Hire an AI lab data import checker

Everything it can hand back

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

• Converted draft records and source-field map
• Verified-term table and blocked import rows

How it does the job well

• Agree the question and the decision it supports before analysing.
• Check the data first: date range, grain, missing values, duplicates and definitions.
• Compare like with like: same period length, definitions and segments.
• Separate what the data shows from what it might mean, and give confidence levels.

Mistakes it avoids

• Reporting a change without checking whether tracking or definitions changed.
• Treating correlation as cause.
• Averages that hide important segments or outliers.

What a good result looks like

An example, not your task: Preparing instrument data for a research data handoff

• Converted records. Draft JSON and flat CSV, or the proposed structure if no files could be made.
• Mapping table. Source file and field, target field, rule, unit, evidence and approval state for each field.
• Verified-term table. Original label, checked term ID and definition, with unclear matches left open.
• Row-level exceptions. Accepted and blocked rows, with count checks against the raw files.
• Import-owner handoff. What the destination owner must still do, with PASS, FAIL or NOT RUN for every check.

How the work is checked

• Numbers reconcile to the source totals, and the reconciliation is shown.
• Every chart and number states its source, period and definition.
• Limits and next checks are listed.
• 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

• Convert with provenance. Read raw exports and target examples. Detect the instrument; clarify ambiguous raw and calculated fields. When permitted tools exist, create ASM JSON and flat CSV. Hand off records, mapping decisions and source-row references.
• Resolve scientific labels. Use mapped labels and sample context. Search the relevant ontology service and validate each selected term detail. Return original label, candidate ID, verified definition, evidence and unresolved ambiguity. Pass accepted mappings to validation.
• Check the import boundary. Compare converted records and accepted terms with the target schema. Reconcile counts, IDs, fields, units and missing values against raw input. Produce row-level exceptions before any import.

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 validate-lab-data-before-system-import 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/validate-lab-data-before-system-import/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

• INSTRUMENT_EXPORTS: attach original CSV, Excel, TXT or PDF files; identify the instrument and run.
• TARGET_SCHEMA: give the ASM version, field definitions, units, vocabulary and an approved example.
• LABEL_SCOPE: name scientific columns needing ontology terms and supply sample context.
• EXECUTION_SCOPE: identify permitted local processing and destination owner; supply no credentials.

Functions and deliverables

• Prepare lab data for a checked import: Attach instrument exports and a target schema. Get mapped records, checked scientific labels and an import exception list.
  • Converted draft records and source-field map
  • Verified-term table and blocked import rows
• Lab instrument data converter: Convert lab instrument files into Allotrope JSON or flat CSV, with parser code for handoff.
  • Allotrope JSON files
  • A flat CSV of measurements
  • Parser code for handoff
  • A short summary of what was converted
  Source: Anthropic.
• Scientific ontology ID matcher: Share scientific labels or existing IDs. Search public term services, check meanings and flag ambiguous or unverified matches.
  • The best matching ontology ID and label for each term
  • Each ID checked against the lookup service
  • Flags for wrong, mislabelled or obsolete IDs
  • Terms with no match listed on their own
  • Ontology prefix lookups
  Source: K-Dense.
• Data quality auditor: Share data and quality rules. Get missing values, duplicates and unusual records, with ranked fixes and a score where supported.
  • Data quality score
  • Missing value and outlier findings
  • Ranked repair plan
  • Monitoring checklist
  Source: Alireza Rezvani.
• Metric definition writer: Share a metric, calculation and business use. Get a YAML definition, validation results and questions for the data owner.
  • A filled metric, dimension or entity YAML
  • A validation report
  • dbt-ready field names
  • Notes on edge cases
  Source: Nimrod Fisher.

Tasks you can use it for

• Preparing instrument data for a research data handoff

Limits to check before starting

• You only want to convert instrument files, with no term checks: use Lab instrument data converter.
• You only want to score the quality of a data set: use Data quality auditor.
• You want a method validation plan, not a data import: use Lab test validation planner.

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.

• Download the complete workflow or agent pack and unzip it.
• Give the complete folder to your AI. Start with START_HERE.txt, then provide the named inputs in the coordinator skill.
• Follow its checkpoints and check the evidence before using any output. Agent packs run one requested cycle; they do not create a schedule.

Read it as data

Read the complete role setup as JSON. Connect your AI to the AI Blocks MCP library and read aiblocks://roles/validate-lab-data-before-system-import 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.