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.
npx skills add datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction --skill cashflow-forecastergit clone --depth 1 https://github.com/datadrivenconstruction/DDC_Skills_for_AI_Agents_in_ConstructionWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/datadrivenconstruction/ddc_skills_for_ai_agents_in_construction/cashflow-forecaster)<a href="https://agentmods.dev/skills/datadrivenconstruction/ddc_skills_for_ai_agents_in_construction/cashflow-forecaster"><img src="https://agentmods.dev/badge/skills/datadrivenconstruction/ddc_skills_for_ai_agents_in_construction/cashflow-forecaster/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/cashflow-forecaster"><img src="https://agentmods.dev/badge/skills/datadrivenconstruction/ddc_skills_for_ai_agents_in_construction/cashflow-forecaster.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00030 | $0.03576 |
| Opus 5 | $0.00015 | $0.01788 |
| Sonnet 5 | $0.00006 | $0.00715 |
| Haiku 4.5 | $0.00003 | $0.00358 |
Grade A, and why
cashflow-forecaster 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 8d 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:
- cashflow-forecaster — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 460 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Cashflow Forecaster
Overview
Forecast construction project cash flow based on schedule, billing cycles, and payment terms. Identify potential cash shortfalls, optimize payment timing, and support project financing decisions.
Cash Flow Curve
┌─────────────────────────────────────────────────────────────────┐
│ CONSTRUCTION CASH FLOW │
├─────────────────────────────────────────────────────────────────┤
│ │
│ $ Income (payments received) │
│ │ ╱──────────╲ │
│ │ ╱ ╲ Positive cash │
│ │ ╱ ╲ position │
│ │ ╱ ╲ │
│ │ ╱ Cash Gap ╲ │
│ ├───────────────────────────────────────────────────── │
│ │╲ │
│ │ ╲ Expenses (costs incurred) │
│ │ ╲──────────╱ │
│ │ │
│ └────────────────────────────────────────────────────────── │
│ Time → │
│ │
└─────────────────────────────────────────────────────────────────┘
Technical Implementation
from dataclasses import dataclass, field
from typing import List, Dict, Optional, Tuple
from datetime import datetime, timedelta
from enum import Enum
import math
class CostCategory(Enum):
LABOR = "labor"
MATERIALS = "materials"
EQUIPMENT = "equipment"
SUBCONTRACTOR = "subcontractor"
GENERAL_CONDITIONS = "general_conditions"
OVERHEAD = "overhead"
OTHER = "other"
class PaymentTerms(Enum):
NET_30 = 30
NET_45 = 45
NET_60 = 60
NET_90 = 90
@dataclass
class CostItem:
id: str
description: str
category: CostCategory
amount: float
scheduled_date: datetime
payment_terms_days: int = 30
paid: bool = False
paid_date: Optional[datetime] = None
@dataclass
class IncomeItem:
id: str
description: str
amount: float
billing_date: datetime
expected_payment_date: datetime
received: bool = False
received_date: Optional[datetime] = None
received_amount: float = 0.0
@dataclass
class CashFlowPeriod:
period_start: datetime
period_end: datetime
opening_balance: float
income: float
expenses: float
net_cashflow: float
closing_balance: float
cumulative_income: float
cumulative_expenses: float
@dataclass
class CashFlowForecast:
project_name: str
forecast_date: datetime
total_contract: float
total_costs: float
periods: List[CashFlowPeriod]
peak_deficit: float
peak_deficit_date: datetime
breakeven_date: Optional[datetime]
financing_required: float
class CashFlowForecaster:
"""Forecast construction project cash flow."""
# Typical cost distribution curve (S-curve)
S_CURVE = [0.05, 0.10, 0.15, 0.20, 0.20, 0.15, 0.10, 0.05]
def __init__(self, project_name: str, contract_value: float,
estimated_cost: float, start_date: datetime,
duration_months: int):
self.project_name = project_name
self.contract_value = contract_value
self.estimated_cost = estimated_cost
self.start_date = start_date
self.duration_months = duration_months
self.end_date = start_date + timedelta(days=duration_months * 30)
self.cost_items: List[CostItem] = []
self.income_items: List[IncomeItem] = []
self.retainage_rate = 0.10 # 10%
self.payment_terms_income = PaymentTerms.NET_30
self.billing_frequency = 30 # Monthly
def set_payment_terms(self, income_terms: PaymentTerms,
retainage_rate: float = 0.10):
"""Set payment terms for income."""
self.payment_terms_income = income_terms
self.retainage_rate = retainage_rate
def add_cost_item(self, description: str, category: CostCategory,
amount: float, scheduled_date: datetime,
payment_terms_days: int = 30) -> CostItem:
"""Add cost item to forecast."""
