Parallel FindAll
/install parallel-findall
Parallel FindAll
Discover entities matching a natural-language description. FindAll is for "give me a list of things that match this criteria" tasks — companies, people, products, papers, anything enumerable. Returns structured entities, not webpages or prose.
When to Use
Trigger this skill when the user asks for:
- "find all [X]", "list every [Y] that…"
- "discover [companies / people / products] matching…"
- "give me a list of [SaaS in vertical Z / YC W24 dev tools / Series A AI startups]"
- Bulk entity discovery where the answer is a structured list, not a narrative
Use Search for "what is X?"; use FindAll for "list every X that matches Y".
Quick Start
parallel-cli findall run "AI startups that raised Series A in 2026" --json
CLI Reference
Basic Usage
parallel-cli findall run "\x3Cobjective>" [options]
parallel-cli findall status frun_xxx --json
parallel-cli findall poll frun_xxx --json
parallel-cli findall result frun_xxx --json
parallel-cli findall extend frun_xxx \x3Cn> --json
parallel-cli findall cancel frun_xxx
Common Flags
| Flag | Description |
|---|---|
-g, --generator |
Generator tier: preview, base, core (default), pro |
-n, --match-limit |
Max matched candidates, 5-1000 (default: 10) |
--exclude '\x3Cjson>' |
Entities to exclude (JSON array of {name, url}) |
--no-wait |
Return immediately with frun_xxx; poll separately |
--dry-run |
Preview the schema FindAll will use, without creating a run |
--json |
Output as JSON |
-o, --output \x3Cfile> |
Save full result to file |
Examples
Basic discovery:
parallel-cli findall run "Find roofing companies in Charlotte NC" --json
Larger result set with core generator:
parallel-cli findall run "AI startups that raised Series A in 2026" -n 50 --json
pro tier for deeper coverage:
parallel-cli findall run "YC W24 dev tools companies" -g pro -n 100 --json
Preview the schema before running:
parallel-cli findall run "Find Series B fintechs in Latin America" --dry-run --json
Exclude already-known entities:
parallel-cli findall run "Find AI startups in healthcare" \
--exclude '[{"name": "Hippocratic AI", "url": "hippocraticai.com"}]' \
--json
Async — launch then poll separately:
parallel-cli findall run "AI startups that raised Series A in 2026" --no-wait --json
# returns frun_xxx
parallel-cli findall status frun_xxx --json
parallel-cli findall poll frun_xxx --json # waits and returns result
Extend an existing run with more matches:
parallel-cli findall extend frun_xxx 50 --json
Cancel a running job:
parallel-cli findall cancel frun_xxx
Best-Practice Prompting
Objective
1-3 sentences describing:
- The entity type (companies / people / products / papers / etc.)
- The matching criteria (vertical, geography, time range, status)
- Any quality filters ("active", "publicly listed", "open-source")
Generator Tier
preview— fast scan, low coverage. Useful only for quick sanity checks.base— broad and fast, but noisy (query echoes, no-URL entries, category placeholders). Use only when the user explicitly accepts noise.core(default) — best balance of coverage and quality.pro— deeper coverage, slower. Use for high-stakes discovery where missing matches is costly.
Match Limit
Pick the smallest -n that satisfies the user's intent. Default 10 is fine for "show me a few"; bump to 50–100 for "list every X". Max 1000.
Response Format
findall run returns JSON with:
findall_id—frun_xxxstatus—running/completed/cancelled/failedschema— the JSON Schema FindAll built for the matchesmatches[]— array of entities, each with fields per the schema (typicallyname,url, plus skill-specific extracted fields)
Output Handling
When relaying matches to the user:
- Filter noise: drop entries with empty
url, query-echo names, or category placeholders. Especially important on-g base. - Group by source domain if the result set is large — helps the user spot duplicate sources.
- Echo
findall_idso the user canextendorcancellater.
Running Out of Context?
For large result sets (-n ≥ 50), save and use sessions_spawn:
parallel-cli findall run "\x3Cobjective>" -n 100 --json -o /tmp/findall-\x3Ctopic>.json
Then spawn a sub-agent:
{
"tool": "sessions_spawn",
"task": "Read /tmp/findall-\x3Ctopic>.json and produce a deduplicated table grouped by category.",
"label": "findall-summary"
}
Error Handling
| Exit Code | Meaning |
|---|---|
| 0 | Success |
| 1 | Unexpected error (network, parse) |
| 2 | Invalid arguments |
| 3 | API error (non-2xx) |
Prerequisites
Requires parallel-cli (installed and authenticated). If parallel-cli --version fails, or if a later command fails with an authentication error, tell the user to see https://docs.parallel.ai/integrations/cli and stop.
References
- 确保已安装 OpenClaw(本地或 Docker 部署)
- 在对话框中输入安装命令:
/install parallel-findall - 安装完成后,直接呼叫该 Skill 的名称或使用
/parallel-findall触发 - 根据 Skill 的参数说明提供必要输入,即可获得结构化输出
Parallel FindAll 是什么?
Discover entities (companies, people, products) matching a natural-language description via the Parallel FindAll API. Use when the user wants to 'find all X'... 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件,目前累计下载 31 次。
如何安装 Parallel FindAll?
在 OpenClaw 或 Claude Code 对话框中运行命令「/install parallel-findall」即可一键安装,无需额外配置。
Parallel FindAll 是免费的吗?
是的,Parallel FindAll 完全免费,采用 MIT-0 许可证,可自由下载、安装和使用。
Parallel FindAll 支持哪些平台?
Parallel FindAll 跨平台运行,可在任意部署了 OpenClaw / Claude Code 的环境中使用(cross-platform)。
谁开发了 Parallel FindAll?
由 NormallyGaussian(@normallygaussian)开发并维护,当前版本 v1.0.3。