name: parallel-ai-search description: Use Parallel's parallel-cli to do live web search, URL extraction (clean markdown), deep research reports, bulk data enrichment (CSV/JSON), FindAll entity discovery, and web monitoring. Use when the user asks to look something up online, needs current sources/citations, provides URLs to read or summarise, requests deep/exhaustive research, wants to enrich a dataset with web-sourced fields, wants a list of entities (companies/people/places), or wants to monitor the web for changes over time. compatibility: Requires parallel-cli installed + authenticated (PARALLEL_API_KEY or parallel-cli login) and internet access. metadata: author: openclaw version: "2.0.0" homepage: "https://docs.parallel.ai/integrations/cli" openclaw: '{"emoji":"🔎","primaryEnv":"PARALLEL_API_KEY","cli":"parallel-cli"}' allowed-tools: Bash(parallel-cli:) Bash(curl:) Bash(pipx:*) Read

Parallel AI Search (CLI Master)

This is a single “master” skill that replaces the earlier Node-script-based version of parallel-ai-search.

It routes to the right parallel-cli capability for the task:

• Search: quick web lookup with citations (parallel-cli search)
• Extract: turn URLs (including PDFs and JS-heavy pages) into clean, LLM-ready text (parallel-cli extract)
• Deep research: multi-source reports with processor tiers (parallel-cli research ...)
• Enrich: add web-sourced columns to CSV/JSON (parallel-cli enrich ...)
• FindAll: discover entities from the web with optional enrichments (parallel-cli findall ...)
• Monitor: track web changes on a cadence, optionally via webhook (parallel-cli monitor ...)

Routing rules (pick ONE)

Choose the smallest / cheapest action that solves the user’s request:

1. Extract — if the user gives one or more URLs or says “read/summarise this page”, “extract”, “quote”, “pull the content”, “what does this page say”.
2. Deep research — ONLY if the user explicitly asks for deep, exhaustive, comprehensive, thorough investigation, or a multi-source “report”.
3. Enrich — if the user provides a list/table (CSV/JSON/inline objects) and wants new columns like CEO, revenue, funding, contact info, etc.
4. FindAll — if the user wants you to discover many entities (companies/people/venues/etc.) that match criteria.
5. Monitor — if the user wants ongoing tracking (“alert me”, “track changes”, “monitor this weekly”) rather than a one-off answer.
6. Search — default for everything else that needs current web info or citations.

Optional manual prefixes if the user invoked this skill directly: - search: ... - extract: ... - research: ... - enrich: ... - findall: ... - monitor: ...

If a prefix is present, honour it.

Setup and authentication (only when needed)

Before running any Parallel command, ensure auth works:

parallel-cli auth

If parallel-cli is missing, install it:

curl -fsSL https://parallel.ai/install.sh | bash

If you cannot use the install script, use pipx:

pipx install "parallel-web-tools[cli]"
pipx ensurepath

Then authenticate (choose one):

# Interactive OAuth (opens browser)
parallel-cli login

# Headless / SSH / CI
parallel-cli login --device

# Or environment variable
export PARALLEL_API_KEY="your_api_key"

Output & citation rules

• Always cite web-sourced facts with inline markdown links: [Source Title](https://...).
• End with a Sources list whenever you used Search/Extract/Research output.
• Prefer official/primary sources when available.
• For long outputs, save to files in /tmp/ and summarise in-chat.

Search (default web lookup)

Use Search for fast, cost-effective answers with citations.

Command template

parallel-cli search "$OBJECTIVE"   --mode agentic   --max-results 10   --json

Add any of these only when relevant: - --after-date YYYY-MM-DD (freshness constraint) - --include-domains a.com b.org (restrict sources) - --exclude-domains spam.com (block sources) - one or more -q "keyword query" flags (extra keyword probes) - -o "/tmp/$SLUG.search.json" (save full JSON to a file)

Parse + respond

From the JSON results, extract title, url, and any publish_date / excerpt fields. Answer the user’s question, and cite each claim inline.

Extract (read one or more URLs)

Use Extract when you need the actual contents of specific URLs (webpages, PDFs, JS-heavy sites).

