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

> CSV file with pipeline/deal data including a stage or status column

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.

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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: Salesforce, HubSpot, Pipedrive, or any CRM export.

Establish for the subtask:
- Output path: `./documents/tmp/sales-data.json`
- Column types to detect: stages/status, amounts, dates, reps/owners


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 Sales Data]: Read the file at `./documents/tmp/sales-data.json` and analyze its contents (Load the parsed and interpreted CSV data)

5. [Read Sales Analytics Guide]: Read the documentation in: `./skills/sauna/[skill_id]/references/sales.analytics.guide.md` (Pipeline conversion output format and stage progression assumptions)

6. Before calculating conversion rates, validate stage data integrity:

1. Check if the data has multiple rows per deal (deal history) or single snapshot:
   - If deal ID column exists with multiple rows per deal: check stage progression
   - If single row per deal: note this is a snapshot, cannot verify progression paths

2. For deal history data, flag:
   - Backward progression (later stage → earlier stage) with specific deal IDs
   - Skipped stages (deal jumped multiple stages) with specific deal IDs

3. Review the validation results from the CSV interpretation step:
   - Note any data quality warnings in your limitations section
   - If errors were flagged, confirm with user before proceeding

Add findings to your analysis limitations section. Proceed with analysis.


7. Analyze the pipeline data for stage-by-stage conversion:

1. Identify the stage column from the interpreted columns
2. If multiple stage columns exist, ask the user which to use
3. Count deals at each stage
4. Calculate conversion rates between sequential stages
5. Identify the biggest drop-off point
6. Look for patterns (e.g., deals stuck at certain stages)

Present results following the Pipeline Conversion Analysis template.
Include the actual data tables with numbers, not just summaries.


8. Provide 2-3 actionable recommendations based on the conversion data.
Focus on the biggest opportunity (usually the worst conversion point).