By name: sales-cowork-workflows description: Turn recurring sales leadership routines (meeting prep, weekly forecast rollups, and large-scale account scoring) into scheduled, repeatable workflows that assemble data from multiple systems and produce standardized outputs with human approval.
Sales Cowork Workflows
Instructions
Use this skill to translate three recurring patterns into concrete, repeatable routines:
- Scheduled meeting prep
- Before each external meeting, ensure logistics are complete (for example, book a conference room if one isn’t set).
- Build a concise customer brief by pulling the latest relevant data (example sources mentioned in the post: BigQuery spend, Salesforce pipeline status).
- Output a brief that the user can quickly review before the call.
- Weekly forecast rollup
- Pull opportunity records and submitted commits from Salesforce’s Forecast view.
- Pull token spend from BigQuery.
- Pull qualitative notes from a small set of internal documents.
- Assemble a single-page web report in a consistent format that includes:
- top-line metrics
- top deals
- movers and decliners
- a forecast snapshot rolled up from each first-line manager
- Publish the report to a shareable link before the weekly forecast call, leaving room for the owner to add commentary.
- Overnight account propensity scoring (at scale)
- Define a scoring rubric (the post describes two separate rubrics: one for tech accounts and one for industries).
- For each account, gather evidence via deep web research and internal data sources (examples mentioned: Salesforce and BigQuery).
- Produce:
- a numerical score
- written rationale per rubric dimension
- Compile results into an interactive dashboard where each AE can view their slice of the territory and see ranked accounts; hovering an account should surface suggested use cases and comparable case studies (as described in the post).
Iteration loop
Run a test territory first, review quality, then adjust weights and re-run. The post gives an example instruction for weight tuning: “I think D4 is probably weighted a little heavy; bring it down a bit”.
Examples
Example: rubric dimensions (from the post)
Tech accounts
- agent opportunity
- internal transformation
- AI commitment
- white space against existing spend
- industry fit
Industries (examples)
- knowledge-worker density
- public AI commitments measured by mentions on the company’s open jobs page
Example: weekly report outline
- Top-line metrics
- Top deals
- Movers and decliners
- Forecast snapshot by first-line manager