods.dev/skills/datadrivenconstruction/ddc_skills_for_ai_agents_in_construction/capacity-planning"><img src="https://agentmods.dev/badge/skills/datadrivenconstruction/ddc_skills_for_ai_agents_in_construction/capacity-planning/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/datadrivenconstruction/ddc_skills_for_ai_agents_in_construction/capacity-planning"><img src="https://agentmods.dev/badge/skills/datadrivenconstruction/ddc_skills_for_ai_agents_in_construction/capacity-planning.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00029 | $0.04496 |
| Opus 5 | $0.00015 | $0.02248 |
| Sonnet 5 | $0.00006 | $0.00899 |
| Haiku 4.5 | $0.00003 | $0.00450 |
Grade A, and why
capacity-planning scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
Copies of this mod
1 near-identical copy found in the catalogue:
- capacity-planning — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 559 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Capacity Planning
Overview
Strategic capacity planning for construction organizations. Forecast resource requirements based on project pipeline, identify capacity constraints, optimize staffing levels, and support go/no-go decisions on new project pursuits.
Capacity Planning Framework
┌─────────────────────────────────────────────────────────────────┐
│ CAPACITY PLANNING │
├─────────────────────────────────────────────────────────────────┤
│ │
│ DEMAND FORECAST CAPACITY ANALYSIS DECISIONS │
│ ─────────────── ───────────────── ───────── │
│ │
│ Current Projects → Available: Pursue new │
│ • Project A (Active) 👷 PM: 5 project? │
│ • Project B (Active) 👷 Supers: 12 ──────── │
│ • Project C (Starting) 📐 Engineers: 8 ✅ Capacity │
│ ⚠️ Stretch │
│ Pipeline: → Required: ❌ Decline │
│ • Bid D (60% win) 👷 PM: 7 │
│ • Bid E (40% win) 👷 Supers: 15 │
│ • Opportunity F 📐 Engineers: 10 │
│ │
│ GAP ANALYSIS: ACTIONS: │
│ • PM: -2 (deficit) • Hire 2 PMs │
│ • Supers: -3 (deficit) • Promote from within │
│ • Engineers: -2 (deficit) • Partner with firm │
│ │
└─────────────────────────────────────────────────────────────────┘
Technical Implementation
from dataclasses import dataclass, field
from typing import List, Dict, Optional, Tuple
from datetime import datetime, timedelta
from enum import Enum
import statistics
class ResourceRole(Enum):
PROJECT_MANAGER = "project_manager"
SUPERINTENDENT = "superintendent"
PROJECT_ENGINEER = "project_engineer"
ESTIMATOR = "estimator"
SCHEDULER = "scheduler"
SAFETY_MANAGER = "safety_manager"
QC_MANAGER = "qc_manager"
ADMIN = "admin"
class ProjectPhase(Enum):
PURSUIT = "pursuit"
PRECONSTRUCTION = "preconstruction"
CONSTRUCTION = "construction"
CLOSEOUT = "closeout"
class OpportunityStatus(Enum):
IDENTIFIED = "identified"
PURSUING = "pursuing"
BID_SUBMITTED = "bid_submitted"
NEGOTIATING = "negotiating"
WON = "won"
LOST = "lost"
@dataclass
class StaffMember:
id: str
name: str
role: ResourceRole
capacity: float = 1.0 # FTE
current_assignment: str = ""
availability_date: datetime = None
skills: List[str] = field(default_factory=list)
max_project_value: float = 0 # Max project size they can handle
@dataclass
class ProjectDemand:
project_id: str
project_name: str
value: float
phase: ProjectPhase
start_date: datetime
end_date: datetime
probability: float = 1.0 # 1.0 for active, <1 for pipeline
resource_needs: Dict[ResourceRole, float] = field(default_factory=dict)
@dataclass
class CapacityGap:
role: ResourceRole
period_start: datetime
period_end: datetime
demand: float
capacity: float
gap: float
severity: str
@dataclass
class CapacityForecast:
forecast_date: datetime
horizon_months: int
total_demand_fte: float
total_capacity_fte: float
utilization_pct: float
gaps: List[CapacityGap]
recommendations: List[str]
class CapacityPlanner:
"""Plan organizational capacity for construction projects."""
