parallel-deep-research
ユーザーが「深度研究」または「詳尽」を明示的に要求した場合にのみ有効化され、包括的で詳細な情報収集と分析を行い、詳細な研究レポートを提供します。
npx skills add parallel-web/parallel-agent-skills --skill parallel-deep-researchBefore / After 効果比較
1 组従来の調査方法は時間がかかりがちで、短時間で包括的かつ深い情報を得ることは困難です。複雑なテーマに直面すると、情報が断片化し、全体像を把握することが難しくなり、意思決定の質に影響を与えます。
このスキルを活用することで、深層並行研究を行い、多岐にわたる情報源から情報を包括的に収集・統合できます。詳細かつ包括的な調査レポートを提供し、ユーザーが複雑なテーマを迅速に理解し、賢明な意思決定を行うのを支援します。
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
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):
| Processor | Expected latency | Use when |
|---|---|---|
pro-fast | 30s – 5 min | Default — good balance of depth and speed |
ultra-fast | 1 – 10 min | Deeper analysis, more sources (~2x cost) |
ultra | 5 – 25 min | Maximum depth, only when explicitly requested (~3x cost) |
Parse the JSON output to extract the run_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-oflag writes results to files instead. - The
-oflag 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:
- Tell the user the research is still running server-side
- Re-run the same
parallel-cli research pollcommand 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:
- Share the executive summary that the poll command printed to stdout
- Tell the user the two generated file paths:
$FILENAME.md— formatted markdown report$FILENAME.json— metadata and basis
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.
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"
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