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Agent Skill · 研究检索

okra(okra 命令行)

okra 用于查找、检索和筛选相关信息,适合在 OpenClaw 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

okra 是研究检索类 Agent Skill,可用宿主为 OpenClaw,通过ClawHub获取,权限为接触敏感数据;许可证为 MIT-0,第三方扫描已通过,按安装量在研究检索类的 73,675 个 Skill 里排第 27,430。

总安装 3,387周安装 137GitHub Stars 公开资料未说明下载 1,063核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
MIT-0
版本
1.0.0

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:okra(okra 命令行)
来源仓库:https://github.com/steventsao/okra
安装命令:
openclaw skills install okra
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 OpenClaw 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

ClawHubOpenClaw
openclaw skills install okra

这个 Skill 是做什么的

研究检索类 Skill 主要解决「找资料、核对事实、把来源整理清楚」的问题,通常会让助手先检索再总结。

OkraPDF — 上传 PDF、阅读提取的内容、提出问题、提取结构化数据以及管理集合。涵盖 MCP、CLI 和 HTTP。

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 OpenClaw;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 openclaw skills install okra;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它需要接触敏感数据,先在测试目录或测试账号里跑,确认没问题再放到正式项目。

常见问题

6 条

okra 怎么安装?

在终端执行 openclaw skills install okra。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

okra 支持哪些 AI 工具?

公开资料标注支持 OpenClaw。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

okra 需要什么权限?

权限级别是接触敏感数据。该 Skill 可能接触密钥、Token、环境变量或敏感配置,应进入高风险复核队列,默认不自动发布。

okra 是免费的吗?

来源标注的许可证是 MIT-0。本站不代收任何费用,也不托管安装包;能否商用、要不要署名,以许可证原文为准。

okra 用的人多吗?

公开数据是累计安装 3,387 次,近一周安装 137 次,下载 1,063 次。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

okra 安全吗?

第三方扫描结果为通过,但这只覆盖已知的静态检查项,不能替代自己读一遍源码和安装命令。

值得选的地方和要先想清楚的地方

值得选的地方

  • 专注 OpenClaw 一种宿主,触发方式和使用习惯不用来回切换。
  • 许可证明确为 MIT-0,商用和分发边界好判断。
  • 第三方扫描已通过,常见的高风险行为没有命中。

先想清楚的地方

  • 需要接触敏感数据,装到常用环境前要先评估影响面。
  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 OpenClaw,想直接扩展它能力的人。
  • 需要处理研究检索类任务、又不想每次都手写提示词的人。
  • 需要许可证清楚、并且有第三方扫描结果的团队项目。

不太适合

  • 环境里有大量密钥、凭证,不方便给第三方 Skill 读取的人。
  • 需要厂商级技术支持和 SLA 的生产系统。

研究检索类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
find-skills

寻找技能 · Codex、Claude

31,199,81316,486操作浏览器MIT有提醒
self-improving-agent

自我改进 Agent · OpenClaw

16,464,0023,396接触敏感数据MIT-0通过
soultrace

灵魂追踪 · Codex、Claude

7,588,94412需要联网未明确有提醒
self-improving

自我完善 · OpenClaw

6,829,2841,046需要联网MIT-0通过
microsoft-foundry

微软代工厂 · Codex、Claude

6,614,144764调用外部服务未明确有提醒
okra

okra 命令行 · 本页

3,387—接触敏感数据MIT-0通过

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

name
okra
description
OkraPDF — upload PDFs, read extracted content, ask questions, extract structured data, and manage collections. Covers MCP, CLI, and HTTP.

OkraPDF

Upload a PDF, get an API. Extract tables, ask questions, get structured JSON — via MCP, CLI, or HTTP.

Designed for subagents. Every document is its own stateless endpoint. Fire off parallel queries to different documents — no shared state, no locks, no ordering issues. Ideal as a tool inside agent loops (Claude, GPT, custom orchestrators).

Setup

MCP (Claude Code, Cursor, OpenCode)

Add to ~/.claude/mcp.json or .cursor/mcp.json:

{
  "mcpServers": {
    "okra-pdf": {
      "type": "url",
      "url": "https://api.okrapdf.com/mcp",
      "headers": { "Authorization": "Bearer YOUR_API_KEY" }
    }
  }
}

CLI

npm install -g okrapdf
okra auth set-key YOUR_API_KEY

HTTP

All endpoints use https://api.okrapdf.com with header Authorization: Bearer $OKRA_API_KEY.