# Typical staffing ratios by project value
STAFFING_RATIOS = {
ResourceRole.PROJECT_MANAGER: 20000000, # 1 PM per $20M
ResourceRole.SUPERINTENDENT: 10000000, # 1 Super per $10M
ResourceRole.PROJECT_ENGINEER: 15000000, # 1 PE per $15M
ResourceRole.ESTIMATOR: 50000000, # 1 Estimator per $50M (pursuit)
ResourceRole.SCHEDULER: 30000000, # 1 Scheduler per $30M
ResourceRole.SAFETY_MANAGER: 25000000, # 1 Safety per $25M
}
# Phase factors (multiply by role ratio)
PHASE_FACTORS = {
ProjectPhase.PURSUIT: {"estimator": 1.5, "pm": 0.3},
ProjectPhase.PRECONSTRUCTION: {"pm": 0.7, "pe": 0.5, "scheduler": 0.5},
ProjectPhase.CONSTRUCTION: {"pm": 1.0, "super": 1.0, "pe": 1.0, "safety": 1.0},
ProjectPhase.CLOSEOUT: {"pm": 0.5, "pe": 0.3, "admin": 1.0}
}
def __init__(self, organization_name: str):
self.organization_name = organization_name
self.staff: Dict[str, StaffMember] = {}
self.projects: Dict[str, ProjectDemand] = {}
self.pipeline: Dict[str, ProjectDemand] = {}
def add_staff(self, id: str, name: str, role: ResourceRole,
capacity: float = 1.0, current_assignment: str = "",
availability_date: datetime = None,
max_project_value: float = 0) -> StaffMember:
"""Add staff member to capacity pool."""
member = StaffMember(
id=id,
name=name,
role=role,
capacity=capacity,
current_assignment=current_assignment,
availability_date=availability_date or datetime.now(),
max_project_value=max_project_value
)
self.staff[id] = member
return member
def add_active_project(self, id: str, name: str, value: float,
phase: ProjectPhase, start_date: datetime,
end_date: datetime) -> ProjectDemand:
"""Add active project to demand forecast."""
# Calculate resource needs based on value and phase
needs = self._calculate_resource_needs(value, phase)
project = ProjectDemand(
project_id=id,
project_name=name,
value=value,
phase=phase,
start_date=start_date,
end_date=end_date,
probability=1.0,
resource_needs=needs
)
self.projects[id] = project
return project
def add_pipeline_opportunity(self, id: str, name: str, value: float,
win_probability: float,
expected_start: datetime,
duration_months: int) -> ProjectDemand:
"""Add pipeline opportunity to demand forecast."""
needs = self._calculate_resource_needs(value, ProjectPhase.CONSTRUCTION)
opportunity = ProjectDemand(
project_id=id,
project_name=name,
value=value,
phase=ProjectPhase.PURSUIT,
start_date=expected_start,
end_date=expected_start + timedelta(days=duration_months * 30),
probability=win_probability,
resource_needs=needs
)
self.pipeline[id] = opportunity
return opportunity
def _calculate_resource_needs(self, value: float,
phase: ProjectPhase) -> Dict[ResourceRole, float]:
"""Calculate resource needs based on project value and phase."""
needs = {}
for role, ratio in self.STAFFING_RATIOS.items():
base_need = value / ratio
# Apply phase factor
phase_key = role.value.split('_')[0][:3]
factor = 1.0
if phase in self.PHASE_FACTORS:
factor = self.PHASE_FACTORS[phase].get(phase_key, 1.0)
needs[role] = base_need * factor
return needs
def get_current_capacity(self) -> Dict[ResourceRole, float]:
"""Get current capacity by role."""
capacity = {role: 0.0 for role in ResourceRole}
for member in self.staff.values():
if member.availability_date <= datetime.now():
capacity[member.role] += member.capacity
return capacity
def get_capacity_at_date(self, target_date: datetime) -> Dict[ResourceRole, float]:
"""Get projected capacity at future date."""