item = CostItem(
id=f"COST-{len(self.cost_items)+1:04d}",
description=description,
category=category,
amount=amount,
scheduled_date=scheduled_date,
payment_terms_days=payment_terms_days
)
self.cost_items.append(item)
return item
def generate_cost_distribution(self, cost_breakdown: Dict[CostCategory, float] = None):
"""Generate cost items based on S-curve distribution."""
if cost_breakdown is None:
# Default breakdown
cost_breakdown = {
CostCategory.LABOR: self.estimated_cost * 0.35,
CostCategory.MATERIALS: self.estimated_cost * 0.30,
CostCategory.SUBCONTRACTOR: self.estimated_cost * 0.20,
CostCategory.EQUIPMENT: self.estimated_cost * 0.05,
CostCategory.GENERAL_CONDITIONS: self.estimated_cost * 0.07,
CostCategory.OVERHEAD: self.estimated_cost * 0.03,
}
# Distribute costs over project duration using S-curve
months = self.duration_months
curve_months = len(self.S_CURVE)
for category, total in cost_breakdown.items():
for month in range(months):
# Map to S-curve
curve_idx = int(month / months * curve_months)
curve_idx = min(curve_idx, curve_months - 1)
monthly_pct = self.S_CURVE[curve_idx]
# Adjust for number of months
adjustment = months / curve_months
amount = total * monthly_pct / adjustment
cost_date = self.start_date + timedelta(days=month * 30)
# Payment terms vary by category
payment_days = 30
if category == CostCategory.SUBCONTRACTOR:
payment_days = 45
elif category == CostCategory.MATERIALS:
payment_days = 30
self.add_cost_item(
f"{category.value} - Month {month+1}",
category,
amount,
cost_date,
payment_days
)
def generate_billing_schedule(self):
"""Generate income items based on billing schedule."""
# Monthly billing based on progress
months = self.duration_months
for month in range(months):
# Map to S-curve for progress
curve_months = len(self.S_CURVE)
curve_idx = int(month / months * curve_months)
curve_idx = min(curve_idx, curve_months - 1)
monthly_pct = self.S_CURVE[curve_idx]
# Adjust for number of months
adjustment = months / curve_months
billing_amount = self.contract_value * monthly_pct / adjustment
# Apply retainage
retainage = billing_amount * self.retainage_rate
net_billing = billing_amount - retainage
billing_date = self.start_date + timedelta(days=(month + 1) * 30)
payment_date = billing_date + timedelta(days=self.payment_terms_income.value)
self.income_items.append(IncomeItem(
id=f"INC-{month+1:04d}",
description=f"Progress Billing #{month+1}",
amount=net_billing,
billing_date=billing_date,
expected_payment_date=payment_date
))
# Retainage release at end
total_retainage = self.contract_value * self.retainage_rate
final_date = self.end_date + timedelta(days=30)
self.income_items.append(IncomeItem(
id="INC-RET",
description="Retainage Release",
amount=total_retainage,
billing_date=final_date,
expected_payment_date=final_date + timedelta(days=self.payment_terms_income.value)
))
def generate_forecast(self, period_days: int = 30,
opening_balance: float = 0) -> CashFlowForecast:
"""Generate cash flow forecast."""
if not self.cost_items:
self.generate_cost_distribution()
if not self.income_items:
self.generate_billing_schedule()
periods = []
current_date = self.start_date
balance = opening_balance
cumulative_income = 0
cumulative_expenses = 0
peak_deficit = 0
peak_deficit_date = current_date
breakeven_date = None
# Extend forecast beyond project end
forecast_end = self.end_date + timedelta(days=90)
while current_date < forecast_end:
period_end = current_date + timedelta(days=period_days)
# Calculate expenses for period (when paid, not when incurred)
period_expenses = sum(
c.amount for c in self.cost_items
if current_date <= c.scheduled_date + timedelta(days=c.payment_terms_days) < period_end
)
# Calculate income for period (when received)
period_income = sum(
i.amount for i in self.income_items
if current_date <= i.expected_payment_date < period_end
)
net_cashflow = period_income - period_expenses
closing_balance = balance + net_cashflow
cumulative_income += period_income
cumulative_expenses += period_expenses
period = CashFlowPeriod(
period_start=current_date,
period_end=period_end,
opening_balance=balance,
income=period_income,
expenses=period_expenses,
net_cashflow=net_cashflow,
closing_balance=closing_balance,
cumulative_income=cumulative_income,
cumulative_expenses=cumulative_expenses
)
periods.append(period)
# Track peak deficit
if closing_balance < peak_deficit:
peak_deficit = closing_balance
peak_deficit_date = current_date
# Track breakeven
if breakeven_date is None and closing_balance > 0 and balance <= 0:
breakeven_date = current_date
balance = closing_balance
current_date = period_end
financing_required = abs(peak_deficit) if peak_deficit < 0 else 0
return CashFlowForecast(
project_name=self.project_name,
forecast_date=datetime.now(),
total_contract=self.contract_value,
total_costs=self.estimated_cost,
periods=periods,
peak_deficit=peak_deficit,
peak_deficit_date=peak_deficit_date,
breakeven_date=breakeven_date,
financing_required=financing_required
)
def analyze_scenarios(self) -> Dict[str, CashFlowForecast]:
"""Analyze different payment scenarios."""