Get a free API key at okrapdf.com (Settings > API Keys).


Upload a PDF

CLI

okra extract invoice.pdf
okra extract https://arxiv.org/pdf/2307.09288
okra extract report.pdf --processor llamaparse
okra run report.pdf "What was total revenue?"     # upload + ask in one shot

Options: -o json|markdown|table, --processor, --tables-only, --text-only, -d <dir> (agentic workspace), -q (quiet, for piping)

MCP

upload_document(url: "https://example.com/report.pdf")
upload_document(url: "https://arxiv.org/pdf/2307.09288", wait: true)
upload_document(url: "https://example.com/invoice.pdf", page_images: "lazy")

Parameters: url (required), wait (default: true), document_id (optional), page_images (none/cover/lazy), processor

HTTP

# From URL
curl -X POST https://api.okrapdf.com/v1/documents \
  -H "Authorization: Bearer $OKRA_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"url": "https://arxiv.org/pdf/2307.09288"}'

# From file
curl -X POST https://api.okrapdf.com/v1/documents \
  -H "Authorization: Bearer $OKRA_API_KEY" \
  -F "file=@report.pdf" -F "page_images=cover"

Response: {"document_id": "doc-abc123", "phase": "extracting", "pages_total": 42}


Check Status

CLI

okra status doc-abc123

MCP

get_document_status(document_id: "doc-abc123")

HTTP

curl https://api.okrapdf.com/v1/documents/doc-abc123/status \
  -H "Authorization: Bearer $OKRA_API_KEY"

Response: {"phase": "complete", "page_count": 42, "total_nodes": 318}

Documents must reach phase: "complete" before reading/asking. Use wait: true on upload (MCP) or poll status.


Read Content

CLI

okra read doc-abc123
okra page get doc-abc123 1
okra toc doc-abc123
okra tree doc-abc123
okra search doc-abc123 "revenue"

MCP

read_document(document_id: "doc-abc123")
read_document(document_id: "doc-abc123", pages: "1-5")
read_document(document_id: "arxiv:2307.09288")

document_id accepts: doc-abc123, arxiv:2307.09288, or https://arxiv.org/pdf/2307.09288.

HTTP

# Full markdown
curl https://api.okrapdf.com/v1/documents/doc-abc123/full.md \
  -H "Authorization: Bearer $OKRA_API_KEY"

# Specific page
curl "https://api.okrapdf.com/v1/documents/doc-abc123/pages/3" \
  -H "Authorization: Bearer $OKRA_API_KEY"

# All pages as JSON
curl https://api.okrapdf.com/v1/documents/doc-abc123/pages \
  -H "Authorization: Bearer $OKRA_API_KEY"

Ask Questions

CLI

okra chat doc-abc123
okra chat send doc-abc123 -m "What are the key findings?"

MCP

ask_document(document_id: "doc-abc123", question: "What was total revenue in 2024?")

Returns answer with page citations (page number + supporting snippet).

HTTP (OpenAI-compatible)

curl -X POST https://api.okrapdf.com/document/doc-abc123/chat/completions \
  -H "Authorization: Bearer $OKRA_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "messages": [{"role": "user", "content": "What was total revenue in 2024?"}],
    "stream": false
  }'

Supports "stream": true for SSE streaming.


Extract Structured Data

CLI

okra extract report.pdf -o json -q | jq '.entities[] | select(.type == "table")'

MCP

extract_data(
  document_id: "doc-abc123",
  prompt: "Extract all line items from this invoice",
  json_schema: {
    "type": "object",
    "properties": {
      "line_items": {
        "type": "array",
        "items": {
          "type": "object",
          "properties": {
            "description": {"type": "string"},
            "quantity": {"type": "number"},
            "unit_price": {"type": "number"},
            "total": {"type": "number"}
          }
        }
      },
      "grand_total": {"type": "number"}
    }
  }
)

HTTP

curl -X POST https://api.okrapdf.com/document/doc-abc123/chat/completions \
  -H "Authorization: Bearer $OKRA_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "messages": [{"role": "user", "content": "Extract all line items"}],
    "response_format": {
      "type": "json_schema",
      "json_schema": {
        "name": "invoice",
        "schema": {
          "type": "object",
          "properties": {
            "line_items": {"type": "array", "items": {"type": "object", "properties": {"description": {"type": "string"}, "amount": {"type": "number"}}}},
            "total": {"type": "number"}
          }
        },
        "strict": true
      }
    },
    "stream": false
  }'

