data-export-engine – Data & Science skill for AI agents

Data Export Engine

What it is: Data extraction.

Overview

Works out the smallest useful Data Export Engine setup for the business in front of it, then

builds it only when asked. The default output is a short recommendation, not a

spreadsheet. Artifacts - CSV, SQL DDL, JSON Schema, Notion mapping - are produced on

request, from one field list so they cannot drift apart.

Layer: Layer 9: Analyze. Fits: Scale stage. Table code: n/a.

When to Use This Skill

• data export log
• data request tracker
• export register
• data extraction record

Also use it when the user says "data extraction", or describes the same process happening in a

spreadsheet, a document or someone inboxes.

Do not use it for: payroll calculation, tax filing, or legal advice. This skill produces

empty templates only - it never holds or processes real employee or customer data.

How It Works

Follow the shared execution contract. The module-specific rules below define only domain fields, decisions, calculations, and safety constraints.

Step 1 - Identify intent

Read the request and pick the intent before asking anything.

• "set up" or "build" or "create" -> the user wants artifacts; go to Step 2.
• "our process is ..." or "it is in a sheet" -> the user wants to move an existing process; capture it, then Step 2.
• "is this right" or "review" or "audit" -> the user wants a check, not a build; answer from what they share.
• "how do I ..." -> advice question; answer directly and offer the build only if it helps.

Ask only if this is the highest-value missing fact; otherwise proceed without an opener:

> Q: Where does the data need to go?

Step 2 - Ask only what is missing

Skip anything the user already answered, in any earlier message. Ask the rest one at a

time, and stop as soon as the remaining answers would not change the output.

• Sources - Which systems? / How many? / What format today?
• Destination - Where is it going? / Who consumes it? / How often?
• Scope - All records or a subset? / Any sensitive data? / Filtering needed?
• Current process - How exported now? / Manual or automated? / What breaks?
• Outcome - What do you need? / A one-off export or a repeating feed?

Never invent an answer. If the user does not know, record it as unknown and carry on.

Step 3 - Hold the internal context

Hold the answers in this shape. It stays internal - it is not shown to the user unless

they ask, and it never carries a value the user did not give.

module: data-export-engine
intent: null            # setup | advice | review | fix | build | convert | export
scale: null             # Starter | Growth | Scale, only if the answer changes it
areas:
  "Sources": null
  "Destination": null
  "Scope": null
  "Current process": null
  "Outcome": null
requested_outputs: []   # csv | sql | json | notion | xlsx - requested formats only
confirmed_facts: []     # only what the user actually said
open_questions: []      # the unanswered ones, in the order worth asking

Step 4 - Recommend the smallest workflow

If an artifact was requested, build it after resolving essential missing facts. Otherwise give a short recommendation and offer the relevant artifact.

Recommended approach: For a one-off export, use a generated CSV. Only build a repeating feed when the consumer needs it on a schedule.

Why this one: Most export requests are one-off. A manual CSV generated from a view solves them; automation is worth it only for a fixed schedule.

Workflow: Scope defined → Exported → Delivered → Consumed or archived

Step 5 - Build only on request

Once the user asks for it, derive the fields from the confirmed context and emit the

requested artifacts. For machine-readable text, keep prose outside the data; for files,

provide a usable link. Report material validation failures or limitations separately.

**A selected Notion output is rendered by notion-manual-import, so route the

Notion step there.** When the user selects Notion, hand that step to

@notion-manual-import: it holds the CSV, the property

mapping, the import steps and the verification checklist, and it renders the Field

Reference below instead of defining a table of its own. Do not restate the mapping

here and do not improvise the import steps. Manual CSV and mapping outputs need no

connection. For requested workspace changes, follow the shared contract: verify actual

tool access and the target before writing. A user saying "connected" is not tool evidence.

Never ask for a Notion password or token.

For an Excel-compatible CSV, use UTF-8 with a byte order mark so Excel opens the

text correctly. A CSV is not an .xlsx workbook; create .xlsx only when the user

requests a workbook.

A CSV carries no types, so after it, name the columns

that need a number, date or currency format applied.

