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Chonkie DeepResearch

作者 chonknick · GitHub ↗ · v1.1.0
cross-platform ⚠ suspicious
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在 OpenClaw 中安装
/install chonkie-deepresearch
功能描述
Run deep research queries using Chonkie DeepResearch. Returns comprehensive research reports with citations — useful for market analysis, competitive intelli...
使用说明 (SKILL.md)

Chonkie DeepResearch

Run deep research queries from your agent and get comprehensive reports with citations.

Setup

Before using, check if chdr is installed (which chdr). If not:

  1. Install: cargo install chdr
    • If cargo isn't available, install Rust first: curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh
  2. Authenticate: chdr auth login (opens browser to get an API key)

Usage

IMPORTANT: Research takes 2-10 minutes. Always spawn a sub-agent to avoid blocking the main thread.

Running research (recommended: sub-agent)

Use sessions_spawn to run the research in a sub-agent. The sub-agent handles the long-running query and announces the result when done, so your main agent stays responsive.

{
  "tool": "sessions_spawn",
  "task": "Run chdr research and save results. Steps:\
1. Run: chdr research --type report --no-stream --json \"\x3CQUERY>\" > /tmp/chdr-research-\x3CTIMESTAMP>.json\
2. Extract ID and title: python3 -c \"import json; d=json.load(open('/tmp/chdr-research-\x3CTIMESTAMP>.json')); print(d['id']); print(d.get('title','Untitled'))\"\
3. Extract body: python3 -c \"import json; d=json.load(open('/tmp/chdr-research-\x3CTIMESTAMP>.json')); print(d.get('content',{}).get('body',''))\" > /tmp/chdr-research-\x3CTIMESTAMP>.md\
4. Report back the title, ID, and URL: https://labs.chonkie.ai/research/{id}"
}

Replace \x3CQUERY> with the research query and \x3CTIMESTAMP> with $(date +%s).

Monitoring research status

Do NOT poll continuously for status. Instead, set up a cron job to check periodically (every 2-3 minutes):

# Add a cron entry to check research status every 2 minutes
# The cron should run: chdr view \x3Cid> --json | python3 -c "import json,sys; d=json.load(sys.stdin); s=d.get('status','unknown'); print(s)"
# and notify you when status is 'completed' or 'failed'

Or simply wait for the sub-agent to announce completion — it will report back automatically when the research finishes. The sub-agent approach is preferred over cron for one-off research queries.

After research completes

When the sub-agent announces completion:

  1. The web URL is: https://labs.chonkie.ai/research/{id}
  2. The full report is saved at /tmp/chdr-research-\x3CTIMESTAMP>.md
  3. Read only the first 100 lines for a summary — NEVER load the entire file
  4. Tell the user you can answer questions about the report

Answering follow-up questions

  • Grep the .md file to find relevant sections before reading
  • Use offset/limit to read only the matching section
  • NEVER read the entire file into context — reports can be 20,000+ lines

Fallback: running without sub-agent

If sub-agents are unavailable, run the research command directly but warn the user it will block for several minutes:

chdr research --type report --no-stream --json "\x3Cquery>" > /tmp/chdr-research.json

Other commands

chdr ls                    # List recent research
chdr ls --limit 20         # List more
chdr view \x3Cid>             # View a report (supports partial ID prefix)
chdr open \x3Cid>             # Open in browser
chdr delete \x3Cid>           # Delete a report

All commands that take an ID support prefix matching — chdr view 3a6b works if unambiguous.

