Token导航 LogoToken导航

Agent Skill · 研究检索

local-deep-research(本地深度研究)

local-deep-research 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。

local-deep-research 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 13,022。

总安装 9,759周安装 391GitHub Stars 公开资料未说明下载 3,159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
1.0.2

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:local-deep-research(本地深度研究)
来源仓库:https://github.com/eplt/local-deep-research
安装命令:
openclaw skills install local-deep-research
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 local-deep-research

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。

使用本地 LDR 服务对复杂主题进行多周期、迭代的深入研究,提供带有引文和来源跟踪的详细报告。

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 local-deep-research;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

local-deep-research 怎么安装?

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

local-deep-research 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。

local-deep-research 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。

local-deep-research 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。

local-deep-research 用的人多吗?

公开数据是累计安装 9,759 次,近一周安装 391 次,下载 3,159 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。

local-deep-research 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
local-deep-research

本地深度研究 · 本页

9,759—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
local-deep-research
description
Multi-cycle deep research using locally-hosted LDR (Local Deep Research) service. Use when user asks for comprehensive research with citations, literature reviews, competitive intelligence, or any research requiring exhaustive web search with iterative question generation. Triggers on: "deep research", "research this topic", "comprehensive analysis with sources", "literature review", "investigate [topic]", "quick summary on [topic]", "detailed report on [topic]", "research in Spanish/French/etc", or when academic-deep-research is requested but using local LDR instance.
version
1.0.2
homepage
https://github.com/eplt/local-deep-research-skill
metadata
openclaw
emoji
🔬
requires
bins
env
primaryEnv
LDR_BASE_URL
files

Local Deep Research Skill

This skill interfaces with a locally-hosted LDR (Local Deep Research) service to perform multi-cycle, iterative research with full citations and source tracking.

What to consider before installing

  • LDR service: The script talks only to the URL in LDR_BASE_URL (default http://127.0.0.1:5000). Only point it at an LDR instance you control. Do not set it to an unknown or untrusted remote host.
  • Required binaries: Ensure curl and jq are installed on the host where the skill runs.
  • Credentials: If your LDR instance requires login, set LDR_SERVICE_USER and LDR_SERVICE_PASSWORD (or LDR_USERNAME/LDR_PASSWORD) via environment variables or a local .env file only. Use a dedicated, low-privilege LDR account (e.g. openclaw_service). Do not store secrets in committed config or in the skill directory.
  • Sourced .env: The script optionally sources ~/.config/local_deep_research/config/.env if that file exists. That file may expose any variables it contains to the script. Verify the contents of that path before use; do not place unrelated secrets there.
  • Review the script: The script performs form-based session+CSRF login and uses an ephemeral cookie jar. It does not send data to any endpoint other than the configured LDR service. You can review scripts/ldr-research.sh before use. For higher assurance, run it in an isolated environment (e.g. container or VM) with network restricted to your LDR host.

Configuration

Credentials (local-only, never transmitted)

LDR uses session-cookie auth with CSRF protection (not HTTP Basic Auth). The skill script performs a proper login flow: GET login page → obtain session cookie and CSRF token → POST credentials + CSRF → reuse session cookie (and CSRF for POSTs) for all API calls. Username and password are used only to create a session with your local LDR instance; they are never sent to ClawHub, GitHub, or any other server.

Do not put credentials in skill config or committed files. Use environment variables or a local .env file only (e.g. LDR_SERVICE_USER, LDR_SERVICE_PASSWORD, or LDR_USERNAME/LDR_PASSWORD). Optional: LDR’s ~/.config/local_deep_research/config/.env is sourced by the script if present. Use a dedicated LDR user (e.g. openclaw_service) for this skill.

All configuration options

  • LDR_BASE_URL — LDR service URL (default: http://127.0.0.1:5000)
  • LDR_LOGIN_URL — Login page URL for session + CSRF (default: $LDR_BASE_URL/auth/login)
  • LDR_SERVICE_USER or LDR_USERNAME — LDR account username (local auth only)
  • LDR_SERVICE_PASSWORD or LDR_PASSWORD — LDR account password (local auth only)
  • LDR_DEFAULT_MODE — Default research mode: quick (Quick Summary) or detailed (Detailed Report) (default: detailed)
  • LDR_DEFAULT_LANGUAGE — Default output language code for report/summary (e.g. en, es, fr, de, zh, ja); empty = LDR default
  • LDR_DEFAULT_SEARCH_TOOL — Default search tool: searxng, auto, local_all (default: auto)

Research modes (Quick Summary vs Detailed Report)

  • quick — Quick Summary: fewer cycles, shorter output, faster. Use when the user wants a concise summary or a quick overview.
  • detailed — Detailed Report: full multi-cycle research, full markdown report, full citations and sources. Use when the user wants comprehensive analysis, literature review, or in-depth coverage.

Actions

start_research

Fire-and-forget: submit a query to LDR and return a research ID immediately.

