Individual AI skill · AI for Inteligencia e Investigación

data-synthesizer

Sintetiza datos de múltiples fuentes: limpia datasets dispares, valida consistency, fusiona data points conflictivos, y genera unified data model. Para cuando tienes datos de múltiples databases, surveys, y APIs que necesitan.

Source content · review pending Source language: EN

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For projects ChatGPT · Gemini · Claude For agents Cursor · Claude Code · Codex

See how to use it

Free ZIP · One canonical method · Usage example · Installation guide · Licence notices

What you receive

One method. No filler.

01
SKILL.md / Project instructions
The same method, wrapped for your way of working.
02
EJEMPLO-DE-USO.md
A prompt you can adapt to your case.
03
INSTALAR-CON-TU-IA.md
The route for web interfaces and agents.

Version: 1.0.1
Format: Platform-agnostic
Access: Free with account

From file to useful result

How to ask with this skill installed

Installing the skill gives your AI a method. Your request still has to provide the case-specific facts, constraints and expected output.

1Name the real task

Describe the decision or deliverable, not just the topic.

2Provide the evidence

Add source material, audience, limits and known facts.

3Define done

Set format, quality criteria and checks.

Source example Replace the brackets with your case

Synthesize customer data from 3 sources for our Spain-based SaaS:

SOURCE A — CRM (HubSpot):
3,400 rows. Columns: contact_id, email, company_name, created_date, industry, deal_value_eur, owner
Problem: 8% of emails are work email only (no first/last name), some company_name have accents wrong

SOURCE B — Billing (Stripe):
1,890 rows. Columns: customer_id, email, company, mrr_eur, subscription_start, plan
Problem: company names differ from CRM (e.g., "Stripe" vs "Stripe Technologies")

SOURCE C — Support (Zendesk):
890 rows. Columns: requester_email, subject, priority, created_at, status

GOLDEN KEY: email address

CONFLICTS I ALREADY KNOW:
- Revenue for 45 customers differs between CRM and Stripe (CRM includes deals not yet won)

Please: (1) Clean each source, (2) Match across sources using email, (3) Apply precedence: Stripe for financial, CRM for company/contact info, (4) Flag unresolved matches (confidence < 80%), (5) Produce unified dataset with conflict_log tab

Mistakes to avoid

Silently imputing null values: Don't fill in "Assumed Spain" for a missing country field. Flag it.
Not documenting transformations: You spent 2 hours figuring out a fix. Write it down so you don't re-learn it.
Assuming sources agree: Always compare sums before merging. If revenue doesn't match, find out why before merging.
Fuzzy matching too aggressively: A 60% confidence match is probably wrong. Flag it and move on.

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Individual skill

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Professional pack

AI for Inteligencia e Investigación

This skill plus the other methods selected for this professional area.

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Same method, different setup

Where will you use it?

We do not duplicate the skill for every platform. We keep one canonical method and explain the correct installation route.

01 ChatGPT

Use it as project knowledge and instructions.

Open web-interface guide → 02 Claude

Add it to a Project and keep the case material separate.

Open web-interface guide → 03 Agents and IDEs

Install SKILL.md in the path expected by your agent.

Compare agent guides →

Traceable by design

Source and licence travel with the file.

SkillsBundle catalogues material of its own and from third parties. Inclusion does not claim original authorship. The individual ZIP preserves the canonical catalogue path and includes the applicable licence notices.

Catalogue path: skills/verticals/research-intelligence/skills/data-synthesizer
Ficha origin: Extracted from SKILL.md

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Start with one real task

Install the method. Keep your judgement.

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