Agent skill

Web Research Parallel

by gooseworks-ai in gooseworks-ai/goose-skills

Web research API with OpenAI-compatible chat completions and async tasks

MITAuto-check passedAI & LLM Engineering

Install Web Research Parallel

skills CLI
$ npx skills add gooseworks-ai/goose-skills --skill web-research-parallel -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install gooseworks-ai/goose-skills web-research-parallel --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/gooseworks-ai/goose-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/research-tools/capabilities/web-research-parallel .claude/skills/web-research-parallel && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
web-research-parallel
GitHub stars
1.2k
Used in
1 other repo
Token cost
~3k tokens
SKILL.md length
1,085 words
Files
2
Skills in repo
273
Repo updated
First seen
Licence
MIT

At a glance

Web research API with OpenAI-compatible chat completions and async tasks

  • Works in 4 steps: Research Automation: Get comprehensive… → OpenAI Drop-in: Use with existing OpenAI… → Competitive Analysis: Research… → …
  • Tasks that involve LLM API integration
  • SKILL.md covers Setup, Capabilities, Usage and Use Cases, plus 1 more section
  • Calls curl, python3 and npx; reaches api.gooseworks.ai; needs GOOSEWORKS_API_KEY

What it does

Web Research Parallel is an agent skill from gooseworks-ai/goose-skills. Web research API with OpenAI-compatible chat completions and async tasks

Its SKILL.md is about 3k tokens, which your agent loads only when the skill is triggered. The skill folder holds 1 other file (for example `skill.meta.json`).

It sits in AI & LLM Engineering, covering LLM API integration. It works with OpenAI. The repository describes itself as: Library of Growth & GTM skills + data APIs for Claude Code, Codex, Cursor to run ads, social, content, lead gen, seo and data scraping. The licence is MIT.

When your agent uses it

  • Tasks that involve LLM API integration

Example prompts

  • “/web-research-parallel”

Requirements

  • Python 3
  • Node.js
  • A credential in GOOSEWORKS_API_KEY

Workflow steps

4 steps, taken from the first numbered list in SKILL.md.

  1. Research Automation: Get comprehensive research on any topic
  2. OpenAI Drop-in: Use with existing OpenAI SDK code
  3. Competitive Analysis: Research competitors and market
  4. Due Diligence: Gather information for investment decisions

What it can do on your machine

Read from SKILL.md and the folder at commit c650c6d. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Shell commands in SKILL.md call:

    • curl
    • python3
    • npx

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    Hosts in commands or code, which the agent is likely to contact:

    • api.gooseworks.ai

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names these keys or tokens, usually read from environment variables:

    • GOOSEWORKS_API_KEY

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Web Research Parallel loads about 3k tokens when it runs. Until then it costs about 24 tokens; SKILL.md has 1,085 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~24
When it runs · the whole SKILL.md, loaded when a task matches
~3k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from gooseworks-ai/goose-skills at commit c650c6d, republished under its MIT licence (© gooseworks-ai). 1,085 words, ~3,005 tokens.

Download SKILL.mdSave it as .claude/skills/web-research-parallel/SKILL.md (or your agent's skills folder). This skill also uses 1 other file; get the full folder from GitHub.
name
web-research-parallel
description
Web research API with OpenAI-compatible chat completions and async tasks
source
orthogonal

Parallel - Web Research API

Setup

Read your credentials from ~/.gooseworks/credentials.json:

bash
export GOOSEWORKS_API_KEY=$(python3 -c "import json;print(json.load(open('$HOME/.gooseworks/credentials.json'))['api_key'])")
export GOOSEWORKS_API_BASE=$(python3 -c "import json;print(json.load(open('$HOME/.gooseworks/credentials.json')).get('api_base','https://api.gooseworks.ai'))")

If ~/.gooseworks/credentials.json does not exist, tell the user to run: npx gooseworks login

All endpoints use Bearer auth: -H "Authorization: Bearer $GOOSEWORKS_API_KEY"

Web research API that returns OpenAI ChatCompletions-compatible responses.

