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/adnpx skills add PurpleAILAB/Decepticon --skill adgit 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/ad)<a href="https://agentmods.dev/skills/purpleailab/decepticon/ad"><img src="https://agentmods.dev/badge/skills/purpleailab/decepticon/ad.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 5.1 | $0.00031 | $0.00671 |
| Opus 5 | $0.00015 | $0.00336 |
| Sonnet 5 | $0.00006 | $0.00134 |
| Haiku 4.5 | $0.00003 | $0.00067 |
Grade A, and why
ad-overview 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:
- ad — 89% identical, 20 lines differ
What it actually says
AD Operator Skill Catalog
Playbooks
| Skill | Use for |
|---|---|
/skills/standard/ad/bloodhound-query/SKILL.md |
Ingest + common Cypher queries |
/skills/standard/ad/kerberoasting/SKILL.md |
Roast SPN users, crack with hashcat |
/skills/standard/ad/asrep-roasting/SKILL.md |
dontreqpreauth users |
/skills/standard/ad/adcs-esc1/SKILL.md |
ESC1 template abuse → domain admin |
/skills/standard/ad/dcsync/SKILL.md |
Replication rights → krbtgt dump |
/skills/standard/ad/laps/SKILL.md |
LAPS local admin password extraction |
/skills/standard/ad/netexec/SKILL.md |
NetExec (formerly CrackMapExec) cheatsheet — SMB/WinRM/LDAP/MSSQL modules |
Workflow
- Collect:
bash("bloodhound-python -u user -p pass -d DOMAIN -c all --zip") bh_ingest_zip("/workspace/bh.zip")dcsync_check— if any principal, that's instant domain compromisekg_query(kind="user")and filter forhasspn=true→ Kerberoast queuekg_query(kind="user")and filter fordontreqpreauth=true→ AS-REP roast- ADCS:
bash("certipy find -u user -p pass -dc-ip X -json")thenadcs_audit plan_attack_chainsto see graph-computed domain compromise paths
Crown jewels to add
kg_add_node(kind="crown_jewel", label="Domain Admins group")
kg_add_node(kind="crown_jewel", label="krbtgt account")
kg_add_node(kind="crown_jewel", label="DC: DC01.corp.local")
What ships with it
11 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- adcs-esc1/SKILL.md 4.5 KB
- asrep-roasting/SKILL.md 3.3 KB
- bloodhound-bhce/SKILL.md 6.3 KB
- bloodhound-query/SKILL.md 3.7 KB
- certipy-esc-chain/SKILL.md 5.7 KB
- coercer/SKILL.md 4.6 KB
- dcsync/SKILL.md 4.5 KB
- kerberoasting/SKILL.md 3.4 KB
- laps/SKILL.md 4.5 KB
- netexec/SKILL.md 5.7 KB
- ntlm-relay/SKILL.md 5.1 KB
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 · 54 lines · 31 tokens per session scan A ee514281eb3d
ad-overview is a skill published in the GitHub repository PurpleAILAB/Decepticon (5,450 stars, last pushed 6d ago), licensed Apache-2.0. It adds 31 tokens to every session and 671 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
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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.
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.