orchestration

orchestration is a skill for Claude Code from PurpleAILAB/Decepticon. It costs 26 tokens per session (1,620 once invoked), scanned A, original, Apache-2.0.

A set of patterns for coordinating multiple agents during a penetration test. It defines how to delegate work, share context, track state, and change the plan as evidence appears.

In plain words
What is it for?
Use it to assign reconnaissance, planning, exploitation, and other phase-specific tasks with clear scope, lessons, acceptance criteria, and output locations.
Why use it?
It reduces lost context, duplicated work, and unclear responsibilities between agents working on different testing phases.

Skill for Claude Code

Written for Claude Code: allowed-tools in frontmatter. Also seen: mentions subagents.

Good fit Use it to assign reconnaissance, planning, exploitation, and other phase-specific tasks with clear scope, lessons, acceptance criteria, and output locations.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/purpleailab/decepticon/orchestration
About the project

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.

PurpleAILAB/Decepticon · 5,491 stars · on GitHub · decepticon.red

Install

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.

Any agent
npx skills add PurpleAILAB/Decepticon --skill orchestration
Clone the repo
git clone --depth 1 https://github.com/PurpleAILAB/Decepticon

Made for: Claude Code.

Wrote 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.

agentmods badge for orchestration

README.md
[![agentmods](https://agentmods.dev/badge/skills/purpleailab/decepticon/orchestration/github.svg)](https://agentmods.dev/skills/purpleailab/decepticon/orchestration)
Your own site
<a href="https://agentmods.dev/skills/purpleailab/decepticon/orchestration"><img src="https://agentmods.dev/badge/skills/purpleailab/decepticon/orchestration/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.

agentmods 80×15 button for orchestration

Your own site · 80×15
<a href="https://agentmods.dev/skills/purpleailab/decepticon/orchestration"><img src="https://agentmods.dev/badge/skills/purpleailab/decepticon/orchestration.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 26 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,620 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00026 $0.01620
Opus 5 $0.00013 $0.00810
Sonnet 5 $0.00005 $0.00324
Haiku 4.5 $0.00003 $0.00162

Measured 8d ago against content hash 02ad7b1302ab, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

Grade A, and why

orchestration 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 8d 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.

Origin

Copies of this mod

1 near-identical copy found in the catalogue:

packages/decepticon/decepticon/skills/standard/decepticon/orchestration/SKILL.md · 184 lines

How it starts

The opening of the file, as written. The whole thing — 184 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Decepticon Orchestration Patterns

Delegation Protocol

Context Handoff — What Every Sub-Agent Needs

Every task() delegation MUST include:

  1. Objective — What specifically to accomplish (from OPPLAN)
  2. Scope — IN SCOPE targets + OUT OF SCOPE boundaries (from RoE)
  3. Context — Relevant findings from previous phases
  4. Lessons — Known gotchas, failed approaches, OPSEC warnings
  5. Acceptance Criteria — How the sub-agent knows it's done
  6. Output Location — Where to save results (e.g. recon/, exploit/)

Delegation Template

task(
  description="""
  OBJECTIVE: {objective_id} — {title}
  PHASE: {phase}

  SCOPE:
  - IN: {in_scope_targets}
  - OUT: {out_of_scope_targets}

  CONTEXT FROM PREVIOUS PHASES:
  {relevant_findings_summary}

  LESSONS LEARNED:
  {known_gotchas}

  ACCEPTANCE CRITERIA:
  - [ ] {criterion_1}
  - [ ] {criterion_2}

  Save all results to {phase}/
  """,
  subagent_type="{agent_name}"
)

Sub-Agent Selection Matrix

Objective Phase Sub-Agent When to Use
Planning soundwave Missing roe.json/conops.json/deconfliction.json, or documents need updating
Recon recon Subdomain/port/service enumeration, OSINT, cloud/web recon
Exploitation exploit Initial access: SQLi, SSTI, AD attacks, credential exploitation
Post-Exploitation postexploit After foothold: cred dump, privesc, lateral movement, C2

Parallel Execution

Delegate independent tasks simultaneously for efficiency:

# Independent targets — run in parallel
task(description="Recon subnet 10.0.0.0/24...", subagent_type="recon")
task(description="Recon subnet 10.0.1.0/24...", subagent_type="recon")

# DO NOT parallelize dependent tasks:
# ✗ Exploit before recon completes
# ✗ PostExploit before foothold established

State Management

Engagement State Files

./
├── plan/
│   ├── roe.json          # Immutable scope boundaries (read every iteration)
│   ├── conops.json       # Operation concept
│   ├── deconfliction.json # Deconfliction identifiers and procedures
│   └── opplan.json       # Objective tracker (update status after each sub-agent)
├── findings/            # Per-finding Markdown files, created lazily
├── lessons_learned.md    # Failed approaches + what worked
└── .ralph_state.json     # Loop iteration counter + completion flags

Read the full file on GitHub · 184 lines

Changes

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.

  1. 8d ago First seen · 184 lines · 26 tokens per session scan A 02ad7b1302ab

Subscribe to this mod's changes

orchestration is a skill published in the GitHub repository PurpleAILAB/Decepticon (5,491 stars, last pushed 12d ago), licensed Apache-2.0. It adds 26 tokens to every session and 1,620 once invoked, about $0.0001 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.