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Overview

Decepticon agents don’t operate from hardcoded playbooks. They draw from a skill system — a curated library of offensive techniques organized by kill chain phase and tagged with MITRE ATT&CK identifiers.

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Progressive Disclosure

Skills use a progressive loading architecture to keep agent context windows clean:
  1. Initial load — Only skill frontmatter (name, description, ATT&CK tags, phase) is loaded
  2. On-demand — When an agent selects a skill, the full content is loaded via read_file()
  3. Clean context — Agents only carry the detail they need for their current objective
This prevents context bloat. An agent working on initial access doesn’t need the full content of lateral movement techniques — just their existence and metadata.

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Skill Categories

Skills are organized by agent role, not by a single kill-chain dimension. This lets the orchestrator load only the skills relevant to the specialist it is dispatching, while still covering the full attack lifecycle. The library currently ships these categories:
CategoryOwning Agent(s)Coverage
soundwave/SoundwaveRoE / ConOps / OPPLAN templates, threat profiles, deconfliction
decepticon/Decepticon (orchestrator)OPPLAN dispatch patterns, sub-agent routing
recon/ReconPassive recon, active recon, web recon, cloud recon
osint/OSINT OperatorOpen-source intelligence — leaked credentials, breach data, public footprint
exploit/ExploitWeb exploitation, Active Directory initial access
post-exploit/Post-ExploitPrivilege escalation, lateral movement, persistence
ad/AD OperatorBloodHound, Kerberoasting, ticket forging, ACL abuse
cloud/Cloud HunterIAM analysis, metadata abuse, cloud-key extraction
contracts/Contract AuditorSolidity static analysis, Foundry PoC harness
reverser/ReverserBinary triage, packer detection, ROP gadgets, Ghidra/radare2
phisher/PhisherPretext-aware payload generation, landing pages, harvester scaffolds
mobile/Mobile OperatoriOS / Android app triage, MASTG checks, emulator-driven dynamic analysis
wireless/Wireless Operator802.11 / Bluetooth / SDR recon and exploitation
iot/IoT OperatorEmbedded device firmware extraction, JTAG/UART, protocol reversing
ics/ICS OperatorSCADA / DCS / PLC engagement workflows on isolated test ranges
supply-chain/Supply-Chain OperatorBuild-system, dependency, signing-key, and CI/CD compromise patterns
dfir/ForensicatorVolatile data capture, timeline reconstruction, IOC pivoting
analyst/AnalystCross-correlation, knowledge-graph queries, narrative drafting
shared/All agentsOPSEC, defense evasion, finding protocol, adversary emulation, references, stealth infra
The vulnresearch plugin’s stage agents (scanner, detector, verifier, patcher, exploiter) draw their skill catalog from a separate plugins/ tree when the bundle is loaded. The progressive-disclosure loader filters this catalog by ATT&CK overlap with the active threat profile — out-of-profile skills are kept off the agent’s working set entirely.

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Skillogy: Graph-Backed Skill Resolver

The catalog above is the source of truth on disk. The catalog the agent actually queries lives in a separate Neo4j-backed graph owned by a standalone Skillogy service. Why a graph and not just read_file() against a directory tree?
  • Skills reference each other — kerberoasting builds on ad-recon and chains into ticket-forging. A graph lets the agent jump along those edges.
  • Filtering by ATT&CK ID, tag, subdomain, or tactic ID needs index lookups that walking the filesystem can’t answer cheaply.
  • Multiple agents read concurrently; the graph centralizes consistency and avoids races on file caches.
Skillogy runs as a sibling container (port 9100 inside the management network) with three agent-facing tools, all of them thin REST clients exposed through SkillogyMiddleware:
ToolWhat it does
find_skill(query?, subdomain?, mitre_id?, tag?, tactic_id?, limit=20)AND-combined relationship-aware discovery. Returns matching :Skill nodes with their frontmatter — body deferred.
load_skill(name_or_path)Fetch the full body + frontmatter of one :Skill node. Same progressive-disclosure principle as before, just over REST.
traverse(from_path, edge_types?, depth=2)Explicit graph BFS from a seed skill — follow BUILDS_ON, CHAINS_TO, MITRE_RELATED edges to find adjacent techniques.

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Hard ACL (ADR-0008)

Skillogy isn’t a flat catalog. Each agent role only sees the slice the orchestrator wants it to see — the ad_operator should not be browsing contracts/ skills, the contract_auditor should not be reading ad/. ADR-0008 wires this as a hard ACL rather than a soft convention: every Skillogy call carries an allowed_path_prefixes parameter (e.g. ["skills/standard/ad/", "skills/shared/"]) and the service rejects anything outside those prefixes at the query layer. Cross-role reads only succeed when the requested skill lives under skills/shared/. The ACL is set by the agent factory (not the model), so a specialist can’t be talked into widening it via prompt injection.

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How the file catalog and the graph stay in sync

The on-disk skill files remain the source of truth. The Skillogy service ingests them at container start (or via an explicit rebuild call) and projects them into Neo4j as :Skill nodes connected by typed edges derived from frontmatter. To add a skill: drop a SKILL.md into the right category directory, rebuild the graph, and it shows up to every agent whose ACL allows that prefix.

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MITRE ATT&CK Integration

ATT&CK mapping is woven into every layer of Decepticon — not added after the fact:

Objectives

Each OPPLAN objective carries mitre technique IDs (e.g., T1190, T1003.001).

Skills

ATT&CK techniques declared in skill frontmatter, displayed inline in the agent’s skill catalog.

Threat Actors

ConOps defines initial_access and ttps as ATT&CK IDs, modeling specific adversary profiles.

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Skill Frontmatter Example

Each skill is a directory containing a SKILL.md (frontmatter + body) and optional references/, scripts/, and assets/ subdirectories. 
---
name: passive-recon
description: "Use when gathering intelligence WITHOUT touching the target"
allowed-tools: Bash Read Write
metadata:
  subdomain: reconnaissance
  tags: passive, dns, subdomain-enum, whois, ct-logs
  mitre_attack: T1590, T1591, T1592
---
The agent sees this metadata when browsing available skills. Only when it selects the skill does the full body — commands, detection notes, references — get loaded into context. Decepticon enforces a SKILL-FIRST RULE: agents must load the relevant skill before acting on a matching trigger keyword.

Multi-Model Routing

Learn how different agent roles are routed to optimal model tiers.