Bulk enrichment of company, people, or product data with web-sourced fields like CEO names, funding, and contact info.
Works with
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\
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionparallel-data-enrichmentExecute the skills CLI command in your project's root directory to begin installation:
Fetches parallel-data-enrichment from parallel-web/parallel-agent-skills and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate parallel-data-enrichment. Access via /parallel-data-enrichment in your agent's command palette.
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.
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Create detailed user stories, acceptance criteria, and feature specs
Example
Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios
Reduce spec writing time by 50%, ensure comprehensive coverage
Research competitors, compare features, identify gaps
Example
Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities
Complete competitive research in 2 hours instead of 2 days
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
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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"
Make data-driven prioritization decisions faster
Draft PRDs, status updates, and stakeholder presentations
Example
Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement
Save 3-5 hours/week on communication overhead
Prerequisites
Time Estimate
30-60 minutes to see productivity improvements
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ Use when
Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.
✗ Avoid when
Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.
mattpocock/skills
parcadei/continuous-claude-v3
cursor/plugins
ailabs-393/ai-labs-claude-skills
pproenca/dot-skills
mattpocock/skills
parallel-data-enrichment is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
Keeps context tight: parallel-data-enrichment is the kind of skill you can hand to a new teammate without a long onboarding doc.
Useful defaults in parallel-data-enrichment — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
We added parallel-data-enrichment from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
Useful defaults in parallel-data-enrichment — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
parallel-data-enrichment is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
Registry listing for parallel-data-enrichment matched our evaluation — installs cleanly and behaves as described in the markdown.
parallel-data-enrichment reduced setup friction for our internal harness; good balance of opinion and flexibility.
parallel-data-enrichment fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
parallel-data-enrichment has been reliable in day-to-day use. Documentation quality is above average for community skills.
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