Tables and Entities

CLI

okra tables doc-abc123
okra tables get doc-abc123 table-0
okra entities list doc-abc123
okra entities images doc-abc123
okra query doc-abc123 "table:has(revenue)"

HTTP

curl https://api.okrapdf.com/v1/documents/doc-abc123/entities/tables \
  -H "Authorization: Bearer $OKRA_API_KEY"

curl https://api.okrapdf.com/v1/documents/doc-abc123/entities \
  -H "Authorization: Bearer $OKRA_API_KEY"

Collections

Group documents and query across all of them at once.

Create and manage

CLI:

okra collections create "Q4 Earnings" -d "Quarterly filings" --docs doc-abc123,doc-def456
okra collections list                          # or: okra col ls
okra collections show "Q4 Earnings"
okra collections add "Q4 Earnings" doc-ghi789
okra collections remove "Q4 Earnings" doc-abc123
okra collections delete "Q4 Earnings"

HTTP:

# Create with seed documents
curl -X POST https://api.okrapdf.com/v1/collections \
  -H "Authorization: Bearer $OKRA_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"name": "Q4 Earnings", "document_ids": ["doc-abc123", "doc-def456"]}'

# Add documents
curl -X POST https://api.okrapdf.com/v1/collections/col-xxx/documents \
  -H "Authorization: Bearer $OKRA_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"document_ids": ["doc-ghi789"]}'

# List / get / delete
curl https://api.okrapdf.com/v1/collections -H "Authorization: Bearer $OKRA_API_KEY"
curl https://api.okrapdf.com/v1/collections/col-xxx -H "Authorization: Bearer $OKRA_API_KEY"
curl -X DELETE https://api.okrapdf.com/v1/collections/col-xxx -H "Authorization: Bearer $OKRA_API_KEY"

Query across documents

Two modes:

ModeBehaviorBest for
fanout (default)Separate completion per document, NDJSON streamPer-document answers
sandboxSingle LLM with grep/Python over all docsCross-doc search, comparisons

CLI:

okra chat -c "Q4 Earnings" -m "Compare revenue across companies"
okra chat "compare revenue" --doc doc-abc123,doc-def456

HTTP (fanout):

curl -X POST https://api.okrapdf.com/v1/collections/col-xxx/query \
  -H "Authorization: Bearer $OKRA_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"query": "What was total revenue in Q4?"}'

Response (NDJSON):

{"type":"start","query_id":"...","doc_count":7}
{"type":"result","doc_id":"doc-xxx","answer":"..."}
{"type":"done","completed":7,"failed":0}

HTTP (sandbox):

curl -X POST https://api.okrapdf.com/v1/collections/col-xxx/query \
  -H "Authorization: Bearer $OKRA_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"prompt": "Compare R&D spending. Show a table.", "mode": "sandbox"}'

Export

# NDJSON stream
curl -N "https://api.okrapdf.com/v1/collections/col-xxx/export?format=markdown" \
  -H "Authorization: Bearer $OKRA_API_KEY"

# Zip archive
curl -L "https://api.okrapdf.com/v1/collections/col-xxx/export?format=zip" \
  -H "Authorization: Bearer $OKRA_API_KEY" -o collection.zip

Exports

# CLI
okra extract report.pdf -o json -q > report.json

# HTTP
curl https://api.okrapdf.com/exports/doc-abc123/markdown -H "Authorization: Bearer $OKRA_API_KEY"
curl -o report.xlsx https://api.okrapdf.com/exports/doc-abc123/excel -H "Authorization: Bearer $OKRA_API_KEY"
curl -o report.docx https://api.okrapdf.com/exports/doc-abc123/docx -H "Authorization: Bearer $OKRA_API_KEY"
curl https://api.okrapdf.com/exports/doc-abc123/snapshot -H "Authorization: Bearer $OKRA_API_KEY"

Page Images

Deterministic, CDN-cached URLs:

https://res.okrapdf.com/v1/documents/{id}/pg_1.png
https://res.okrapdf.com/v1/documents/{id}/w_400,h_300/pg_1.png

Document Management

CLI

okra list
okra read doc-abc123
okra status doc-abc123
okra delete doc-abc123
okra auth set-key YOUR_API_KEY
okra auth whoami

HTTP

curl "https://api.okrapdf.com/v1/documents?limit=20" -H "Authorization: Bearer $OKRA_API_KEY"

Subagent & Parallel Patterns

OkraPDF is built for agent-to-agent use. Each document is an isolated Durable Object with its own SQLite — queries to different documents never contend. Run them in parallel freely.

