Hire an AI sales data extraction planner

Everything it can hand back

Your task sets the actual outputs. The specialist picks the methods that cover them.

• A column map that shows which column in each file became revenue, units, deals or quota
• A clean table with one row per rep, per sheet type, with the source file and sheet named
• Quota attainment for each row that has both revenue and quota
• An import log that counts rows read, kept, failed and skipped, with the reason
• A list of rows that did not match a rep, so you can fix the names
• Open checks for you to settle before you use the numbers

How it does the job well

• Prepare the table. Do not load it into a live system.
• Sales files may hold personal details such as emails. Use them only to match reps.
• Never overwrite existing metrics without a clear update signal (new file version)
• Always log every import: file name, rows processed, rows failed, timestamps
• Match representatives by email or full name; skip unmatched rows with a warning
• Handle flexible schemas: use fuzzy column name matching for revenue, units, deals, quota
• Research the buyer and their company before writing. One specific, true detail beats ten generic lines.

Mistakes it avoids

• Making claims about results, prices or terms that are not approved.
• Long first messages that ask for too much.
• Updating pipeline numbers on hope rather than evidence.

What a good result looks like

An example, not your task: Your reps or regions each send a sales workbook, and the column names do not match.

• Column map. Each column you used and the standard name it was matched to.
• Extracted metrics. Revenue, units, deals, quota and attainment by rep and sheet type, with the source named.
• Import log. Rows read, kept, failed and skipped for each sheet, with reasons.
• Unmatched rows. Rows that did not match a rep, with the closest name and what to fix.
• Open checks. Figures and matches to confirm against the original files.

How the work is checked

• Personal details are verifiable and correct.
• Each deal update cites the call, email or note behind it.
• Follow-ups have dates and owners.
• States which checks passed, failed or could not run, and names missing inputs.

These checks have not been run against a model yet. Your AI uses them to check its own work.

Capabilities

• List the files. It names each file and sheet, and marks the ones that look like temporary or empty copies.
• Map the columns. It matches column names such as revenue, sales and total sales to one standard name, and shows each match.
• Find the sheet type. It reads the sheet name for month to date, year to date or year end. If the name is unclear, it asks once.
• Match the reps. It matches each row to your rep list by email or full name. Rows it cannot match are skipped and listed.
• Clean and calculate. It turns currency text into numbers and works out quota attainment where revenue and quota both exist.
• Log the result. It counts rows read, kept, failed and skipped for each sheet, and lists the open checks.

How the role works

1. Checks your task, context and available tools.
2. Picks the methods needed and prepares a work plan.
3. Carries out supported tasks, with each method's limits and review gates.
4. Checks the real deliverables and corrects supported faults.
5. Hands back files, evidence and unresolved gaps.

Your specialist's working setup

Download the base specialist without a task form or account. README.md and SETUP.md explain how to use the folder. SOUL.md defines its scope. INTAKE.md asks missing questions inside your AI. PLAYBOOK.md, SKILLS.md, BEST_PRACTICES.md, CHECKS.md and HANDOFF.md guide the work and its checked deliverables.

Complete associated skills, supporting files and licences are included. Choose only the methods needed for the actual task. Keep context, working files and accepted feedback in your AI project.

Use this specialist through MCP or the API

Call prepare_specialist with role_id sales-data-extraction-agent only. No task is required to load it. Your AI asks what job you need done, what you want back and which relevant context it can use.

For task-led hiring, call find_specialist, explain the suggested specialists and let the user accept or change the team.

Complete ZIP: Download base specialist. API: GET /api/v1/specialists/sales-data-extraction-agent/pack. The host AI supplies its own model, tools and execution; a pack does not connect accounts or run a task.

Inputs and helpful context

The specialist starts from the task brief. Only active methods require their inputs.

For the primary method, when used

• Your sales Excel or CSV files, attached or pasted, with the sheet names
• Your list of reps, with full names and emails, so rows can be matched
• What each figure means, if the names are unclear, such as whether a "Total" column is revenue
• Whether a file is a new version of an earlier one (optional)
• Quotas, if they are not already in the files (optional)

Helpful context (optional)

• A rep list with email addresses, which gives the safest match.
• One example of each sheet type, so the agent learns your layout.
• The date each file was created.