scenarios = {}
# Base case
scenarios["base"] = self.generate_forecast()
# Optimistic - faster payments
original_terms = self.payment_terms_income
self.payment_terms_income = PaymentTerms.NET_30
scenarios["optimistic"] = self.generate_forecast()
# Pessimistic - slower payments
self.payment_terms_income = PaymentTerms.NET_60
scenarios["pessimistic"] = self.generate_forecast()
self.payment_terms_income = original_terms
return scenarios
def calculate_financing_cost(self, forecast: CashFlowForecast,
annual_rate: float = 0.08) -> Dict:
"""Calculate cost of financing the cash deficit."""
if forecast.financing_required == 0:
return {"financing_needed": False, "cost": 0}
# Calculate weighted average deficit duration
total_deficit_days = 0
weighted_deficit = 0
for period in forecast.periods:
if period.closing_balance < 0:
deficit = abs(period.closing_balance)
days = (period.period_end - period.period_start).days
total_deficit_days += days
weighted_deficit += deficit * days
avg_deficit = weighted_deficit / total_deficit_days if total_deficit_days else 0
# Calculate interest cost
daily_rate = annual_rate / 365
interest_cost = weighted_deficit * daily_rate
return {
"financing_needed": True,
"peak_deficit": forecast.peak_deficit,
"deficit_days": total_deficit_days,
"average_deficit": avg_deficit,
"annual_rate": annual_rate,
"estimated_interest": interest_cost,
"recommendation": f"Line of credit needed: ${forecast.financing_required:,.0f}"
}
def generate_report(self, forecast: CashFlowForecast) -> str:
"""Generate cash flow forecast report."""
lines = [
"# Cash Flow Forecast Report",
"",
f"**Project:** {forecast.project_name}",
f"**Forecast Date:** {forecast.forecast_date.strftime('%Y-%m-%d')}",
"",
"## Summary",
"",
f"| Metric | Value |",
f"|--------|-------|",
f"| Contract Value | ${forecast.total_contract:,.0f} |",
f"| Estimated Cost | ${forecast.total_costs:,.0f} |",
f"| Gross Margin | ${forecast.total_contract - forecast.total_costs:,.0f} ({(forecast.total_contract - forecast.total_costs)/forecast.total_contract*100:.1f}%) |",
f"| Peak Cash Deficit | ${forecast.peak_deficit:,.0f} |",
f"| Peak Deficit Date | {forecast.peak_deficit_date.strftime('%Y-%m-%d')} |",
f"| Financing Required | ${forecast.financing_required:,.0f} |",
"",
"## Monthly Cash Flow",
"",
"| Period | Income | Expenses | Net | Balance |",
"|--------|--------|----------|-----|---------|"
]
for period in forecast.periods:
if period.income > 0 or period.expenses > 0:
lines.append(
f"| {period.period_start.strftime('%Y-%m')} | "
f"${period.income:,.0f} | ${period.expenses:,.0f} | "
f"${period.net_cashflow:,.0f} | ${period.closing_balance:,.0f} |"
)
# Financing analysis
financing = self.calculate_financing_cost(forecast)
if financing["financing_needed"]:
lines.extend([
"",
"## Financing Analysis",
"",
f"- Peak Deficit: ${financing['peak_deficit']:,.0f}",
f"- Days in Deficit: {financing['deficit_days']}",
f"- Estimated Interest Cost: ${financing['estimated_interest']:,.0f}",
f"- **{financing['recommendation']}**"
])
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.
- 8d ago First seen · 460 lines · 30 tokens per session scan A d878cf9b6a1d
cashflow-forecaster is a skill published in the GitHub repository datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction (308 stars, last pushed 20d ago), licensed MIT. It adds 30 tokens to every session and 3,576 once invoked, about $0.0002 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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