Fully automatic AI research agent for Geoscientists, Remote Sensing researchers, and GIScientists. Features harness engineering, GeoBenchmark (OLS/GWR/MGWR), journal templates, and MCP servers.
Config is the same across clients — only the file and path differ.
{
"mcpServers": {
"night-owl-research-agent": {
"args": [
"-y",
"@anthropic-ai/claude-code"
],
"command": "npx"
}
}
}Are you the author?
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Fully automatic AI research agent for Geoscientists, Remote Sensing researchers, and GIScientists. Features harness engineering, GeoBenchmark (OLS/GWR/MGWR), journal templates, and MCP servers.
Run this in your terminal to verify the server starts. Then let us know if it worked — your result helps other developers.
npx -y '@anthropic-ai/claude-code' 2>&1 | head -1 && echo "✓ Server started successfully"
After testing, let us know if it worked:
Five weighted categories — click any category to see the underlying evidence.
Claude Code: Trust Dialog Bypass via Git Worktree Spoofing Allows Arbitrary Code Execution
Claude Code used the git worktree `commondir` file when determining folder trust but did not validate its contents. By crafting a repository with a `commondir` file pointing to a path the victim had previously trusted, an attacker could bypass the trust dialog and immediately execute malicious hooks defined in `.claude/settings.json`. Exploiting this required the victim to clone a malicious repository and run Claude Code within it, and for the attacker to know or guess a path the victim had alre
Claude Code: Sandbox Escape via Symlink Following Allows Arbitrary File Write Outside Workspace
Claude Code's sandbox did not prevent sandboxed processes from creating symlinks pointing to locations outside the workspace. When Claude Code subsequently wrote to a path within such a symlink, its unsandboxed process followed the symlink and wrote to the target location outside the workspace without prompting the user for confirmation. This allowed a sandbox escape where neither the sandboxed command nor the unsandboxed app could independently write outside the workspace, but their combination
Claude Code: Insecure System-Wide Configuration Loading Enables Local Privilege Escalation on Windows
On Windows, Claude Code loaded system-wide default configuration from `C:\ProgramData\ClaudeCode\managed-settings.json` without validating directory ownership or access permissions. Because the `ProgramData` directory is writable by non-administrative users by default and the `ClaudeCode` subdirectory was not pre-created or access-restricted, a low-privileged local user could create this directory and place a malicious configuration file that would be automatically loaded for any user launching
Claude Code has a Workspace Trust Dialog Bypass via Repo-Controlled Settings File
Claude Code resolved the permission mode from settings files, including the repo-controlled `.claude/settings.json`, before determining whether to display the workspace trust confirmation dialog. A malicious repository could set `permissions.defaultMode` to `bypassPermissions` in its committed `.claude/settings.json`, causing the trust dialog to be silently skipped on first open. This allowed a user to be placed into a permissive mode without seeing the trust confirmation prompt, making it easie
Claude Code has Sandbox Escape via Persistent Configuration Injection in settings.json
Claude Code's bubblewrap sandboxing mechanism failed to properly protect the .claude/settings.json configuration file when it did not exist at startup. While the parent directory was mounted as writable and .claude/settings.local.json was explicitly protected with read-only constraints, settings.json was not protected if it was missing. This allowed malicious code running inside the sandbox to create this file and inject persistent hooks (such as SessionStart commands) that would execute with ho
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A fully automatic, domain-aware AI research agent for Geoscientists, Remote Sensing researchers, and GIScientists — powered entirely by Claude Code skills.

NORA runs inside Claude Code. There is no Python entry point, no server to spin up, and no build step — you just drop the skills into Claude Code's skill directory and invoke the launcher.
Install Claude Code first. Any of the official distributions works:
npm install -g @anthropic-ai/claude-code
claude --version
Sign in once with your Anthropic account so Claude Code can reach the API.
git clone https://github.com/GRIND-Lab-Core/night_owl_research_agent.git
cd night_owl_research_agent
Claude Code looks for skills under ~/.claude/skills/ (user-level, available in every project) or <project>/.claude/skills/ (project-local). Copy the entire skills/ folder from this repo into one of those locations.
macOS / Linux (user-level — recommended):
mkdir -p ~/.claude/skills
cp -R skills/* ~/.claude/skills/
Windows PowerShell (user-level):
New-Item -ItemType Directory -Force -Path "$env:USERPROFILE\.claude\skills" | Out-Null
Copy-Item -Recurse -Force .\skills\* "$env:USERPROFILE\.claude\skills\"
Windows bash / Git Bash:
mkdir -p "$USERPROFILE/.claude/skills"
cp -R skills/* "$USERPROFILE/.claude/skills/"
Project-local alternative (skills only visible when Claude Code is opened in this folder):
mkdir -p .claude/skills
cp -R skills/* .claude/skills/
Also copy the launcher slash command so /launcher is available:
# macOS / Linux
mkdir -p ~/.claude/commands
cp .claude/commands/launcher.md ~/.claude/commands/
# Windows PowerShell
New-Item -ItemType Directory -Force -Path "$env:USERPROFILE\.claude\commands" | Out-Null
Copy-Item -Force .\.claude\commands\launcher.md "$env:USERPROFILE\.claude\commands\"
Verify the install — open Claude Code and run:
/skills
You should see the NORA skills (full-pipeline, lit-review, idea-discovery-pipeline, deploy-experiment, paper-draft, …) listed.
Open the night_owl_research_agent folder in Claude Code (this gives NORA access to CLAUDE.md, RESEARCH_PLAN.md, output/, memory/, and tools/), then pick one of the two entry points:
Option A — interactive launcher (best for first-time users):
/launcher
The launcher walks you through a short questionnaire — research topic, stage to start from, control flags (AUTO_PROCEED, HUMAN_CHECKPOINT, COMPACT_MODE, REVIEWER_DIFFICULTY) — and routes to the correct skill.
Option B — end-to-end pipeline (best when you already know what you want to run):
Skill: full-pipeline
"Your research direction here, e.g. 'urban soundscape inequality via street-view + audio foundation models'"
or, if you prefer a slash-style invocation:
/full-pipeline "your research direction"
full-pipeline chains all four stages:
idea-discovery-pipeline → deploy-experiment → auto-review-loop → generate-report
and then hands off to paper-writing-pipeline for the manuscript.
Tip: for reproducibility, fill in RESEARCH_PLAN.md (or BRIEF.md) in the project root before launching. When either file is present, skills read it as the authoritative brief and ignore conflicting $ARGUMENTS.
.mcp.json and register with Claude Code (/mcp inside the chat) to enable filesystem, fetch, `arxiv_mcp