From parallel
Bulk enriches lists of companies, people, or products with web-sourced fields like CEO names, funding, and contacts. Processes CSV files or inline data; supports multi-turn context chaining.
npx claudepluginhub parallel-web/parallel-agent-skills --plugin parallelThis skill is limited to using the following tools:
Enrich: $ARGUMENTS
Adds research-powered enrichment columns (e.g., funding, verticals, tech stack) to Extruct company tables. Use to run column configs from enrichment-design, design on-the-fly, trigger runs, or monitor progress.
Enriches company lists with 5-10 key decision makers' names, titles, verified emails, and LinkedIn URLs using Parallel, Apollo, and MillionVerifier APIs.
Dispatches AI researchers to classify, rank, score, deduplicate, merge, forecast, and enrich Python dataframes at scale.
Share bugs, ideas, or general feedback.
Enrich: $ARGUMENTS
Inform the user that enrichment may take several minutes depending on the number of rows and fields requested.
Use ONE of these command patterns (substitute user's actual data):
For inline data:
parallel-cli enrich run --data '[{"company": "Google"}, {"company": "Microsoft"}]' --intent "CEO name and founding year" --target "output.csv" --no-wait --json
For CSV file:
parallel-cli enrich run --source-type csv --source "input.csv" --target "output.csv" --source-columns '[{"name": "company", "description": "Company name"}]' --intent "CEO name and founding year" --no-wait --json
If this is a follow-up to a previous research or enrichment task where you know the interaction_id, add context chaining:
parallel-cli enrich run --data '...' --intent "..." --target "output.csv" --no-wait --json --previous-interaction-id "$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:
Tell them they can background the polling step to continue working while it runs.
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:
--timeout 540 (9 minutes) to stay within tool execution limitsEnrichment of large datasets can take longer than 9 minutes. If the poll exits without completing:
parallel-cli enrich poll command to continue waitingAfter step 1: Share the monitoring URL (for tracking progress).
After step 2:
interaction_id and tell the user they can ask follow-up questions that build on this enrichmentDo 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.
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"