SkillsSKILLS
Productivity

parallel-data-enrichment▌

parallel-web/parallel-agent-skills · updated Apr 8, 2026

$npx skills add https://github.com/parallel-web/parallel-agent-skills --skill parallel-data-enrichment
summary

Bulk enrichment of company, people, or product data with web-sourced fields like CEO names, funding, and contact info.

  • ›Accepts inline JSON data or CSV files; outputs enriched results to CSV
  • ›Runs asynchronously with progress tracking via monitoring URL and polling commands
  • ›Requires parallel-cli tool and internet access; handles large datasets with configurable timeouts
  • ›Supports flexible field requests through natural language intent descriptions (e.g., \"CEO name and founding year\
skill.md

Data Enrichment

Enrich: $ARGUMENTS

Before starting

Inform the user that enrichment may take several minutes depending on the number of rows and fields requested.

Step 1: Start the enrichment

Use ONE of these command patterns (substitute user's actual data):

For inline data:

parallel-cli enrich run --data '[{"company": "Google"}, {"company": "Microsoft"}]' --intent "CEO name and founding year" --target "output.csv" --no-wait --json

For CSV file:

parallel-cli enrich run --source-type csv --source "input.csv" --target "output.csv" --source-columns '[{"name": "company", "description": "Company name"}]' --intent "CEO name and founding year" --no-wait --json

If this is a follow-up to a previous research or enrichment task where you know the interaction_id, add context chaining:

parallel-cli enrich run --data '...' --intent "..." --target "output.csv" --no-wait --json --previous-interaction-id "$INTERACTION_ID"

By chaining interaction_id values across requests, each follow-up automatically has the full context of prior turns — so you can enrich entities discovered in earlier research without restating what was already found.

IMPORTANT: Always include --no-wait so the command returns immediately instead of blocking.

Parse the output to extract the taskgroup_id, interaction_id, and monitoring URL. Immediately tell the user:

  • Enrichment has been kicked off
  • The monitoring URL where they can track progress

Tell them they can background the polling step to continue working while it runs.

Step 2: Poll for results

parallel-cli enrich poll "$TASKGROUP_ID" --timeout 540 --output "/tmp/$TARGET"

Use the same target filename from step 1. The --target flag on enrich run does not carry over to the poll — you must pass --output here to save the results.

Important:

  • Use --timeout 540 (9 minutes) to stay within tool execution limits

If the poll times out

Enrichment of large datasets can take longer than 9 minutes. If the poll exits without completing:

  1. Tell the user the enrichment is still running server-side
  2. Re-run the same parallel-cli enrich poll command to continue waiting

Response format

After step 1: Share the monitoring URL (for tracking progress).

After step 2:

  1. Report number of rows enriched
  2. Preview first few rows of the output CSV
  3. Tell user the full path to the output CSV file
  4. Share the interaction_id and tell the user they can ask follow-up questions that build on this enrichment

Do NOT re-share the monitoring URL after completion — the results are in the output file.

Remember the interaction_id — if the user asks a follow-up question that relates to this enrichment, use it as --previous-interaction-id in the next research or enrichment command.

Setup

If parallel-cli is not found, install and authenticate:

curl -fsSL https://parallel.ai/install.sh | bash

If unable to install that way, install via pipx instead:

pipx install "parallel-web-tools[cli]"
pipx ensurepath

Then authenticate:

parallel-cli login

Or set an API key: export PARALLEL_API_KEY="your-key"