capacity-planning

capacity-planning is a skill for Claude Code, Codex from datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction. It costs 29 tokens per session (4,496 once invoked), scanned A, original, MIT.

A planning tool for forecasting how many people and other resources a construction organization will need. It compares current projects and likely future work with available staff and capacity.

In plain words
What is it for?
Use it to forecast resource demand, find capacity gaps, plan staffing, and support go/no-go decisions on new project bids.
Why use it?
It helps identify staffing shortages before accepting new work. This supports decisions about hiring, workload limits, and whether to pursue a project.

Skill for Claude CodeCodex

Which agent this was written for is unclear — built for openclaw. Also seen: built for openclaw.

Good fit Use it to forecast resource demand, find capacity gaps, plan staffing, and support go/no-go decisions on new project bids.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/datadrivenconstruction/ddc_skills_for_ai_agents_in_construction/capacity-planning
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction --skill capacity-planning
Clone the repo
git clone --depth 1 https://github.com/datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction

Made for: Claude Code, Codex.

Wrote this? Show the measurements

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agentmods badge for capacity-planning

README.md
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<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>
Per session 29 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 4,496 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce 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

Measured 9d ago against content hash a9309cfc58c8, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

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.

Origin

Copies of this mod

1 near-identical copy found in the catalogue:

5_DDC_Innovative/capacity-planning/SKILL.md · 559 lines

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)

Read the full file on GitHub · 559 lines

Files

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.

Changes

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.

  1. 9d ago First seen · 559 lines · 29 tokens per session scan A a9309cfc58c8

Subscribe to this mod's changes

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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