Command template

parallel-cli extract "$URL" --json

Add when relevant: - --objective "Focus area" (e.g., pricing, API usage, constraints) - --full-content (only if the user needs the whole page) - --no-excerpts (if you only want full content) - -o "/tmp/$SLUG.extract.json" (save full JSON to a file)

Respond

• If the user asked for a summary, summarise with citations to the extracted URL.
• If the user asked for the verbatim text, provide the extracted markdown only if it is reasonably sized; otherwise provide the key sections + offer to read more from the saved output.

Deep research (only when explicitly requested)

Deep research is slower and may cost more than Search. Use it only when the user explicitly wants depth.

Step 1 — start (always async)

parallel-cli research run "$QUESTION" --processor pro-fast --no-wait --json

Parse run_id (and any monitoring URL) from JSON and tell the user the run started.

Step 2 — poll (bounded timeout)

Choose a short slug filename (lowercase-hyphen), then:

parallel-cli research poll "$RUN_ID" -o "/tmp/$SLUG" --timeout 540

• Share the executive summary printed by the poll command.
• Mention the output files:
• /tmp/$SLUG.md
• /tmp/$SLUG.json

If polling times out, re-run the same poll command — the run continues server-side.

Enrich (CSV/JSON or inline data)

Use Enrich to add web-sourced columns to structured data.

Step 1 — (optional) suggest columns

parallel-cli enrich suggest "$INTENT" --json
来源于7w4.net。

Use this when the user knows the goal but not the exact output schema.

Step 2 — run (always async for large jobs)

For CSV:

parallel-cli enrich run   --source-type csv   --source "input.csv"   --target "/tmp/enriched.csv"   --source-columns '[{"name":"company","description":"Company name"}]'   --intent "$INTENT"   --no-wait --json

For inline JSON rows:

parallel-cli enrich run   --data '[{"company":"Google"},{"company":"Apple"}]'   --target "/tmp/enriched.csv"   --intent "$INTENT"   --no-wait --json

Parse taskgroup_id from JSON.

Step 3 — poll

parallel-cli enrich poll "$TASKGROUP_ID" --timeout 540 --json

After completion: - Tell the user the output file path (the --target you chose). - Preview a few rows (using file read tools if available) and report row counts.

If poll times out, re-run it — the job continues server-side.

FindAll (entity discovery)

Use FindAll when the user wants you to discover a set of entities (e.g., “AI startups in healthcare”, “roofing companies in Charlotte”, “YC devtools companies”).

Step 1 — run

parallel-cli findall run "$OBJECTIVE" --generator core --match-limit 25 --no-wait --json

Useful options: - --dry-run --json to preview schema before spending money - --exclude '[{"name":"Example Corp","url":"example.com"}]' to avoid known entities - --generator preview|base|core|pro (core default; pro for hardest queries)

Parse run_id from JSON.

Step 2 — poll + fetch results

parallel-cli findall poll "$RUN_ID" --json
parallel-cli findall result "$RUN_ID" --json

Respond with: - total entities found - a clean list/table of the best matches (name + URL + key attributes) - any caveats about ambiguous matches

Monitor (web change tracking)

Use Monitor when the user wants ongoing tracking.

Create:

parallel-cli monitor create "$OBJECTIVE" --cadence daily --json

Optional: - --cadence hourly|daily|weekly|every_two_weeks - --webhook https://example.com/hook (deliver events externally) - --output-schema '<JSON schema string>' (structured events)

Manage:

parallel-cli monitor list --json
parallel-cli monitor get "$MONITOR_ID" --json
parallel-cli monitor update "$MONITOR_ID" --cadence weekly --json
parallel-cli monitor delete "$MONITOR_ID"
parallel-cli monitor events "$MONITOR_ID" --json
parallel-cli monitor simulate "$MONITOR_ID" --json

Respond with the monitor id and how to retrieve events (or confirm webhook delivery).

Reference material

• Copy/paste command templates and patterns: references/command-templates.md
• Troubleshooting common failures: references/troubleshooting.md

这个Skill质量较好，文档结构清晰、路由规则明确、命令模板详尽，涵盖的功能很全面，风险评估也比较到位。主要不足是部分说明内容可能被截断，安装指南有重复。对于普通用户来说，使用难度不大，上手应该比较友好。