capacity = {role: 0.0 for role in ResourceRole}
for member in self.staff.values():
if member.availability_date <= target_date:
capacity[member.role] += member.capacity
return capacity
def calculate_demand(self, target_date: datetime,
include_pipeline: bool = True,
pipeline_threshold: float = 0.0) -> Dict[ResourceRole, float]:
"""Calculate resource demand at date."""
demand = {role: 0.0 for role in ResourceRole}
# Active projects
for project in self.projects.values():
if project.start_date <= target_date <= project.end_date:
for role, need in project.resource_needs.items():
demand[role] += need * project.probability
# Pipeline (weighted by probability)
if include_pipeline:
for opp in self.pipeline.values():
if opp.probability >= pipeline_threshold:
if opp.start_date <= target_date <= opp.end_date:
for role, need in opp.resource_needs.items():
demand[role] += need * opp.probability
return demand
def identify_gaps(self, horizon_months: int = 12) -> List[CapacityGap]:
"""Identify capacity gaps over forecast horizon."""
gaps = []
for month in range(horizon_months):
period_start = datetime.now() + timedelta(days=month * 30)
period_end = period_start + timedelta(days=30)
capacity = self.get_capacity_at_date(period_start)
demand = self.calculate_demand(period_start, include_pipeline=True)
for role in ResourceRole:
cap = capacity.get(role, 0)
dem = demand.get(role, 0)
gap = cap - dem
if gap < 0:
severity = "critical" if gap < -1 else "warning"
gaps.append(CapacityGap(
role=role,
period_start=period_start,
period_end=period_end,
demand=dem,
capacity=cap,
gap=gap,
severity=severity
))
return gaps
def can_pursue_project(self, value: float, start_date: datetime,
duration_months: int) -> Dict:
"""Evaluate if organization can pursue new project."""
# Calculate needs for potential project
needs = self._calculate_resource_needs(value, ProjectPhase.CONSTRUCTION)
end_date = start_date + timedelta(days=duration_months * 30)
# Check capacity over project duration
can_staff = True
bottlenecks = []
current_date = start_date
while current_date <= end_date:
capacity = self.get_capacity_at_date(current_date)
demand = self.calculate_demand(current_date)
for role, need in needs.items():
available = capacity.get(role, 0) - demand.get(role, 0)
if need > available:
can_staff = False
bottlenecks.append({
"date": current_date,
"role": role.value,
"needed": need,
"available": available,
"gap": need - available
})
current_date += timedelta(days=30)
# Determine recommendation
if can_staff:
recommendation = "GO - Sufficient capacity"
elif len(bottlenecks) <= 2:
recommendation = "CONDITIONAL - Minor gaps, consider hiring"
else:
recommendation = "CAUTION - Significant capacity constraints"
return {
"can_staff": can_staff,
"recommendation": recommendation,
"resource_needs": {r.value: v for r, v in needs.items()},
"bottlenecks": bottlenecks[:10],
"actions_required": self._suggest_hiring(bottlenecks)
}
def _suggest_hiring(self, bottlenecks: List[Dict]) -> List[str]:
"""Suggest hiring actions based on gaps."""
if not bottlenecks:
return []
# Aggregate gaps by role
role_gaps = {}
for b in bottlenecks:
role = b['role']
if role not in role_gaps:
role_gaps[role] = 0
role_gaps[role] = max(role_gaps[role], b['gap'])
actions = []
for role, gap in sorted(role_gaps.items(), key=lambda x: -x[1]):
hires = int(gap) + 1
actions.append(f"Hire {hires} {role}(s) - Gap: {gap:.1f} FTE")
return actions
def generate_forecast(self, horizon_months: int = 12) -> CapacityForecast:
"""Generate capacity forecast."""
