revenue-forecaster

name	revenue-forecaster
version	1.0.0
tier	automation
description	Weekly revenue / sales forecasting for small businesses with multiple locations or product lines. Blends recent trend + seasonal baseline + YoY growth with per-entity holiday multipliers and week-of-month adjustments. Ships autoresearch-compatible eval and parameters so you can tune it on your own historical data.
requires	{"bins":["python3"],"skills":[]}

Revenue Forecaster

Predict next week's revenue. Project 13 weeks of cash. Stress-test scenarios. Tune it yourself with autoresearch.

Most SMB forecasting is either a gut number or a wild spreadsheet. This skill gives your agent a real parametric forecast model that handles the things that actually matter for small businesses:

• Multiple locations or product lines with independent per-entity parameters (each location has its own holiday patterns, growth rate, and week-of-month quirks)
• Holiday multipliers (Thanksgiving, Christmas, New Year's, MLK, July 4th, etc.)
• Week-of-month patterns (some businesses spike on pay weeks; some dip early in the month)
• Outlier trimming so one anomalous holiday doesn't inflate the recent average
• Exponential decay on recent weeks for businesses with fast-changing volume
• YoY growth blending when you have enough history
• Graceful fallback when you don't — the same model runs with or without YoY data
• Autoresearch integration — the skill ships with a fixed eval script and a parameters file so you can autoresearch your way to lower MAPE on your own data

Triggers

• "forecast revenue", "predict sales", "how much will we make"
• "weekly forecast", "this week's forecast"
• "13 week forecast", "13-week cash projection", "cash runway"
• "daily forecast", "how much on Friday"
• "stress test", "what if sales drop"
• "tune the forecast", "improve forecast accuracy"

Prerequisites

• Historical revenue data in weekly buckets per entity. Minimum usable: 8 weeks. Good: 6+ months. Great: 1+ year (unlocks YoY blending).
• Python 3.9+ with the standard library — no pandas/numpy required.
• A way to feed the data to the scripts. The built-in adapter reads CSV; see references/data-adapters.md for Google Sheets, BigQuery, and Square API patterns.

Quick Start

1. Bring your data — Export a CSV with three columns:

   week_start,entity,revenue
   2025-10-06,Main Street,48350.00
   2025-10-06,Downtown,32110.00
   2025-10-13,Main Street,51200.00
   ...
   

   week_start must be the Monday of the week (ISO week start). entity is your location, shop, product line, or any other unit you want forecasted independently. revenue is that week's total.

2. Copy the starter parameters — cp templates/parameters.json config/parameters.json and edit it if you already know things about your business (e.g., Shop A has a Christmas spike of 2×).

3. Run a forecast:

   python3 scripts/forecast.py --data data/history.csv --params config/parameters.json
   

   Output is JSON to stdout with one entry per entity: point estimate, confidence range, component breakdown, and the methodology note so you can explain the number.

4. Pair with autoresearch (optional but recommended) — The scripts ship with scripts/eval.py, which is a backtesting evaluator that the autoresearch skill can drive to tune your parameters. The parameters that ship with this skill are sensible defaults, not optimized to your business — autoresearch is how you close that gap.

Workflow

Phase 1: Get Your Data In

The scripts expect a single CSV with one row per entity per week. The built-in reader in scripts/forecast.py handles CSV by default. For other sources:

• Google Sheets — Use the google-sheets skill to pull a range, pipe it as CSV into forecast.py.
• BigQuery — Use bq query --format csv and redirect into the script.
• Square POS — Use the Square Connect API (GET /v2/payouts) to dump weekly payout history to a CSV.
• Custom — Any data source that can produce rows of (week_start, entity, revenue) works.

See references/data-adapters.md for copy-pasteable patterns for each of the above.

Phase 2: Pick a Forecast Mode

The same forecast.py entry point supports multiple modes via --mode:

Mode	What it produces	When to use
weekly (default)	Point estimate + range + component breakdown for the current week, per entity	Weekly cash planning, AP run inputs
thirteen-week	Rolling 13-week cash projection with optional recurring obligations	Runway questions, big expense planning, approaching seasonal dips
daily	Weekly forecast distributed across days using day-of-week multipliers from recent history	Daily cash flow, "will deposits cover Friday payroll?"
stress	Applies a configurable shock (e.g., -30% for 3 weeks) on top of the weekly forecast	"What if sales drop?", summer slump prep, vendor payment planning

Examples:

# Weekly forecast (default)
python3 scripts/forecast.py --data data/history.csv

# 13-week rolling projection, including recurring obligations
python3 scripts/forecast.py --data data/history.csv --mode thirteen-week \
  --obligations config/recurring_obligations.json

# Daily breakdown for the current week
python3 scripts/forecast.py --data data/history.csv --mode daily

# Stress test: 30% drop for 3 weeks starting now
python3 scripts/forecast.py --data data/history.csv --mode stress \
  --shock -0.30 --shock-weeks 3

Phase 3: Tune It With Autoresearch

The defaults in templates/parameters.json are reasonable starting points — they are not tuned to your business. The whole point of shipping this with an eval script is so you can find the right parameters for your data.

1. Scaffold an autoresearch session — follow the autoresearch skill workflow. Point it at:

   • research.md → copy templates/research.md as your research file; edit the domain-knowledge section with what you know about your business (e.g., "Labor Day is dead for us", "Dec 24 is our biggest day").
   • eval script → scripts/eval.py (already read-only-safe — it never modifies parameters)
   • parameters file → config/parameters.json
   • experiments/ → a new directory the agent creates
   • archive.json, coverage.json → the agent creates these on first run

2. Run the loop. The autoresearch skill handles the three-agent pipeline (researcher, critic, meta-reviewer). Start with 10–20 experiments.