Inputs:

  • query (required) — The research question or topic
  • mode (optional) — quick (Quick Summary) or detailed (Detailed Report) (default from config)
  • language (optional) — Output language for the report/summary, e.g. en, es, fr, de, zh, ja (default from config or LDR default)
  • search_tool (optional) — searxng, auto, local_all (default from config)
  • iterations (optional) — Number of research cycles (default: LDR's default)
  • questions_per_iteration (optional) — Questions to generate per cycle

Returns:

{
  "research_id": "uuid-string",
  "mode": "detailed",
  "search_tool": "auto",
  "submitted_at": "2026-03-10T08:00:00Z",
  "status": "queued"
}

Usage:

# Quick Summary (faster, shorter)
scripts/ldr-research.sh start_research --query "Solid-state battery advances" --mode quick

# Detailed Report with output in Spanish
scripts/ldr-research.sh start_research \
  --query "What are the latest developments in solid-state batteries?" \
  --mode detailed \
  --language es \
  --search_tool searxng

get_status

Check the status of a research job.

Inputs:

  • research_id (required) — The research job ID from start_research

Returns:

{
  "research_id": "uuid-string",
  "state": "pending|running|completed|failed|timeout",
  "progress": 45,
  "message": "Synthesizing sources from iteration 2...",
  "last_milestone": "Generated 12 questions from 8 sources"
}

Usage:

scripts/ldr-research.sh get_status --research_id <uuid>

get_result

Fetch the complete research report once finished.

Inputs:

  • research_id (required) — The research job ID

Returns:

{
  "research_id": "uuid-string",
  "query": "original query",
  "mode": "detailed",
  "summary": "executive summary text",
  "report_markdown": "full markdown report",
  "sources": [
    {
      "id": 1,
      "title": "Source Title",
      "url": "https://example.com",
      "snippet": "relevant excerpt",
      "type": "web|local_doc"
    }
  ],
  "iterations": 3,
  "created_at": "2026-03-10T08:00:00Z",
  "completed_at": "2026-03-10T08:15:00Z"
}

Usage:

scripts/ldr-research.sh get_result --research_id <uuid>

Orchestration Pattern

One-shot (wait for completion)

For interactive sessions where the user can wait:

  1. Call start_research
  2. Poll get_status every 10-30 seconds
  3. When state == "completed", call get_result
  4. Present the report to the user

Async (fire-and-forget with follow-up)

For background processing:

  1. Call start_research, return the research_id to the user
  2. User can check status later with get_status --research_id <id>
  3. When ready, call get_result to fetch the complete report

Chained workflows

After research completes:

  1. Call get_result to get sources
  2. Pass sources to other skills (e.g., markdown-converter, summarize)
  3. Build RAG indexes or knowledge bases from the sources

Error Handling

start_research failures

  • HTTP/network errors — Retry with exponential backoff (3 attempts)
  • LDR validation errors — Return error to user (bad query, invalid params)
  • Auth failures — Check credentials, return clear error

get_status / get_result failures

  • Temporarily unavailable — Retry 2-3 times before surfacing error
  • Research not found — Return "unknown research_id" error
  • Timeout — Return state with timeout reason

Timeouts

  • Per HTTP request — 30-60 seconds (configurable)
  • Total research duration — No client-side limit (LDR manages this)
  • Status polling interval — 10-30 seconds recommended

Example Session

User: "Research the latest developments in quantum computing"

Assistant: Starting deep research with LDR...
→ start_research(query="latest developments in quantum computing", mode="detailed")
→ Returns: research_id="abc-123", status="queued"

Assistant: Research started (ID: abc-123). This will take ~5-10 minutes.
I'll check the progress and let you know when it's complete.

[After polling...]

Assistant: Research complete! Here's what I found:

## Summary
[summary from get_result]

## Full Report
[report_markdown from get_result]

## Sources (12 found)
1. [Source 1 title](url)
2. [Source 2 title](url)
...

Related Skills

  • academic-deep-research — Alternative for academic-focused research with APA 7th citations
  • deep-research-pro — Web-based deep research (no local LDR required)
  • tavily / searxng — Simple web search for quick lookups
  • summarize — Process LDR output for additional summarization

Troubleshooting

LDR service not responding

  1. Check LDR_BASE_URL is correct
  2. Verify LDR service is running: curl http://127.0.0.1:5000/health
  3. Check LDR logs for errors

Authentication failures

  1. Ensure credentials are set via env or local .env only (e.g. LDR_SERVICE_USER, LDR_SERVICE_PASSWORD), not in committed config.
  2. LDR uses session + CSRF (not Basic Auth). The script GETs the login page, extracts the CSRF token, then POSTs the login form. If LDR uses a different login path or field names, set LDR_LOGIN_URL or see the script’s login section.
  3. Test: run the script with credentials set and check for "Login successful"; or open LDR_LOGIN_URL in a browser and sign in there to verify LDR is up.

Research stuck in "running" state

  1. Check LDR service health
  2. Review LDR logs for stuck jobs
  3. Consider timeout and restart if >30 minutes with no progress

平台分布

OpenClaw

97.44%
按下载量换算3,078

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。

继续浏览同类 Skills