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Gemini Deep Research (J-claw)
by
Skywalker326
· GitHub ↗
· v1.0.0
· MIT-0
113
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Install in OpenClaw
/install gemini-deep-research-jclaw
Description
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...
README (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:
geminiCLI installed (npm install -g @google/gemini-cli)gemini-deep-researchextension 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 pathstatus: "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
Usage Guidance
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.
Capability Analysis
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).
Capability Assessment
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.
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.
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.
How to Use
- Make sure OpenClaw is installed (local or Docker)
- Run the install command in chat:
/install gemini-deep-research-jclaw - After installation, invoke the skill by name or use
/gemini-deep-research-jclaw - Provide required inputs per the skill's parameter spec and get structured output
Version History
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.
Metadata
Frequently Asked Questions
What is Gemini Deep Research (J-claw)?
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... It is an AI Agent Skill for Claude Code / OpenClaw, with 113 downloads so far.
How do I install Gemini Deep Research (J-claw)?
Run "/install gemini-deep-research-jclaw" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.
Is Gemini Deep Research (J-claw) free?
Yes, Gemini Deep Research (J-claw) is completely free, licensed under MIT-0. You can download, install and use it at no cost.
Which platforms does Gemini Deep Research (J-claw) support?
Gemini Deep Research (J-claw) is cross-platform and runs anywhere OpenClaw / Claude Code is available (cross-platform).
Who created Gemini Deep Research (J-claw)?
It is built and maintained by Skywalker326 (@skywalker-lili); the current version is v1.0.0.
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