As tools inside an agent loop

Map each document to a callable tool. The orchestrating agent picks which docs to query:

import { createOkra } from '@okrapdf/runtime';

const okra = createOkra({ apiKey: process.env.OKRA_API_KEY! });

const docs = [
  { id: 'doc-abc123', label: 'NVIDIA 10-K' },
  { id: 'doc-def456', label: 'AMD 10-K' },
  { id: 'doc-ghi789', label: 'Intel 10-K' },
];

// Each doc becomes a tool the agent can call
const sessions = Object.fromEntries(
  docs.map((d) => [d.label, okra.sessions.from(d.id)]),
);

// Execute in parallel when agent calls multiple tools at once
const results = await Promise.all(
  toolCalls.map(tc => sessions[tc.name].prompt(tc.input.question))
);

Fan-out: same question across N documents

// SDK — fire all in parallel, collect answers
const question = 'What was total revenue and YoY growth?';
const answers = await Promise.all(
  docIds.map(id => okra.sessions.from(id).prompt(question))
);
# curl — parallel background requests
for doc_id in doc-abc123 doc-def456 doc-ghi789; do
  curl -s -X POST "https://api.okrapdf.com/document/$doc_id/chat/completions" \
    -H "Authorization: Bearer $OKRA_API_KEY" \
    -H "Content-Type: application/json" \
    -d "{\"messages\": [{\"role\": \"user\", \"content\": \"$question\"}], \"stream\": false}" &
done
wait
# CLI — multi-doc in one command
okra chat "compare revenue" --doc doc-abc123,doc-def456,doc-ghi789

MCP subagent pattern (Claude Code)

When Claude Code spawns subagents, each can independently call OkraPDF MCP tools:

# Subagent 1: ask_document(document_id: "doc-abc123", question: "What was revenue?")
# Subagent 2: ask_document(document_id: "doc-def456", question: "What was revenue?")
# Subagent 3: ask_document(document_id: "doc-ghi789", question: "What was revenue?")
# All run concurrently — no contention

When to use which pattern

PatternBest for
MCP toolsAgent picks which docs to query dynamically
SDK Promise.allYou know the doc set upfront, want max parallelism
Collections querySame question across a predefined group, server handles fan-out
okra chat --docQuick CLI comparison of 2-5 docs

Piping and Scripting

# Batch extract
for pdf in *.pdf; do okra extract "$pdf" -o json -q > "${pdf%.pdf}.json"; done

# Fan-out: same question to multiple docs in parallel
for doc_id in doc-abc123 doc-def456 doc-ghi789; do
  curl -s -X POST "https://api.okrapdf.com/document/$doc_id/chat/completions" \
    -H "Authorization: Bearer $OKRA_API_KEY" \
    -H "Content-Type: application/json" \
    -d "{\"messages\": [{\"role\": \"user\", \"content\": \"What was total revenue?\"}], \"stream\": false}" &
done
wait

Available Processors

ProcessorBest ForSpeed
textlayerNative PDFs with selectable textFast
llamaparseComplex layouts, mixed contentMedium
unstructuredGeneral purposeMedium
azure-diForms, invoices, receiptsMedium
docaiHigh-accuracy OCRSlow
geminiVision-based extractionMedium
qwenOpen-source VLM extractionMedium

Error Codes

StatusMeaning
202Accepted, processing async
400Bad request
401Missing or invalid API key
404Document not found
409Conflict (document exists)
429Rate limited

Links

平台分布

OpenClaw

95.91%
按下载量换算1,020

安全审计

VirusTotal

通过

ClawScan

通过

Static analysis

通过

权限和风险

接触敏感数据

该 Skill 可能接触密钥、Token、环境变量或敏感配置,应进入高风险复核队列,默认不自动发布。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。目前只有一个公开来源,建议同时核对源仓库。

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