To run this task you must have the following required information:

> CSV file with date/period column and metric to forecast (at least 4 periods)

If you don't have all of this information, exit here and respond asking for any extra information you require, and instructions to run this task again with ALL required information.

---

You MUST use a todo list to complete these steps in order. Never move on to one step if you haven't completed the previous step. If you have multiple read steps in a row, read them all at once (in parallel).

Add all steps to your todo list now and begin executing.

## Steps

1. If the CSV path was not already provided, ask the user for it.
Common sources: Historical performance data, finance exports, operational metrics.

Establish for the subtask:
- Output path: `./documents/tmp/executive-data.json`
- Column types to detect: dates/periods, numeric metrics to forecast


2. [Gather Requirements for Parse and Interpret CSV] The next step has the following requirements: "CSV file path to parse. Column type hints (e.g., "scores, customers, dates, categories"). Output file path for the interpreted data.". Search the user's data for this information or ask them directly if needed. Do not proceed until you have this information.

3. [Execute Parse and Interpret CSV Task]: Spawn a subagent and provide it with the requirements gathered above and instructions to read `./skills/sauna/[skill_id]/references/recipes/stdlib.csv.interpret.md` for its task list

4. [Read Parsed Executive Data]: Read the file at `./documents/tmp/executive-data.json` and analyze its contents (Load the parsed and interpreted CSV data)

5. [Read Executive Analytics Guide]: Read the documentation in: `./skills/sauna/[skill_id]/references/executive.analytics.guide.md` (Forecasting analysis output format)

6. Before forecasting, validate the data:

1. Identify the date column and the metric to forecast
2. If multiple metrics exist, ask which one to forecast
3. Verify at least 4 data points exist (required for projection)
4. Check for seasonality or obvious patterns
5. Note any outliers that might skew the projection

If data is insufficient, explain what's needed and stop.


7. Create the forecast:

1. Calculate growth rate between each period
2. Determine average growth rate and trend direction
3. Build three scenarios:
   - Conservative: Lower bound of growth rate range
   - Base case: Average growth rate
   - Optimistic: Upper bound of growth rate range
4. Project the metric for the next period (quarter/month)

Present results following the Forecasting Analysis template.
Be explicit about methodology and assumptions.


8. Add confidence factors and caveats:

- What factors support this forecast?
- What risks could invalidate it?
- What would you need to increase confidence?

Remind the user this is a simple projection, not a sophisticated model.
Recommend professional forecasting for high-stakes decisions.