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local-deep-research本地深度研究

Agent Skill

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。

总安装

9,759

周安装

391

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下载量

3,159
OpenClaw

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

MIT-0

最后核验

2026-05-01

来源状态

来源可访问

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

简介

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。

SKILL.md

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)

  • quickQuick Summary: fewer cycles, shorter output, faster. Use when the user wants a concise summary or a quick overview.
  • detailedDetailed 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

适合场景

01

OpenClaw 用户查找和安装 Skill 时

02

用户想查找某类 Agent Skill 时

03

需要根据任务场景推荐可安装能力包时

04

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

补充不同宿主或平台的使用分布数据

能力 5

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

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

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。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

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