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Gemini Deep Research (J-claw)

Name: Gemini Deep Research (J-claw)
Author: skywalker-lili

作者 Skywalker326 · GitHub ↗ · v1.0.0 · MIT-0

cross-platform ⚠ suspicious

113

总下载

当前安装

版本数

在 OpenClaw 中安装

/install gemini-deep-research-jclaw

功能描述

Gemini Deep Research via the gemini-cli deep-research MCP extension. Use when user wants to research a topic deeply, run market/industry analysis, or generat...

使用说明 (SKILL.md)

Gemini Deep Research

Executes a full Deep Research workflow using the official gemini-deep-research MCP extension.

Prerequisites

See references/setup-guide.md for full setup instructions. The skill assumes:

gemini CLI installed (npm install -g @google/gemini-cli)
gemini-deep-research extension installed and enabled
Paid Google AI API key configured via gemini extensions config gemini-deep-research

If any prerequisite is missing, inform the user and link to references/setup-guide.md.

Workflow

Step 1 — Confirm Intent & Parameters

Ask the user to confirm:

Research topic (exact wording goes to Gemini — make it clear and specific)
Report format — present as a choice:
- Comprehensive Research Report (default, most thorough)
- Executive Brief (concise, ~1-2 pages)
- Technical Deep Dive (detailed, technical audience)
Save path — default: ~/ObsidianVault/Default/DeepResearch/\x3CYYYYMMDD>-\x3Cslug>.md

If user does not specify format, use Comprehensive Research Report.

If user does not specify a save path, use the default and inform them.

Step 2 — Build the Command

node \x3Cskill>/scripts/dr-client.js \
  --input "\x3Cuser's research topic>" \
  --output "\x3Cfull save path>" \
  --format "\x3Cchosen format>" \
  --timeout 900000

\x3Cskill> = the skill's scripts directory (resolved by the agent)
Timeout defaults to 15 minutes (900,000 ms)

Step 3 — Spawn as Background Sub-agent

Use sessions_spawn with runtime: "subagent" to run the script in the background so the main session remains responsive.

Pass all parameters (input, output, format, timeout) via the task string.

Monitor completion via the sub-agent's completion event.

Step 4 — Handle Result

Parse the JSON output from the script:

status: "completed" → Report the success to the user with the file path
status: "error" → Report the error message to the user with a suggestion to check the API key or extension setup

Step 5 — Notify

Send a Discord message (via current session reply) confirming:

Topic researched
File path
Format used
Any errors if applicable

Report Format Reference

Format	Description
`Comprehensive Research Report`	Full multi-section report with analysis, data, and citations
`Executive Brief`	Condensed summary for decision-makers, ~1-2 pages
`Technical Deep Dive`	Detailed technical analysis, suited for specialists

The format is passed as-is to the Gemini API — it serves as a style hint, not a strict guarantee.

Error Handling

Error	Likely Cause	Resolution
`API key not found`	Key not configured	Guide user to `references/setup-guide.md` step 4
`429 Too Many Requests`	Free-tier key used or quota exceeded	Requires paid key — inform user
`Research timed out`	Took > 15 minutes	Retry or shorten query
MCP server spawn failed	Extension not installed or path wrong	Verify `~/.gemini/extensions/gemini-deep-research/` exists

File Naming

Default pattern: YYYYMMDD-\x3Cslug>.md

YYYYMMDD = today's date
\x3Cslug> = sanitized version of the research topic (lowercase, spaces to hyphens, strip special chars)
Example: 20260325-iran-hormuz-strait-market-impact.md

安全使用建议

This skill appears to implement its stated purpose, but proceed with caution: - Verify the third‑party gemini-deep-research extension on the referenced GitHub repo before installing (check the author, recent commits, issues, and code). The setup step installs that repo and enables auto-update — a future update could change behavior. - Confirm how your Google AI API key is stored and used. The setup guide says the extension stores the key in the system keychain, yet the client reads ~/.gemini/extensions/gemini-deep-research/.env and passes those env values to the spawned MCP process; avoid placing secrets in plaintext .env files and prefer keychain-based config if available. - Be aware the tool will write files to whatever output path you choose (default: your Obsidian vault). Use a safe output directory if you do not want research results stored in your personal notes or to risk overwriting files. - If you have sensitive queries or secrets, review the extension's code (especially the MCP server dist/index.js) to ensure it does not transmit data to unexpected endpoints. Consider running the extension and this skill in a sandboxed environment or on a disposable account until you trust the extension. - If you want greater assurance, ask the publisher for the extension's provenance and a signed release, or run the MCP server locally and inspect network activity during a test run.