Export Name,Source Module,Requested By,Purpose,Format,Date Requested,Date Delivered,Contains Personal Data,Approved By,Status,Export ID
Payroll export Feb,Invoices & Billing,Rohit Verma,Payroll audit,CSV,2026-01-15,2026-01-15,FALSE,Vikram Singh,Completed,

CREATE TABLE data_export_engine (
  export_name VARCHAR(255),
  source_module VARCHAR(255),
  requested_by VARCHAR(255),
  purpose VARCHAR(255),
  format VARCHAR(255),
  date_requested DATE NOT NULL,
  date_delivered DATE,
  contains_personal_data BOOLEAN NOT NULL,
  approved_by VARCHAR(255),
  status VARCHAR(100) NOT NULL,
  export_id SERIAL PRIMARY KEY,
  created_at TIMESTAMP DEFAULT NOW(),
  updated_at TIMESTAMP DEFAULT NOW()
);

CREATE INDEX idx_data_export_engine_status ON data_export_engine (status);

{
  "$schema": "https://json-schema.org/draft/2020-12/schema",
  "title": "Data Export Engine",
  "type": "object",
  "additionalProperties": false,
  "properties": {
      "Export Name": { "type": "string" },
      "Source Module": { "type": "string" },
      "Requested By": { "type": "string" },
      "Purpose": { "type": "string" },
      "Format": { "type": "string" },
      "Date Requested": { "type": "string", "format": "date" },
      "Date Delivered": { "type": ["string", "null"], "format": "date" },
      "Contains Personal Data": { "type": "boolean" },
      "Approved By": { "type": "string" },
      "Status": { "type": "string" },
      "Export ID": { "type": "integer" }
  },
  "required": [
      "Date Requested",
      "Contains Personal Data",
      "Status"
  ]
}

| CSV column | Notion property | Set after import |
|---|---|---|
| Export Name | Title | Use as the database title |
| Source Module | Text | Leave as Text |
| Requested By | Text | Leave as Text |
| Purpose | Text | Leave as Text |
| Format | Text | Leave as Text |
| Date Requested | Date | Convert to Date |
| Date Delivered | Date | Convert to Date |
| Contains Personal Data | Checkbox | Convert to Checkbox |
| Approved By | Text | Leave as Text |
| Status | Select (add options after import) | Convert to Select, add options: "Requested", "Approved", "Running", "Completed", "Failed" |
| Export ID | Text (preserve source ID) | Keep imported IDs as Text; optionally add a separate Unique ID property |

The rows above are documentation examples only. Emit empty templates unless the user explicitly requests examples. Money stays currency, dates stay date,

and anything pointing at another table stays relation.

Field Reference

| # | Field | Type | SQL | JSON Schema | Notion | CSV example |

|---:|---|---|---|---|---|---|

| 1 | Export Name | text | VARCHAR(255) | string | Text | Payroll export Feb |

| 2 | Source Module | text | VARCHAR(255) | string | Text | Invoices & Billing |

| 3 | Requested By | text | VARCHAR(255) | string | Text | Rohit Verma |

| 4 | Purpose | text | VARCHAR(255) | string | Text | Payroll audit |

| 5 | Format | text | VARCHAR(255) | string | Text | CSV |

| 6 | Date Requested | date | DATE | string, format: date | Date | 2026-01-15 |

| 7 | Date Delivered | date | DATE | string, format: date | Date | 2026-01-15 |

| 8 | Contains Personal Data | checkbox | BOOLEAN | boolean | Checkbox | FALSE |

| 9 | Approved By | text | VARCHAR(255) | string | Text | Vikram Singh |

| 10 | Status | select | VARCHAR(100) | string | Select (add options after import) | Completed |

| 11 | Export ID | id | SERIAL PRIMARY KEY | integer | Text (preserve source ID) | (blank) |

Date Delivered stays unknown/null before delivery; never invent a completed date for Requested/Approved/Failed records. The personal-data flag must be explicitly assessed, not inferred false from missing inputs.

Select Options

Status

Requested | Approved | Running | Completed | Failed

Relations

Link fields: none

Examples

Prompt

We copy data out of two systems by hand every month.

Context first - one question per message, nothing already answered:

> Q: Where does it go?

> *

… (full content is in the download package)