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DeepLink Agentic
by
ShirleyDDDD
· GitHub ↗
· v1.1.0
· MIT-0
135
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0
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0
Active Installs
3
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Install in OpenClaw
/install realestate-deep-research
Description
深度智联 Agentic 智能体 Skill。让用户告别碎片化查询，由 AI 智能体统筹复杂的房地产行业研究任务。支持房地产市场分析、土地研判、房地产企业分析、房地产项目案例分析、房地产项目设计建议、政策解读、物业行业资讯（日/周/月报）、物业行业招投标监测等房地产行业研究任务的创建、进度查询、成果获取与迭代优化...
Usage Guidance
This skill appears to do what it says: it forwards user queries and any user-uploaded files to agentic.dichanai.com using AGENTIC_TOKEN. Before installing, confirm you trust agentic.dichanai.com. Avoid uploading highly sensitive data unless you understand that it will be transmitted to that service. Be aware the token-renewal step prints a NEW_TOKEN value to the terminal — ensure your agent or workflow will not accidentally share that output with others or with the user. Prefer using a least-privilege or short-lived AGENTIC_TOKEN if possible, and rotate the token if you suspect it was exposed. Finally, note the update instructions which can replace local skill files from the remote host — keep that trust boundary in mind because compromised updates could change behavior.
Capability Analysis
Type: OpenClaw Skill
Name: realestate-deep-research
Version: 1.1.0
The skill bundle is a functional integration for the 'Agentic' real estate research platform (agentic.dichanai.com). The primary component is a Python CLI script (agentic.py) that interacts with a documented API to create research tasks, manage files, and handle token lifecycle. While the script includes a token renewal feature that prints new credentials to stdout (potentially risky if the agent leaks terminal output), the SKILL.md instructions explicitly warn the AI agent to keep this data private. The code is well-structured, lacks obfuscation, and its behaviors (file uploads, network requests to the primary domain) are consistent with its stated purpose of performing deep industry research.
Capability Assessment
Purpose & Capability
Name/description match implementation: the skill's CLI and SKILL.md talk to agentic.dichanai.com for creating/monitoring research tasks. Required binary (python3), the requests dependency, and a single AGENTIC_TOKEN are all expected for this purpose.
Instruction Scope
Instructions explicitly instruct the agent to send user queries and any user-uploaded files to agentic.dichanai.com and to use the provided CLI. The SKILL.md requires the agent to inform the user that data will be sent to agentic.dichanai.com (good). It also directs the agent to upload files and to poll for HITL states. This behavior is coherent, but it means user data and uploads will be transmitted to the remote platform (declared in the doc).
Install Mechanism
Install spec only pulls in the 'requests' package (reasonable). The skill also instructs manual update via fetching a .skill file from the platform; that implies a remote update path (host: agentic.dichanai.com). This is expected for a hosted service but poses the usual update risk: if the remote update source is compromised, updated skill files could change behavior.
Credentials
Only AGENTIC_TOKEN is required and used as the primary credential, which is proportionate. Two caution points: (1) the token renewal flow prints NEW_TOKEN to stdout and asks the operator/agent to update AGENTIC_TOKEN — if the agent forwards terminal output to users, the token could be leaked; (2) token scope is unspecified — a broadly privileged token would increase risk. Both are operational considerations rather than inconsistencies.
Persistence & Privilege
always is false and the skill does not request system-wide privileges or alter other skills. Autonomous invocation is allowed (platform default) but not combined here with other suspicious privileges.
How to Use
- Make sure OpenClaw is installed (local or Docker)
- Run the install command in chat:
/install realestate-deep-research - After installation, invoke the skill by name or use
/realestate-deep-research - Provide required inputs per the skill's parameter spec and get structured output
Version History
v1.1.0
Version 1.1.0
- 明确说明 `poll` 命令不会自动处理中途出现的 HITL（人类介入）状态，需人工判断并中断。
- 优化 HITL 轮询说明，强调需主动转交用户，避免遗漏。
- 对使用流程做了小幅调整，强化了任务状态和用户指导方面的细节说明。
- 其余文本与命令未发生重大变动。
v1.0.1
Version 1.1.0 introduces HITL（Human-in-the-Loop）支持及安全更新：
- 新增 HITL 流程说明：任务遇到 hitl 状态时必须中止自动流程，主动向用户转述问题，获取意见后再提交，确保人机协作可控。
- 增加数据交互与隐私提示，明确文件传输范围及数据保护承诺。
- 安全提示：Token 自动续期不得在用户侧泄露，敏感信息只应内部更新。
- 标准工作流、命令用法说明全面更新，同步增强 demo 和环境/依赖说明。
- 新增依赖声明、元数据、环境变量与 requests 包自动安装指令，提升易用性。
v1.0.0
Initial release of realestate-deep-research Skill:
- Enables users to manage complex real estate research tasks through an AI agent tailored for the industry.
- Supports creation, progress tracking, result retrieval, and iterative refinement of tasks such as market analysis, land evaluation, enterprise research, policy interpretation, industry news, and bidding monitoring.
- Guides users in clarifying and structuring their research requirements before submission.
- All operations are performed via a unified script interface with detailed subcommands.
- Integrates token management and version update checking for secure and reliable access.
- Reminds users of point consumption on the platform before creating tasks.
- Provides comprehensive reference and best practices for task creation, file management, and status tracking.
Metadata
Frequently Asked Questions
What is DeepLink Agentic?
深度智联 Agentic 智能体 Skill。让用户告别碎片化查询，由 AI 智能体统筹复杂的房地产行业研究任务。支持房地产市场分析、土地研判、房地产企业分析、房地产项目案例分析、房地产项目设计建议、政策解读、物业行业资讯（日/周/月报）、物业行业招投标监测等房地产行业研究任务的创建、进度查询、成果获取与迭代优化... It is an AI Agent Skill for Claude Code / OpenClaw, with 135 downloads so far.
How do I install DeepLink Agentic?
Run "/install realestate-deep-research" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.
Is DeepLink Agentic free?
Yes, DeepLink Agentic is completely free, licensed under MIT-0. You can download, install and use it at no cost.
Which platforms does DeepLink Agentic support?
DeepLink Agentic is cross-platform and runs anywhere OpenClaw / Claude Code is available (cross-platform).
Who created DeepLink Agentic?
It is built and maintained by ShirleyDDDD (@shirleydddd); the current version is v1.1.0.
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