gaps = self.identify_gaps(horizon_months)
# Calculate totals
capacity = self.get_current_capacity()
demand = self.calculate_demand(datetime.now())
total_capacity = sum(capacity.values())
total_demand = sum(demand.values())
utilization = (total_demand / total_capacity * 100) if total_capacity > 0 else 0
# Generate recommendations
recommendations = []
if utilization > 90:
recommendations.append("High utilization - consider hiring")
elif utilization < 60:
recommendations.append("Low utilization - review project pipeline")
# Role-specific recommendations
critical_gaps = [g for g in gaps if g.severity == "critical"]
gap_roles = set(g.role.value for g in critical_gaps)
for role in gap_roles:
recommendations.append(f"Critical gap in {role} - immediate action needed")
return CapacityForecast(
forecast_date=datetime.now(),
horizon_months=horizon_months,
total_demand_fte=total_demand,
total_capacity_fte=total_capacity,
utilization_pct=utilization,
gaps=gaps,
recommendations=recommendations
)
def generate_report(self) -> str:
"""Generate capacity planning report."""
forecast = self.generate_forecast()
lines = [
"# Capacity Planning Report",
"",
f"**Organization:** {self.organization_name}",
f"**Report Date:** {forecast.forecast_date.strftime('%Y-%m-%d')}",
"",
"## Executive Summary",
"",
f"| Metric | Value |",
f"|--------|-------|",
f"| Active Projects | {len(self.projects)} |",
f"| Pipeline Opportunities | {len(self.pipeline)} |",
f"| Total Staff | {len(self.staff)} |",
f"| Current Capacity (FTE) | {forecast.total_capacity_fte:.1f} |",
f"| Current Demand (FTE) | {forecast.total_demand_fte:.1f} |",
f"| Utilization | {forecast.utilization_pct:.0f}% |",
"",
"## Capacity by Role",
"",
"| Role | Capacity | Demand | Gap |",
"|------|----------|--------|-----|"
]
capacity = self.get_current_capacity()
demand = self.calculate_demand(datetime.now())
for role in ResourceRole:
cap = capacity.get(role, 0)
dem = demand.get(role, 0)
gap = cap - dem
gap_icon = "✅" if gap >= 0 else "⚠️" if gap > -1 else "🔴"
lines.append(
f"| {role.value} | {cap:.1f} | {dem:.1f} | {gap:+.1f} {gap_icon} |"
)
# Active projects
lines.extend([
"",
"## Active Projects",
"",
"| Project | Value | Phase | End Date |",
"|---------|-------|-------|----------|"
])
for p in sorted(self.projects.values(), key=lambda x: x.value, reverse=True):
lines.append(
f"| {p.project_name} | ${p.value:,.0f} | {p.phase.value} | "
f"{p.end_date.strftime('%Y-%m-%d')} |"
)
# Pipeline
if self.pipeline:
lines.extend([
"",
"## Pipeline",
"",
"| Opportunity | Value | Probability | Expected Start |",
"|-------------|-------|-------------|----------------|"
])
for p in sorted(self.pipeline.values(), key=lambda x: -x.probability):
lines.append(
f"| {p.project_name} | ${p.value:,.0f} | {p.probability:.0%} | "
f"{p.start_date.strftime('%Y-%m-%d')} |"
)
# Gaps
critical_gaps = [g for g in forecast.gaps if g.severity == "critical"]
if critical_gaps:
lines.extend([
"",
f"## Critical Capacity Gaps ({len(critical_gaps)})",
"",
"| Role | Period | Gap |",
"|------|--------|-----|"
])
for gap in critical_gaps[:10]:
lines.append(
f"| {gap.role.value} | {gap.period_start.strftime('%Y-%m')} | "
f"{gap.gap:.1f} FTE |"
)
# Recommendations
if forecast.recommendations:
lines.extend([
"",
"## Recommendations",
""
])
for rec in forecast.recommendations:
lines.append(f"- {rec}")
return "\n".join(lines)
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 9d ago First seen · 559 lines · 29 tokens per session scan A a9309cfc58c8
capacity-planning is a skill published in the GitHub repository datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction (312 stars, last pushed 21d ago), licensed MIT. It adds 29 tokens to every session and 4,496 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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