---
name: parallel-ai-search-30e527279dcd
description: Web search + URL extraction via Parallel Search/Extract APIs. Use for up-to-date research, domain-scoped searching, and extracting LLM-ready excerpts/markdown from URLs.
homepage: https://docs.parallel.ai/search/search-quickstart
metadata: {"openclaw":{"emoji":"🔎","homepage":"https://docs.parallel.ai/","requires":{"bins":["node"],"env":["PARALLEL_API_KEY"]},"primaryEnv":"PARALLEL_API_KEY"}}
---

# Parallel AI Search (OpenClaw skill)

Use this skill to run web research through **Parallel Search** (ranked, LLM-optimised excerpts) and **Parallel Extract** (clean markdown from specific URLs, including JS-heavy pages and PDFs).

The skill ships tiny **Node .mjs** helpers so the agent can call the APIs deterministically via the OpenClaw **exec** tool.

## Quick start

### 1) Provide the API key

Prefer configuring it in `~/.openclaw/openclaw.json` (host runs):

```json5
{
  skills: {
    entries: {
      "parallel-ai-search": {
        enabled: true,
        apiKey: "YOUR_PARALLEL_API_KEY"
      }
    }
  }
}
```

Notes:
- If the agent run is **sandboxed**, the Docker sandbox does **not** inherit host env. Provide the key via `agents.defaults.sandbox.docker.env` (or bake it into the image).
- This skill is gated on `PARALLEL_API_KEY`. If it’s missing, OpenClaw won’t load the skill.

### 2) Run a search

Use **exec** to run:

```bash
node {baseDir}/scripts/parallel-search.mjs \
  --objective "When was the United Nations established? Prefer UN websites." \
  --query "Founding year UN" \
  --query "Year of founding United Nations" \
  --max-results 5 \
  --mode one-shot
```

### 3) Extract content from URLs

```bash
node {baseDir}/scripts/parallel-extract.mjs \
  --url "https://www.un.org/en/about-us/history-of-the-un" \
  --objective "When was the United Nations established?" \
  --excerpts \
  --no-full-content
```

### 4) One command: search → extract top results

```bash
node {baseDir}/scripts/parallel-search-extract.mjs \
  --objective "Find recent research on quantum error correction" \
  --query "quantum error correction 2024" \
  --query "QEC algorithms" \
  --max-results 6 \
  --top 3 \
  --excerpts
```

## When to use

Trigger this skill when the user asks for:
- “Parallel search”, “parallel.ai search”, “Parallel Extract”, “Search API”, “Extract API”
- “web research with Parallel”, “LLM-optimised excerpts”, “source_policy/include_domains”, “after_date”, “fetch_policy”
- “extract clean markdown from URL/PDF”, “crawl a JS-heavy page”, “get fresh web results”

## Default workflow

1. **Search** with an *objective* + a few *search_queries*.
2. **Inspect** titles/URLs/publish dates; choose the best sources.
3. **Extract** the specific pages you actually need (top N URLs).
4. **Answer** using the extracted excerpts/full content.

Use **Search** to discover; use **Extract** to read.

## Best-practice prompting for Parallel

### Objective
Write 1–3 sentences describing:
- the real task context (why you need the info)
- freshness constraints (“prefer 2025+”, “after 2024-01-01”, “use latest docs”)
- preferred sources (“official docs”, “standards bodies”, “GitHub releases”)

### search_queries
Add 3–8 keyword queries that include:
- the specific terms, version numbers, error strings
- common synonyms
- if relevant, date terms (“2025”, “2026”, “Jan 2026”)

### Mode
- Use `mode=one-shot` for single-pass questions (default).
- Use `mode=agentic` for multi-step research loops (shorter, more token-efficient excerpts).

### Source policy
When you need tight control, set `source_policy`:
- `include_domains`: allowlist (max 10)
- `exclude_domains`: denylist (max 10)
- `after_date`: RFC3339 date (`YYYY-MM-DD`) to filter for freshness

## Scripts

All scripts print a JSON response to stdout by default.

### `scripts/parallel-search.mjs`

Calls `POST https://api.parallel.ai/v1beta/search`.

Common flags:
- `--objective "..."`
- `--query "..."` (repeatable)
- `--mode one-shot|agentic`
- `--max-results N` (1–20)
- `--include-domain example.com` (repeatable)
- `--exclude-domain example.com` (repeatable)
- `--after-date YYYY-MM-DD`
- `--excerpt-max-chars N` (per result)
- `--excerpt-max-total-chars N` (across results)
- `--fetch-max-age-seconds N` (force freshness; 0 disables)
- `--request path/to/request.json` (advanced: full request passthrough)
- `--request-json '{"objective":"..."}'` (advanced)

### `scripts/parallel-extract.mjs`

Calls `POST https://api.parallel.ai/v1beta/extract`.

Common flags:
- `--url "https://..."` (repeatable, max 10)
- `--objective "..."`
- `--query "..."` (repeatable)
- `--excerpts` / `--no-excerpts`
- `--full-content` / `--no-full-content`
- `--excerpts-max-chars N` / `--excerpts-max-total-chars N`
- `--full-max-chars N`
- `--fetch-max-age-seconds N` (min 600 when set)
- `--fetch-timeout-seconds N`
- `--disable-cache-fallback`
- `--request path/to/request.json` (advanced)

### `scripts/parallel-search-extract.mjs`

Convenience pipeline:
1) Search
2) Extract the top N URLs from the search results (single Extract call)

Common flags:
- All `parallel-search.mjs` flags
- `--top N` (1–10)
- Extraction toggles: `--excerpts`, `--full-content`, plus the extract excerpt/full settings

## Output handling conventions

When turning API output into a user-facing answer:
- Prefer **official / primary sources** when possible.
- Quote or paraphrase **only** the relevant extracted text.
- Include **URL + publish_date** (when present) for transparency.
- If results disagree, present both and say what each source claims.

## Error handling

Scripts exit with:
- `0` success
- `1` unexpected error (network, JSON parse, etc.)
- `2` invalid arguments
- `3` API error (non-2xx) — response body is printed to stderr when possible

## References

Load these only when needed:
- `references/parallel-api.md` — compact API field/shape reference
- `references/openclaw-config.md` — OpenClaw config + sandbox env notes
- `references/prompting.md` — objective/query templates and research patterns


---
Source attribution: kbarbel640-del
Original project: https://github.com/kbarbel640-del/skills/blob/e7e336ac628c3e3bd771aff1aaa469c93778f94a/skills/tristanmanchester/parallel-ai-search/SKILL.md
License: MIT
License information: https://opensource.org/license/mit
Original upstream instructions; downloaded from Mega AI Hub.

## Upstream license notice

MIT License

Copyright (c) 2026 clawdbot

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.



# Mega AI Hub resource metadata

Creator / publisher: kbarbel640-del
Original project: https://github.com/kbarbel640-del/skills/blob/e7e336ac628c3e3bd771aff1aaa469c93778f94a/skills/tristanmanchester/parallel-ai-search/SKILL.md
Attribution basis: Source repository record

Fields of work: Data & Analytics, Education & Research, Marketing
Roles: Data Analyst, Marketing Analyst, Researcher
Specialties: API
Tools: Parallel Ai
Country applicability: Country not specified
Classification basis: Source catalog labels
Instruction SHA-256: 8e9fc9de4c5000284212f70926750cded083f8959a9b241a7f086520a3f72266