parallel-deep-research（并行深度研究）

Deep Research

Research topic: $ARGUMENTS

When to use (vs parallel-web-search)

ONLY use this skill when the user explicitly requests deep/exhaustive research. Deep research is 10-100x slower and more expensive than parallel-web-search. For normal "research X" requests, quick lookups, or fact-checking, use parallel-web-search instead.

Step 1: Start the research

parallel-cli research run "$ARGUMENTS" --processor pro-fast --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 research run "$ARGUMENTS" --processor lite --no-wait --json --previous-interaction-id "$INTERACTION_ID"

By chaining interaction_id values across requests, each follow-up question automatically has the full context of prior turns — so you can drill deeper into a topic without restating what was already researched. Use --processor lite for follow-ups since the heavy research was already done in the initial turn and the follow-up just needs to build on that context.

This returns instantly. Do NOT omit --no-wait — without it the command blocks for minutes and will time out.

Processor options (choose based on user request):

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

• Deep research has been kicked off
• The expected latency for the processor tier chosen (from the table above)
• 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

Choose a descriptive filename based on the topic (e.g., ai-chip-market-2026, react-vs-vue-comparison). Use lowercase with hyphens, no spaces.

parallel-cli research poll "$RUN_ID" -o "$FILENAME" --timeout 540

Important:

• Use --timeout 540 (9 minutes) to stay within tool execution limits
• Do NOT pass --json — the full output is large and will flood context. The -o flag writes results to files instead.
• The -o flag generates two output files:

- $FILENAME.json — metadata and basis - $FILENAME.md — formatted markdown report

• The poll command prints an executive summary to stdout when the research completes. Share this executive summary with the user — it gives them a quick overview without having to open the files.

If the poll times out

Higher processor tiers can take longer than 9 minutes. If the poll exits without completing:

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

Response format

After step 1: Share the monitoring URL (for tracking progress only — it is not the final report).

After step 2:

1. Share the executive summary that the poll command printed to stdout
2. Tell the user the two generated file paths:

- $FILENAME.md — formatted markdown report - $FILENAME.json — metadata and basis

1. Share the interaction_id and tell the user they can ask follow-up questions that build on this research (e.g., "drill deeper into X" or "compare that to Y")

Do NOT re-share the monitoring URL after completion — the results are in the files, not at that link.

Ask the user if they would like to read through the files for more detail. Do NOT read the file contents into context unless the user asks.

Remember the interaction_id — if the user asks a follow-up question that relates to this research, 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"