Capabilities

  • Retrieve FindAll Run Status: Retrieve a FindAll run (free)
  • FindAll Run Result: Retrieve the FindAll run result at the time of the request (free)
  • Cancel FindAll Run: Cancel a FindAll run (free)
  • Retrieve Task Run Input: Retrieves the input of a run by run_id (free)
  • Retrieve Task Run: Retrieves run status by run_id (free)
  • Ingest FindAll Run: Transforms a natural language search objective into a structured FindAll spec
  • Retrieve Task Run Result: Retrieves a run result by run_id, blocking until the run is completed (free)
  • Create FindAll Run: Starts a FindAll run
  • Search: Searches the web
  • Chat API: Parallel Chat is a web research API that returns OpenAI ChatCompletions compatible streaming text and JSON
  • Extract: Extracts relevant content from specific web URLs
  • Create Task Run: Initiates a task run

Usage

Retrieve FindAll Run Status (free)

Retrieve a FindAll run.

bash
curl -s -X POST $GOOSEWORKS_API_BASE/v1/proxy/orthogonal/run \
  -H "Authorization: Bearer $GOOSEWORKS_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"api":"parallel","path":"/v1beta/findall/runs/{findall_id}"}'
FindAll Run Result (free)

Retrieve the FindAll run result at the time of the request.

bash
curl -s -X POST $GOOSEWORKS_API_BASE/v1/proxy/orthogonal/run \
  -H "Authorization: Bearer $GOOSEWORKS_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"api":"parallel","path":"/v1beta/findall/runs/{findall_id}/result"}'
Cancel FindAll Run (free)

Cancel a FindAll run.

bash
curl -s -X POST $GOOSEWORKS_API_BASE/v1/proxy/orthogonal/run \
  -H "Authorization: Bearer $GOOSEWORKS_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"api":"parallel","path":"/v1beta/findall/runs/{findall_id}/cancel"}'
Retrieve Task Run Input (free)

Retrieves the input of a run by run_id.

bash
curl -s -X POST $GOOSEWORKS_API_BASE/v1/proxy/orthogonal/run \
  -H "Authorization: Bearer $GOOSEWORKS_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"api":"parallel","path":"/v1/tasks/runs/{run_id}/input"}'
Retrieve Task Run (free)

Retrieves run status by run_id.The run result is available from the /result endpoint.

bash
curl -s -X POST $GOOSEWORKS_API_BASE/v1/proxy/orthogonal/run \
  -H "Authorization: Bearer $GOOSEWORKS_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"api":"parallel","path":"/v1/tasks/runs/{run_id}"}'
Ingest FindAll Run

Transforms a natural language search objective into a structured FindAll spec.Note: Access to this endpoint requires the parallel-beta header.The generated specification serves as a suggested starting point and can be furthercustomized by the user.

Parameters:

  • objective* (string) - Input model for FindAll ingest.
bash
curl -s -X POST $GOOSEWORKS_API_BASE/v1/proxy/orthogonal/run \
  -H "Authorization: Bearer $GOOSEWORKS_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"api":"parallel","path":"/v1beta/findall/ingest","body":{"objective":"Find all AI startups in San Francisco"}}'
Retrieve Task Run Result (free)

Retrieves a run result by run_id, blocking until the run is completed.

Parameters:

  • timeout (integer)
bash
curl -s -X POST $GOOSEWORKS_API_BASE/v1/proxy/orthogonal/run \
  -H "Authorization: Bearer $GOOSEWORKS_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"api":"parallel","path":"/v1/tasks/runs/{run_id}/result"}'
Create FindAll Run

Starts a FindAll run.This endpoint immediately returns a FindAll run object with status set to ‘queued’.You can get the run result snapshot using the GET /v1beta/findall/runs//result endpoint.You can track the progress of the run by:Polling the status using the GET /v1beta/findall/runs/ endpoint,Subscribing to real-time updates via the /v1beta/findall/runs//eventsendpoint,Or specifying a webhook with relevant event types during run creation to receivenotifications.

Parameters:

  • objective* (string) - Input model for FindAll run.
  • entity_type* (string) - Type of the entity for the FindAll run.
  • match_conditions* (MatchCondition · object[]) - List of match conditions for the FindAll run.
  • generator* (enum<string>) - Generator for the FindAll run. One of base, core, pro, preview.
  • match_limit* (integer) - Maximum number of matches to find for this FindAll run. Must be between 5 and 1000 (inclusive).
  • exclude_list (ExcludeCandidate · object[] | null) - List of entity names/IDs to exclude from results.
  • metadata (Metadata · object) - Metadata for the FindAll run.
  • webhook (Webhook · object) - Webhook for the FindAll run.
bash
curl -s -X POST $GOOSEWORKS_API_BASE/v1/proxy/orthogonal/run \
  -H "Authorization: Bearer $GOOSEWORKS_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"api":"parallel","path":"/v1beta/findall/runs"}'
  "goal": "Find all AI startups in San Francisco"
}'

Searches the web.To access this endpoint, pass the parallel-beta header with the valuesearch-extract-2025-10-10.