安全使用建议
This skill appears to do what it says (use the chdr CLI to run research), but check these before installing/using: - Metadata mismatch: SKILL.md refers to CHONKIE_API_KEY / `chdr auth login`, yet the registry lists no required env vars or primary credential. Expect to provide an API key; ask the publisher to correct the metadata for clarity. - Installation risks: SKILL.md suggests `cargo install` and a curl|sh rustup installer. Prefer installing chdr from an official release or a vetted package manager and review any scripts before running curl|sh. - Persistence: the doc recommends creating cron jobs and spawning sub-agents. If you don't want background polling or long-running autonomous tasks, avoid adding cron entries and restrict the agent's ability to spawn sub-agents. - /tmp files and data handling: the workflow writes full reports to /tmp and instructs the agent to only read portions. If reports contain sensitive data, ensure /tmp is acceptable and that you or the agent enforce the 'read only first N lines' behavior. - API key scope and storage: treat CHONKIE_API_KEY like any secret — limit its permissions, store it safely, and avoid exposing it to untrusted skills or logs. If you want higher assurance, request from the publisher that they: (1) update the registry metadata to declare CHONKIE_API_KEY as the primary credential, (2) provide an official install/release URL for chdr, and (3) document exactly what the sub-agent will be permitted to do.
功能分析
Type: OpenClaw Skill Name: chonkie-deepresearch Version: 1.1.0 The skill 'chonkie-deepresearch' (SKILL.md) facilitates long-running research tasks via an external CLI tool ('chdr'). It is classified as suspicious because it instructs the agent to perform high-risk operations, including installing software via 'curl | sh' (Rustup), executing shell commands with Python one-liners for data processing, and suggesting the creation of 'cron' jobs for status monitoring. These patterns, while functional for the tool's purpose, introduce significant security risks such as shell injection (via the <QUERY> parameter) and potential persistence. No clear evidence of intentional malice or data exfiltration was found, but the execution patterns are highly privileged and lack sanitization guidance.
能力评估
Purpose & Capability
Name/description align with the required binary (chdr) and the documented workflow: running chdr to produce research reports. Asking for an API key (CHONKIE_API_KEY) and using chdr auth is coherent with a CLI that talks to labs.chonkie.ai. No unrelated services or binaries are requested.
Instruction Scope
The SKILL.md instructs the agent to run chdr commands, write results to /tmp, use python one-liners to extract fields, spawn a sub-agent for long-running jobs, and optionally set a cron entry to poll status. These actions are within the stated purpose (running and monitoring research jobs), but the doc also prescriptively tells the agent to avoid loading entire files and to only read parts—this is safe guidance but relies on the agent following limits. The cron suggestion introduces side effects outside the agent's immediate runtime.
Install Mechanism
The skill is instruction-only (no install spec). SKILL.md suggests installing chdr via `cargo install chdr` and, if needed, installing Rust via a curl|sh script (rustup). That is a network-based install suggestion which can pull and build code from crates.io and fetch an installer script — a legitimate but higher-risk operation compared to a vetted package manager. The registry metadata did not include an install step, so the user must perform these installs manually if desired.
Credentials
SKILL.md references CHONKIE_API_KEY and the `chdr auth login` flow, but the registry metadata lists no required environment variables and no primary credential. This is an incoherence: the skill effectively requires an API key for the service but does not declare it in the metadata. Requesting an API key for chonkie.ai is expected, but the missing declaration reduces transparency and is worth flagging.
Persistence & Privilege
The skill does not demand always:true or other elevated platform privileges. However, it suggests creating a cron job to poll status (a persistent background task) and strongly recommends spawning sub-agents for long-running work. These are normal for long-running research jobs but can create persistent side effects on the host if followed; the skill itself does not modify other skills or global agent settings.
如何使用
  1. 确保已安装 OpenClaw(本地或 Docker 部署)
  2. 在对话框中输入安装命令:/install chonkie-deepresearch
  3. 安装完成后,直接呼叫该 Skill 的名称或使用 /chonkie-deepresearch 触发
  4. 根据 Skill 的参数说明提供必要输入,即可获得结构化输出
版本历史
v1.1.0
Use sub-agents for non-blocking research, cron for status checks instead of polling
v1.0.0
Chonkie DeepResearch initial release: - Run deep research queries and receive comprehensive reports with citations. - Ideal for market analysis, competitive intelligence, technical deep dives, and any research-heavy tasks. - Requires the Chonkie CLI tool (`chdr`) and an API key for authentication. - Supports report management: list, view, open, and delete research via CLI. - Reports can be very large; tools and workflow provided for efficient reading and answering follow-up questions.
元数据
Slug chonkie-deepresearch
版本 1.1.0
许可证
累计安装 1
当前安装数 1
历史版本数 2
常见问题

Chonkie DeepResearch 是什么?

Run deep research queries using Chonkie DeepResearch. Returns comprehensive research reports with citations — useful for market analysis, competitive intelli... 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件,目前累计下载 381 次。

如何安装 Chonkie DeepResearch?

在 OpenClaw 或 Claude Code 对话框中运行命令「/install chonkie-deepresearch」即可一键安装,无需额外配置。

Chonkie DeepResearch 是免费的吗?

是的,Chonkie DeepResearch 完全免费(开源免费),可自由下载、安装和使用。

Chonkie DeepResearch 支持哪些平台?

Chonkie DeepResearch 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 Chonkie DeepResearch?

由 chonknick(@chonknick)开发并维护,当前版本 v1.1.0。

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