Tools and access

• Claude, ChatGPT or Gemini: They can read an attached workbook, so every sheet is checked and no row is dropped from a paste.
• Excel or Google Sheets: Open the clean table there, add a pivot and compare three rows with the original.

Functions and deliverables

• Sales data extraction planner: Pull month to date and year to date sales numbers out of messy Excel reports into one clean table, with a log of skipped rows.
  • A column map that shows which column in each file became revenue, units, deals or quota
  • A clean table with one row per rep, per sheet type, with the source file and sheet named
  • Quota attainment for each row that has both revenue and quota
  • An import log that counts rows read, kept, failed and skipped, with the reason
  • A list of rows that did not match a rep, so you can fix the names
  • Open checks for you to settle before you use the numbers
  Source: Michael Sitarzewski and Agency Agents contributors.
• Data extraction pipeline builder: Turn a website, file, or API source and field list into extraction code with validation checks.
  • Extraction script
  • Clean CSV or JSON file
  • Validation result
  • Cost and quota estimate
  Source: Alireza Rezvani.
• Trailing year financial calculator: Share annual and matching year-to-date figures. Get a trailing-year sales or profit total with period checks.
  • A trailing 12-month figure built from last year, this year to date and the same period last year, with the period checks.
  • An editable workbook of the trailing twelve months, with its inputs, formulas and test cases to rerun.
  • A tie-out of the trailing figure and four quarters where available, the inputs still missing and the checks that ran.
• Indian spreadsheet data cleaner: Share an Excel file or range. Review fixes for spaces, dates, text numbers and duplicates, with original data protected.
  • A cleaned copy of your sheet, with fixes in helper columns
  • Dates and numbers in one format
  • Duplicates marked, and removed only after you say yes
  • A table of every fix: column, issue, count and fix used
  • A list of columns that mix types, for you to review
  Source: nighomni123.
• Brand profile builder: Share your website. Get a reusable profile covering your products, customers, voice and approved claims, with sources.
  • A folder of eight plain files, from a start file for your AI to a claims list and a change log
  • A tag on every fact, and every claim marked approved, pending or banned
  • A two-sentence diagnosis of your biggest gap, and a list of what's missing and who can answer it
  • A short slice for your next step, if you name one

Tasks you can use it for

• Your reps or regions each send a sales workbook, and the column names do not match.
• You need month to date and year to date figures from many sheets, as one table.
• You want a record of what was read, what was skipped and why, before you build a report from it.
• A report is wrong, and you need to trace each number back to its file and sheet.

Limits to check before starting

• You already have one clean table and want a report by territory and rep: use the Sales data consolidation planner.
• You want to review deal health or forecast a quarter from a CRM export: use the Sales pipeline analyst.
• You want an AI to fill in a CRM record from a call note: use the CRM activity logger.
• You need a script that watches a folder and writes to a database. Ask the AI for code, then have a developer run and check it.

Best way to use this role

Give it one clear outcome, the actual files and a definition of done. Keep the work in one AI project so it can reuse checked context and earlier files.

• Add the pack to your AI as a skill. Or open a new chat and paste SKILL.md, then references/source-agent.md.
• Attach your Excel or CSV files if your AI accepts files. If not, copy each sheet in as text and name the sheet above it.
• Paste your list of reps with full names and emails. Say what each sheet type means, such as "MTD is month to date".
• Say "Extract the metrics and show the column map and import log".
• Read the column map first. Correct any wrong match, such as a column read as revenue that is really units.
• Check three rows against the original files. When they match, use the table for your report.

Read it as data

Read the complete role setup as JSON. Connect your AI to the AI Blocks MCP library and read aiblocks://roles/sales-data-extraction-agent for its role instructions and pinned sources.

This is a prepared AI role. No task has run. Your AI carries out authorized work using its available tools and reports the actual deliverables and checks.