Parameters:

  • mode (enum<string> | null) - Request to Search API.
  • objective (string | null) - Natural-language description of what the web search is trying to find. May include guidance about preferred sources or freshness. At least one of objective or search_queries must be provided.
  • search_queries (string[] | null) - Optional list of traditional keyword search queries to guide the search. May contain search operators. At least one of objective or search_queries must be provided.
  • processor (enum<string> | null) - DEPRECATED: use mode instead.
  • max_results (integer | null) - Upper bound on the number of results to return. May be limited by the processor. Defaults to 10 if not provided.
  • max_chars_per_result (integer | null) - DEPRECATED: Use excerpts.max_chars_per_result instead.
  • excerpts (object) - Optional settings to configure excerpt generation.
  • source_policy (object) - Optional source policy governing domain and date preferences in search results.
  • fetch_policy (object) - Fetch policy: determines when to return cached content from the index (faster) vs fetching live content (fresher). Default is to disable live fetch and return cached content from the index.
bash
curl -s -X POST $GOOSEWORKS_API_BASE/v1/proxy/orthogonal/run \
  -H "Authorization: Bearer $GOOSEWORKS_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"api":"parallel","path":"/v1beta/search","body":{"objective":"AI agent frameworks comparison 2024"}}'
Chat API

Parallel Chat is a web research API that returns OpenAI ChatCompletions compatible streaming text and JSON.

Parameters:

  • model (string)
  • messages (array)
  • stream (boolean)
  • response_format (object)
bash
curl -s -X POST $GOOSEWORKS_API_BASE/v1/proxy/orthogonal/run \
  -H "Authorization: Bearer $GOOSEWORKS_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"api":"parallel","path":"/chat/completions"}'
  "model": "parallel",
  "messages": [
    {"role": "user", "content": "What are the latest developments in quantum computing?"}
  ]
}'
Show full SKILL.md (435 more words)Show less
Extract

Extracts relevant content from specific web URLs.To access this endpoint, pass the parallel-beta header with the valuesearch-extract-2025-10-10.

Parameters:

  • urls* (string[]) - Extract request.
  • objective (string) - If provided, focuses extracted content on the specified search objective.
  • search_queries (string[]) - If provided, focuses extracted content on the specified keyword search queries.
  • fetch_policy (object) - Fetch policy: determines when to return cached content from the index (faster) vs fetching live content (fresher). Default is to use a dynamic policy based on the search objective and url.
  • excerpts (boolean) - Include excerpts from each URL relevant to the search objective and queries. Note that if neither objective nor search_queries is provided, excerpts are redundant with full content. default:true
  • full_content (boolean) - Include full content from each URL. Note that if neither objective nor search_queries is provided, excerpts are redundant with full content. default:false
bash
curl -s -X POST $GOOSEWORKS_API_BASE/v1/proxy/orthogonal/run \
  -H "Authorization: Bearer $GOOSEWORKS_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"api":"parallel","path":"/v1beta/extract","body":{"urls":["https://example.com/article"]}}'
Create Task Run

Initiates a task run.Returns immediately with a run object in status ‘queued’.Beta features can be enabled by setting the ‘parallel-beta’ header.

Parameters:

  • processor* (string) - Task run input with additional beta fields.
  • input* (string) - Input to the task, either text or a JSON object.
  • metadata (object) - User-provided metadata stored with the run. Keys and values must be strings with a maximum length of 16 and 512 characters respectively.
  • source_policy (object) - Optional source policy governing preferred and disallowed domains in web search results.
  • task_spec (object) - Task specification. If unspecified, defaults to auto output schema.
  • mcp_servers (object[]) - Optional list of MCP servers to use for the run.To enable this feature in your requests, specify mcp-server-2025-07-17 as one of the values in parallel-beta header (for API calls) or betas param (for the SDKs).
  • enable_events (boolean) - Controls tracking of task run execution progress. When set to true, progress events are recorded and can be accessed via the Task Run events endpoint. When false, no progress events are tracked. Note that progress tracking cannot be enabled after a run has been created. The flag is set to true by default for premium processors (pro and above).To enable this feature in your requests, specify events-sse-2025-07-24 as one of the values in parallel-beta header (for API calls) or betas param (for the SDKs).
  • webhook (object) - Callback URL (webhook endpoint) that will receive an HTTP POST when the run completes.This feature is not available via the Python SDK. To enable this feature in your API requests, specify the parallel-beta header with webhook-2025-08-12 value.
bash
curl -s -X POST $GOOSEWORKS_API_BASE/v1/proxy/orthogonal/run \
  -H "Authorization: Bearer $GOOSEWORKS_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"api":"parallel","path":"/v1/tasks/runs"}'
  "processor": "base",
  "input": "Research the competitive landscape of AI coding assistants"
}'

