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Skills/parallel-web/parallel-agent-skills/parallel-data-enrichment
parallel-data-enrichment logo

parallel-data-enrichment

parallel-web/parallel-agent-skills
9K installs57 stars
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Installation

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

Summary

Bulk data enrichment. Adds web-sourced fields (CEO names, funding, contact info) to lists of companies, people, or products. Use for enriching CSV files or inline data. Supports multi-turn: pass --previous-interaction-id from a prior research task to carry context forward.

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.

Optional: Suggest output columns

If the user gave a vague intent ("enrich these companies with useful info") and you're not sure what columns to add, ask the API for a suggestion before kicking off the run:

parallel-cli enrich suggest "Find CEO and recent funding info" --json

The response is an envelope: {title, processor, enriched_columns, warnings}. Extract just the enriched_columns array (not the whole envelope) and pass it as the value of --enriched-columns on enrich run, in place of --intent — the two flags are alternative ways to specify what to enrich, not combined. If suggest returned a processor, pass it through explicitly via --processor on the run call (it's a tuned recommendation for the schema). Skip this whole section if the user already specified the fields they want.

enrich suggest requires parallel-cli ≥ 0.3.0. If it errors with anything resembling no such command / No such command / unknown command, do not bail — skip the suggestion step, fall through to step 1 with --intent, complete the run, and mention parallel-cli update (or pipx upgrade parallel-web-tools) in the final response so the user picks up the feature next time.

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 task and you have its interaction_id, add context chaining:

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

The enrichment will run with the full context of that prior research — so you can enrich entities discovered earlier without restating what was already found. Note: enrichment does not itself produce a new interaction_id, so you cannot chain a further follow-up off of an enrichment.

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

Parse the --json output to extract taskgroup_id and url. The output is {taskgroup_id, url, num_runs} — there is no interaction_id field, do not look for one. 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

Pick a concrete output path (e.g., /tmp/enrichment-acme.json). Note: the file is JSON regardless of the extension you choose — it's an array of {input, output} objects, not a CSV. Name it .json to avoid confusing yourself or the user.

parallel-cli enrich poll "$TASKGROUP_ID" --timeout 540 --output "/tmp/enrichment-<descriptive-name>.json"

Important:

  • Use --timeout 540 (9 minutes) to stay within tool execution limits
  • The --target from step 1 is unused in --no-wait mode — only --output here determines where results are saved, and the file is always JSON

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 from the output file (it's a JSON array of {input, output} objects)
  3. Tell the user the full path to the output file

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

Setup

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

/parallel:parallel-cli-setup

If any parallel-cli enrich command returns 403, tell the user balance is likely required. Offer to run parallel-cli balance get, and if needed ask for explicit confirmation before running parallel-cli balance add <amount_cents>. Then retry the original enrichment command.

Score

0–100
69/ 100

Grade

C

Popularity21/30

8,738 installs — solid traction.

Completeness27/30

Documented: full SKILL.md body, description, one-line install. Missing: category/license metadata.

Trust15/25

Community skill with a public GitHub source repository you can review.

Freshness6/15

No update timestamp is tracked for this skill in our catalog.

Scored automatically from popularity, completeness, trust, and freshness — computed only from data in our catalog, never fabricated.

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Paste a snippet into your GitHub README. The badge updates automatically and links back to this page.

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Markdown

[![Parallel Data Enrichment skill](https://www.remoteopenclaw.com/skills/parallel-web/parallel-agent-skills/parallel-data-enrichment/badges/score.svg)](https://www.remoteopenclaw.com/skills/parallel-web/parallel-agent-skills/parallel-data-enrichment)

HTML

<a href="https://www.remoteopenclaw.com/skills/parallel-web/parallel-agent-skills/parallel-data-enrichment"><img src="https://www.remoteopenclaw.com/skills/parallel-web/parallel-agent-skills/parallel-data-enrichment/badges/score.svg" alt="Parallel Data Enrichment skill"/></a>

Parallel Data Enrichment FAQ

How do I install the Parallel Data Enrichment skill?

Run “npx skills add https://github.com/parallel-web/parallel-agent-skills --skill parallel-data-enrichment” in your terminal. The skill is added to your agent's skills directory and picked up automatically on the next run — no restart or extra configuration needed.

What does the Parallel Data Enrichment skill do?

Bulk data enrichment. Adds web-sourced fields (CEO names, funding, contact info) to lists of companies, people, or products. Use for enriching CSV files or inline data. Supports multi-turn: pass --previous-interaction-id from a prior research task to carry context forward. The full SKILL.md on this page shows the exact instructions the skill gives your agent.

Is the Parallel Data Enrichment skill free?

Yes. Parallel Data Enrichment is a free, open-source skill published from parallel-web/parallel-agent-skills. As with any third-party skill, review the source repository before installing it into an agent with sensitive access.

Does Parallel Data Enrichment work with Claude Code and OpenClaw?

Yes. Skills use the portable SKILL.md format, so Parallel Data Enrichment works with Claude Code, OpenClaw, Codex, Hermes, and any other agent that reads SKILL.md skills.

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