3. Watch the meta-reviewer. The first big wins usually come from structural changes the researcher wouldn't find on its own: adding a holiday multiplier you didn't know existed, enabling week-of-month adjustments, tightening the outlier threshold. The meta-reviewer is the mechanism that surfaces these.

See references/methodology.md for a deep dive on each parameter and what it controls.

Config Schema (parameters.json)

The parameters file has two sections: global defaults and per-entity overrides. Every field in the global section can be overridden per entity.

{
  "blending": {
    "recent_weight": 0.82,
    "seasonal_weight": 0.16,
    "yoy_weight":    0.02
  },
  "blending_no_yoy": {
    "recent_weight":   1.00,
    "seasonal_weight": 0.00
  },

  "lookback": {
    "recent_weeks":       4,
    "variability_weeks":  12,
    "seasonal_min_weeks": 3
  },

  "exponential_weighting": {
    "decay_factor": 1.0
  },

  "outlier_handling": {
    "trim_holiday_weeks":   true,
    "holiday_threshold_pct": 1.30
  },

  "week_of_month": {
    "enabled":     false,
    "week_1_adj":  0.0,
    "week_2_adj":  0.0,
    "week_3_adj":  0.0,
    "week_4_adj":  0.0
  },

  "holidays": {
    "enabled":             true,
    "thanksgiving_mult":   1.0,
    "christmas_mult":      1.0,
    "new_years_mult":      1.0,
    "mlk_week_mult":       1.0,
    "early_jan_mult":      1.0,
    "late_jan_mult":       1.0,
    "memorial_day_mult":   1.0,
    "july_4th_mult":       1.0,
    "labor_day_mult":      1.0,
    "easter_passover_mult": 1.0
  },

  "growth_adjustment": {
    "n_months_avg": 6
  },

  "data_filters": {
    "global": {
      "min_weekly_revenue": 0,
      "data_start_date":    null
    }
  },

  "per_entity": {
    "Main Street": {
      "holidays": {
        "christmas_mult":   2.05,
        "thanksgiving_mult": 1.94
      },
      "week_of_month": {
        "enabled":    true,
        "week_3_adj": 0.075
      }
    },
    "Downtown": {
      "exponential_weighting": { "decay_factor": 0.95 },
      "data_filters": {
        "data_start_date": "2024-09-02"
      }
    }
  }
}

The full schema with field-by-field documentation is in references/methodology.md. The starter file at templates/parameters.json has all the knobs at neutral defaults so the model will run out of the box — autoresearch is what moves them away from neutral.

Output Format

forecast.py --mode weekly emits JSON like this (one entry per entity):

{
  "forecast_date": "2026-04-07",
  "forecast_week": "2026-04-06",
  "entities": {
    "Main Street": {
      "point_estimate": 48350.00,
      "confidence_range": { "low": 45200.00, "high": 51500.00 },
      "components": {
        "recent_4wk_avg":     48100.00,
        "seasonal_avg":       46800.00,
        "yoy_growth_rate":    0.042,
        "yoy_adjusted":       48765.60,
        "week_of_month":      1,
        "week_of_month_adj": -0.05
      },
      "methodology": {
        "weights": "82% recent + 16% seasonal + 2% YoY (6mo growth: 4.2%) | week 1 adj: -5.0%",
        "model_version": "1.0.0"
      }
    }
  }
}

The structure is deliberately verbose — the components and methodology blocks are so the agent can explain the forecast, not just report it. When the business owner asks "why is this number low?", the answer should be "week 1 of the month typically runs 5% under the 4-week average at Main Street," not "the model says so."

Integration With Other Skills

• autoresearch — Tune parameters on your own historical data. This is the intended pairing.
• morning-briefing — Include this week's forecast in the daily digest.
• google-sheets — Read historical data from a sheet, pipe into forecast.py.
• google-ads — Compare forecasted revenue to ad spend for a rough CAC/ROAS view.
• semantic-layer-audit — Document which data source you're feeding to the forecaster and why.

Tips

• Start with default parameters. They'll produce a forecast that's usually within 10–20% of actuals. Autoresearch is what gets you under 5%.
• Enable YoY only when you have 18+ months of data. With less history, a YoY term pulls from a tiny sample and adds variance without improving accuracy.
• Per-entity overrides compound. A global 1.0× Christmas multiplier with a per-entity 2.05× override means that entity gets 2.05×. The per-entity value wins.
• Holiday multipliers should be learned, not guessed. Your guess is usually within ±20%; autoresearch can usually get within ±2%.
• The eval script is sacred. Don't edit scripts/eval.py during an autoresearch session — that's the thing keeping the researcher honest. If the eval is wrong, fix it once and then freeze it again.
• Retune quarterly. Your business changes. Parameters should change with it. A 15-minute autoresearch session every quarter is usually enough to keep MAPE stable.

Credits

The forecasting methodology in this skill — blended recent/seasonal/YoY with per-entity holiday multipliers and week-of-month adjustments — was developed at Prospect Butcher Co and tuned on real multi-location sales history through many autoresearch iterations, taking forecast error from double digits down to low single-digit MAPE. The generalizations in this public version (pluggable data adapters, entity-agnostic naming, parameter files decoupled from any specific POS) are a cleanup of that work into something any SMB can point at its own data.

If you extend this skill with a new data adapter, a new forecast mode, or a genuinely new parameter that moves accuracy on real data, PRs welcome.

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Forecasting is the bridge between "what happened" and "what should we do next." Make the bridge a little less wobbly.