功能分析

Type: OpenClaw Skill Name: gemini-deep-research-jclaw Version: 1.0.0 The skill is a legitimate wrapper for the Gemini Deep Research MCP extension, providing a workflow for the OpenClaw agent to perform automated research. The core logic in `scripts/dr-client.js` communicates with a local MCP server using standard JSON-RPC to manage research tasks, while `SKILL.md` provides clear instructions for background execution via sub-agents. No evidence of data exfiltration, malicious execution, or prompt injection was found; the skill correctly directs users to install official prerequisites from a plausible GitHub repository (allenhutchison/gemini-cli-deep-research).

能力评估

ℹ Purpose & Capability

The skill's name/description align with the code: it runs a local gemini-deep-research MCP server to perform long-running research requests and save a report. The JavaScript client spawns the extension's dist/index.js and polls it via MCP commands, which is coherent with the stated purpose. Minor inconsistency: the setup guide says the extension stores the API key in the system keychain (and that GEMINI_API_KEY env var is stripped), yet the shipped client explicitly reads an extension .env file and merges it into the spawned process environment — this mismatch should be clarified.

ℹ Instruction Scope

SKILL.md instructs the agent to run the provided node script as a background sub-agent and to save reports to a user-chosen path (defaulting to an ObsidianVault path). The script reads ~/.gemini/extensions/gemini-deep-research/.env (if present), spawns the MCP server, polls status, and writes the report file. It does not attempt to enumerate unrelated system files or exfiltrate data to external endpoints in the skill itself. However, because it will read an extension .env and write into the user's filesystem by default, users should be aware of potential sensitive-data exposure or overwriting of local files.

ℹ Install Mechanism

The skill is instruction-only (no install spec). The setup guide requires installing @google/gemini-cli (official) and installing the deep-research extension from a GitHub URL with --auto-update. Installing a third‑party extension from GitHub (auto-update enabled) is a moderate-risk action—it pulls and runs external code (the extension's dist/index.js) on your system and can change over time via updates. The skill code itself does not embed remote downloads, but it depends on that external install.

⚠ Credentials

The skill does not request unrelated credentials, and its need for a paid Google AI API key matches the stated use. However, the client loads a .env file from the extension directory and merges those variables into the MCP server's environment when spawning it. If that .env contains secrets (API keys, tokens), they will be passed to the spawned process. This conflicts with the setup guide's claim that the CLI stores the key in the system keychain and strips KEY envvars from MCP processes — the mismatch is a potential secret‑handling risk and should be reconciled. Also note the default save path writes into a local Obsidian vault by default, which may be unexpected for some users.

✓ Persistence & Privilege

The skill does not request always:true, does not modify other skills, and only runs on explicit invocation (or when the agent chooses to invoke it). It spawns a background sub-agent for long-running work which is consistent with the stated workflow and is not itself an elevated privilege.

如何使用

确保已安装 OpenClaw（本地或 Docker 部署）
在对话框中输入安装命令：/install gemini-deep-research-jclaw
安装完成后，直接呼叫该 Skill 的名称或使用 /gemini-deep-research-jclaw 触发
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

v1.0.0

Initial release: MCP-based Gemini deep research skill with full workflow. Supports Comprehensive Research Report, Executive Brief, and Technical Deep Dive formats. Outputs to Obsidian.

元数据

Slug gemini-deep-research-jclaw

版本 1.0.0

许可证 MIT-0

累计安装 0

当前安装数 0

历史版本数 1

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