Use Cases

  1. Research Automation: Get comprehensive research on any topic
  2. OpenAI Drop-in: Use with existing OpenAI SDK code
  3. Competitive Analysis: Research competitors and market
  4. Due Diligence: Gather information for investment decisions

Discover More

For full endpoint details and parameters:

bash
curl -s -X POST $GOOSEWORKS_API_BASE/v1/proxy/orthogonal/search \
  -H "Authorization: Bearer $GOOSEWORKS_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"prompt":"parallel API endpoints"}' List all endpoints
curl -s -X POST $GOOSEWORKS_API_BASE/v1/proxy/orthogonal/details \
  -H "Authorization: Bearer $GOOSEWORKS_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"api":"parallel","path":"/v1beta/findall"}'   # Get endpoint details

© gooseworks-ai, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 1 other file in skills/research-tools/capabilities/web-research-parallel of gooseworks-ai/goose-skills.

  • SKILL.md
  • skill.meta.json

Open the folder on GitHubat commit c650c6d

Used in 1 other repository

We found 1 copy of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in gooseworks-ai/goose-skills, which our catalogue first saw on October 9, 2026.

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Instructor Structured LLM OutputsOrchestra-Research/AI-Research-SKILLs13k6 repos~4.2kAutomated safety check: PassMIT
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Works with

Questions about Web Research Parallel

What does Web Research Parallel do?

Web research API with OpenAI-compatible chat completions and async tasks. Web Research Parallel is an agent skill from gooseworks-ai/goose-skills.

When should I use Web Research Parallel?

Web Research Parallel fits situations like: tasks that involve LLM API integration.

How do I install Web Research Parallel in Claude Code?

Run `npx skills add gooseworks-ai/goose-skills --skill web-research-parallel -a claude-code`. Or copy the skill folder (skills/research-tools/capabilities/web-research-parallel in gooseworks-ai/goose-skills) into .claude/skills/web-research-parallel in your project. Claude Code loads it when a task matches its description.

How do I install Web Research Parallel in Codex?

Run `npx skills add gooseworks-ai/goose-skills --skill web-research-parallel -a codex`. Or copy the skill folder (skills/research-tools/capabilities/web-research-parallel in gooseworks-ai/goose-skills) into .agents/skills/web-research-parallel in your project. Codex loads it when a task matches its description.

Can I use Web Research Parallel in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add gooseworks-ai/goose-skills --skill web-research-parallel -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/web-research-parallel, .gemini/skills/web-research-parallel, .github/skills/web-research-parallel and .opencode/skills/web-research-parallel in your project.

What does Web Research Parallel need to run?

Going by SKILL.md and its folder, Web Research Parallel needs the command-line tools its instructions call (curl, python3 and npx) and credentials named GOOSEWORKS_API_KEY. Our summary lists: Python 3; Node.js; A credential in GOOSEWORKS_API_KEY.

Does Web Research Parallel access the network?

SKILL.md names 1 domain. In commands or code: api.gooseworks.ai; the agent is likely to contact it when it follows the instructions. This is read from the text; nothing was executed.

Is Web Research Parallel safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Web Research Parallel use?

Web Research Parallel is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Web Research Parallel use?

About 3k tokens (SKILL.md is roughly 12k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Web Research Parallel?

Skills that share tags, products or a category with Web Research Parallel: Dingo Verify (MigoXLab/dingo, 757 stars), Agnes Free Text (kangarooking/agnes-free-model-skills, 199 stars), Openai Docs (theowenyoung/home, 115 stars) and Instructor Structured LLM Outputs (Orchestra-Research/AI-Research-SKILLs, 13k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Web Research Parallel?

gooseworks-ai (a GitHub organization) maintains it in gooseworks-ai/goose-skills, which has 1,240 GitHub stars. The repository holds 273 skills in this directory. The repository was last updated on October 8, 2026.

Source: gooseworks-ai/goose-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.