by ARCANGEL0
Meet Mitsu, your chibi red-team operator. Connect your AI provider, set the scope, and she can take an authorized pentest from recon and attack-surface mapping through vulnerability discovery, > exploitation, and post-exploitation, armored with 165+ MCP-connected tools and live session intel. Guide every step or let her pwn autonomously. ( ◠‿◠)
# Add to your Claude Code skills
git clone https://github.com/ARCANGEL0/MitsuLast scanned: 10/23/2026
{
"issues": [],
"status": "PASSED",
"scannedAt": "2026-10-23T09:07:45.525Z",
"npmAuditRan": true,
"pipAuditRan": true,
"promptInjectionRan": true
}See how Mitsu compares with popular alternatives.
Mitsu is an open-source ai agents skill for AI coding assistants such as Claude Code, Codex CLI, and ChatGPT, built by ARCANGEL0. Meet Mitsu, your chibi red-team operator. Connect your AI provider, set the scope, and she can take an authorized pentest from recon and attack-surface mapping through vulnerability discovery, > exploitation, and post-exploitation, armored with 165+ MCP-connected tools and live session intel. Guide every step or let her pwn autonomously. ( ◠‿◠). It has 534 GitHub stars.
Yes. Mitsu passed SkillsLLM's automated security scan — a dependency vulnerability audit plus prompt-injection heuristics — with no high-severity issues. You can read the full report in the Security Report section on this page.
Clone the repository with "git clone https://github.com/ARCANGEL0/Mitsu" and add it to your Claude Code skills directory (see the Installation section above).
Mitsu is primarily written in Python. It is open-source under ARCANGEL0 on GitHub, so you can review or fork the full source.
Yes. SkillsLLM lists many other AI Agents skills you can browse and compare side by side. Open the AI Agents category from the badge at the top of this page, or use the Related Skills and comparison links further down to weigh Mitsu against similar tools.
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⚠️ Third-Party Software Notice
This skill is third-party open-source software developed and hosted independently on GitHub. SkillsLLM is an informational directory and does not control or maintain the underlying repository.
Any security checks, ratings, or warnings displayed by SkillsLLM are automated and limited in scope. They do not constitute a security certification or guarantee that the software is safe, error-free, or free from malicious code, vulnerabilities, compromised dependencies, or prompt-injection risks.
Review the source code, permissions, dependencies, and configuration before installing or running any third-party skill. Use is at your own risk. To the maximum extent permitted by applicable law, SkillsLLM is not liable for losses arising from third-party software.
Automated security scan — not a certification.
A little personality. A serious toolkit. A full engagement, one evidence-led step at a time.
Mitsu is your chibi red-team companion for authorized penetration tests, built to help carry an engagement from the first look at the attack surface through service enumeration, vulnerability research, validation, and access-path assessment. Her MCP server connects the model to Mitsu’s 165-tool Red Team Armory, custom command wrappers, and external MCP servers, so she can turn a plan into focused tool work and use the returned evidence to choose what to check next. When a requested utility is missing, she can offer to install it and wait for your approval.
The engagement’s Intel grows alongside the work: targets, IPs, domains, ports and services, vulnerability leads, confirmed findings, accounts, evidence, and observations all stay organized in session context. That gives Mitsu a record of what is known, what has been tried, and where a lead stopped fitting, rather than treating each turn as a fresh start. Sessions keep their own history and workspace, and the recorded results can feed summaries and attack-surface maps.
Pick the role you want her to take. Recon keeps work passive and operator-led; Guided pauses for your choices and lets you steer the next phase; AutoPwn can follow evidence-backed paths through an in-scope assessment with fewer interruptions. Mitsu supports multiple model providers, and you can change the provider, working directory, tools, scope, and autonomy to fit each engagement. She brings the chibi energy; you give her the sword to attack.
flowchart TD
A[Launch Mitsu] --> B{Choose a session}
B -->|New| C[Create session]
B -->|Resume| D[Load history and Intel]
C --> E[Select provider and model]
D --> E
E --> F[Set target, scope, mode, and workspace]
F --> G[Describe the assessment objective]
G --> H[Model reviews conversation and session Intel]
H --> I[Choose a focused next step]
I --> J{Need a tool, a detail, or a final answer?}
J -->|Question or choice| K[Show an interactive dialog]
K --> H
J -->|Final answer| Z[Summarize evidence and remaining gaps]
J -->|Tool| L{Mode and approval check}
L -->|Guided| M[Ask operator to choose the next action]
M -->|Continue| N[Run one focused check]
M -->|Redirect or stop| H
L -->|AutoPwn| N
L -->|Recon| O[Use permitted passive or read-only checks]
L -->|Approval required| P[Show approval or sudo dialog]
P -->|Approve| N
P -->|Decline| H
N --> Q[Run capability through MCP]
O --> Q
Q --> R[Review tool output and evidence]
R --> S[Update targets, services, IPs, domains, and observations]
S --> T{Relevant vulnerability lead?}
T -->|Yes| U[Check product, version, and prerequisites]
U --> V{Evidence supports applicability?}
V -->|No or unknown| W[Record uncertainty and choose another check]
V -->|Yes| X[Validate impact within scope]
X --> Y{Confirmed?}
Y -->|No| W
Y -->|Yes| AA[Record finding, severity, evidence, and references]
T -->|No| AB{Useful in-scope work remains?}
W --> AB
AA --> AB
AB -->|Yes| I
AB -->|No| Z
Z --> AC[Save session and review results]
AC --> AD[Generate report, map attack surface, export, or resume]
AE[Operator sends a new direction] -. interrupts active turn .-> H
Mitsu runs the selected model in a tool loop: it receives the session history and Intel, requests a tool when useful, and continues from the returned evidence. Recon, Guided, and AutoPwn apply different interaction and approval rules around that loop. A new operator message can redirect an active turn; recorded Intel keeps confirmed assets and findings alongside unresolved observations.
Mitsu requires Python 3.10 or newer. The GitHub installer uses uv to set up and manage its environment.
Install Mitsu with:
pip install mitsu
mitsu
git clone https://github.com/ARCANGEL0/Mitsu.git
cd Mitsu
./scripts/install.sh
Mitsu can identify missing tools or the local SearXNG container needed for a task and offer to install them with your approval.
From the Mitsu source directory:
uv tool install .
mitsu
To run directly from the checkout instead:
./mitsu
The first-run setup walks you through the essentials:
After that, you’re ready to start a session.
You can return to provider and model setup from the command center at any time. In a session, use /connect to change provider settings or /model to switch models. For a local Ollama server, make sure it is running and has a model available before connecting.
Switch modes with /mode. The mode controls how Mitsu interacts with targets and when she asks you to decide.
| Mode | What to expect |
|---|---|
| Recon | Passive, operator-led work such as public-source lookups, DNS research, and local read-only inspection. If the next useful step requires active interaction with a target, Mitsu opens a dialog to choos |