Decepticon is an autonomous red-team agent that coordinates AI agents, security tools, sandboxes, and supporting services for authorized cybersecurity assessments. Security researchers and red teams can run it through its Docker stack, cloud service, command-line interface, or Python SDK, with the catalogue entries representing its available skills.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add PurpleAILAB/Decepticon --skill scattered-spidergit clone --depth 1 https://github.com/PurpleAILAB/DecepticonWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/purpleailab/decepticon/scattered-spider)<a href="https://agentmods.dev/skills/purpleailab/decepticon/scattered-spider"><img src="https://agentmods.dev/badge/skills/purpleailab/decepticon/scattered-spider/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/purpleailab/decepticon/scattered-spider"><img src="https://agentmods.dev/badge/skills/purpleailab/decepticon/scattered-spider.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 5 findings, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high YARA Match · line 3 YARA rule matched a hack tool or exploit indicator (offensive tools, reconnaissance, privilege escalation, or exploit frameworks).Fix: Remove offensive tool references and exploit code. Legitimate agent skills should not contain penetration testing tools, exploit frameworks, or reconnaissance utilities.
- high YARA Match · line 44 YARA rule matched a hack tool or exploit indicator (offensive tools, reconnaissance, privilege escalation, or exploit frameworks).Fix: Remove offensive tool references and exploit code. Legitimate agent skills should not contain penetration testing tools, exploit frameworks, or reconnaissance utilities.
- high YARA Match · line 71 YARA rule matched a known malware signature (reverse shell, backdoor, ransomware, C2 framework, or info stealer).Fix: Remove the malware payload or compromised file entirely. Investigate how it entered the skill and audit all other artifacts for additional indicators of compromise.
- high Privilege Escalation · line 82 Code accesses credential files (SSH keys, AWS credentials, etc.). This could indicate credential theft attempts.Fix: Remove references to credential paths. Use environment variables or secrets managers. For docs, use placeholder paths (e.g., /path/to/config). Never load .env or token files in production code paths.
- high YARA Match · line 131 YARA rule matched a hack tool or exploit indicator (offensive tools, reconnaissance, privilege escalation, or exploit frameworks).Fix: Remove offensive tool references and exploit code. Legitimate agent skills should not contain penetration testing tools, exploit frameworks, or reconnaissance utilities.
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00049 | $0.05548 |
| Opus 5 | $0.00024 | $0.02774 |
| Sonnet 5 | $0.00010 | $0.01110 |
| Haiku 4.5 | $0.00005 | $0.00555 |
Grade A, and why
scattered-spider scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 10d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 171 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Scattered Spider (UNC3944, Octo Tempest, Muddled Libra, Star Fraud) — Adversary Emulation Profile
Scattered Spider (MITRE ATT&CK G1015) is a financially motivated, predominantly native English-speaking cybercriminal collective active since at least 2022 and widely linked to the loosely affiliated "The Com" social network. The group is exceptional not for novel malware but for aggressive, high-tempo social engineering of human identity processes: it phones and SMS-phishes help desks and employees, impersonates IT staff, bypasses MFA (push bombing, SIM swapping, attacker-registered tokens), and rapidly pivots into cloud identity (Microsoft Entra ID, Okta, AWS, Azure) and virtualization (VMware ESXi/vCenter) before staging data theft and ransomware. It began with the 2022 "0ktapus" Okta-credential phishing wave, escalated to the high-profile 2023 MGM Resorts and Caesars Entertainment intrusions, and through 2024–2025 operated as an affiliate of multiple ransomware-as-a-service brands (BlackCat/ALPHV, RansomHub, Qilin, DragonForce), hitting retail, insurance, and aviation. This profile teaches Decepticon to emulate G1015's signature identity-centric TTPs inside an authorized engagement and helps the blue cell anticipate detection.
Attribution & motivation
- Sponsor / nation: Non-state, criminal. Members are assessed to be primarily young, native English speakers based in the US and UK, affiliated with the broader "The Com" cybercrime community. Not a nation-state actor; multiple arrests and a 2025 US extradition have been reported (per Krebs on Security / Infosecurity Magazine).
- Motivation: Primarily financial — credential theft, SIM-swap fraud / crypto theft, data extortion, and ransomware (
T1657Financial Theft). There is no credible espionage, destructive-only, or influence mandate; destruction (encryption) is in service of extortion. - Confidence: High confidence the cluster tracked as Scattered Spider = UNC3944 (Mandiant/Google) = Octo Tempest / Storm-0875 (Microsoft) = Muddled Libra (Palo Alto Unit 42) = Roasted 0ktapus = Scatter Swine = Star Fraud. Note these vendor clusters overlap but are not byte-for-byte identical; treat as a fluid affiliate community rather than a fixed roster.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 10d ago First seen · 171 lines · 49 tokens per session scan A a6b557e4699d
scattered-spider is a skill published in the GitHub repository PurpleAILAB/Decepticon (5,471 stars, last pushed 10d ago), licensed Apache-2.0. It adds 49 tokens to every session and 5,548 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
interactive-dashboard
Interactive web dashboards: stock trackers, sector heatmaps, portfolio monitors — served via preview URL.
onboarding
First-time user onboarding to set up investment profile, watchlists, portfolio, and preferences.
idea-generation
Stock screening and idea generation: quantitative screens, thematic analysis, shortlist.
secretary
Workspace and research management — dispatch analyses, monitor running agents, manage workspaces and threads.
python-lib-analyzer
Analyze any Python library structure, explore modules, classes, and functions with signatures and documentation.
analyzing-windows-prefetch-with-python
Use when parse Windows Prefetch files using the windowsprefetch Python library to reconstruct application execution history, detect renamed or masquerading binaries, and identify suspicious program execution patterns. Use when working with analyzing windows prefetch with python.