Trigger Scenarios

运行渗透测试或红队演练进行漏洞赏金狩猎对目标进行侦察或扫描检查或恢复正在进行的测试任务查询 Decepticon 测试结果

Install

npx skills add PurpleAILAB/Decepticon --skill decepticon -g -y

More Options

Non-standard path

npx skills add https://github.com/PurpleAILAB/Decepticon/tree/main/integrations/agent-skills/decepticon -g -y

Use without installing

npx skills use PurpleAILAB/Decepticon@decepticon

指定 Agent (Claude Code)

npx skills add PurpleAILAB/Decepticon --skill decepticon -a claude-code -g -y

安装 repo 全部 skill

npx skills add PurpleAILAB/Decepticon --all -g -y

预览 repo 内 skill

npx skills add PurpleAILAB/Decepticon --list

SKILL.md

Frontmatter

{
    "name": "decepticon",
    "license": "Apache-2.0",
    "version": "2.0.0",
    "metadata": {
        "hermes": {
            "tags": [
                "decepticon",
                "red-teaming",
                "penetration-testing",
                "bug-bounty",
                "mcp",
                "autonomous-agents",
                "recon",
                "exploitation",
                "sarif"
            ],
            "related_skills": [
                "pentest-recon",
                "offensive-reporting",
                "reconnaissance"
            ]
        },
        "homepage": "https:\/\/github.com\/PurpleAILAB\/Decepticon"
    },
    "description": "Drive Decepticon — an autonomous multi-agent red-team framework — over MCP to run authorized penetration tests and bug-bounty engagements end to end, then watch and steer them live from chat. Launch an engagement against a target, poll its transcript to narrate progress, send messages to refocus it, and pull findings as SARIF. Use when the user asks to run a pentest\/red-team engagement, hunt a bug bounty, do recon, exploit\/scan a host, web app, API, network, cloud, Active Directory, mobile app, or smart contract WITH Decepticon — or to check\/resume a running engagement or report what Decepticon found. Triggers: run a decepticon engagement, pentest this with decepticon, bug bounty, recon this target, red team this, scan this host, resume the engagement, what did decepticon find, decepticon status. Do NOT use for ad-hoc local tool runs (running nmap\/sqlmap\/ffuf directly) when no Decepticon server is involved — this drives the Decepticon orchestrator, not raw tools."
}

Decepticon engagements (over MCP)

Drive Decepticon — an autonomous multi-agent red-team framework — as if its CLI were in this chat. Run an authorized engagement end to end (recon → exploitation → post-exploitation → reporting) across web, API, network, Active Directory, cloud, mobile, smart-contract, and binary targets, then watch it progress and steer it as it runs.

You interact through the decepticon_* MCP tools (listed below). The heavy work runs inside the Decepticon server; you are the operator at the console.

Mental model — read this first

An engagement is a thread. decepticon_start_engagement returns a thread_id. That is the handle for every other tool — there are no run ids to track.
The orchestrator (decepticon graph) builds an OPPLAN and delegates to specialist sub-agents (recon, exploit, postexploit, analyst, reverser, cloud_hunter, ad_operator, mobile_operator, …) via a task() tool. You watch that narrative and nudge it.
Engagements are long and asynchronous (minutes to hours). start returns immediately; you poll and narrate. Never block waiting for completion.

Authorization — non-negotiable

Only start engagements against assets the user has explicitly confirmed are in scope. If scope is unclear or missing, ask before starting — do not guess a target.
ALWAYS pass scope + rules of engagement in instruction: name the in-scope hosts/domains/paths and the explicit out-of-scope items. The orchestrator enforces RoE on every tool call, but you are responsible for giving it correct scope.
Decline targets that are plainly not the user's to test.

Prerequisites (verify on first failure)

A Decepticon LangGraph server must be running and reachable (DECEPTICON_API_URL, default http://localhost:2024). If a tool errors with a connection failure, tell the user to start it (langgraph dev or the Docker stack) — don't retry blindly.
The decepticon MCP server must be registered and launched with DECEPTICON_SKIP_BOOT=1 (fast start). See the integration docs.

Tools

Tool	Use it to	Key args	Returns (key fields)
`decepticon_list_graphs`	see available graphs	—	`[{graph_id, name}]`
`decepticon_list_engagements`	browse / resume	`limit`	`[{thread_id, engagement_name, status}]`
`decepticon_start_engagement`	launch	`targets[]`, `instruction`, `scan_mode`, `engagement_name?`	`{thread_id, engagement_name, run_id, status}`
`decepticon_transcript`	watch the narrative	`thread_id`, `after_index`, `limit`	`{messages[], next_index, total, run_status}`
`decepticon_watch`	live sub-agent burst	`thread_id`, `max_seconds`, `max_events`	`{events[], run_status}`
`decepticon_send_message`	steer / answer / `/model`	`thread_id`, `message`	`{run_id, status}`
`decepticon_engagement_state`	OPPLAN / scope / phase	`thread_id`	`{engagement_name, message_count, values}`
`decepticon_engagement_status`	run status + findings ready	`thread_id`, `engagement_name?`	`{status, findings_available}`
`decepticon_engagement_findings`	pull results	`engagement_name`, `include_sarif?`	`{available, result_count, level_counts, sarif?}`
`decepticon_cancel_engagement`	stop the run	`thread_id`	text

Full parameters, defaults, clamps, and return schemas are in reference.md. Worked end-to-end runs are in examples.md.

The core loop

Pick a graph. Usually decepticon (full kill chain). Use recon for recon-only, soundwave for planning. decepticon_list_graphs() if unsure.
Start. decepticon_start_engagement(targets=[…], instruction="In scope: …; Out of scope: …", scan_mode="standard"). Save thread_id and engagement_name.
Watch + narrate. Loop decepticon_transcript(thread_id, after_index=<previous next_index>); summarise only the NEW messages for the user (coordinator decisions, task(<specialist>) delegations, results). For a live burst use decepticon_watch(thread_id). Check decepticon_engagement_status(thread_id, engagement_name); stop polling when status is terminal or findings_available is true.
Steer when useful: decepticon_send_message(thread_id, "skip the staging host, focus on the API"), answer the coordinator, or switch models with /model anthropic/claude-opus-4-8.
Report. When findings exist: decepticon_engagement_findings(engagement_name, include_sarif=true) → present severity, counts, and reproduction. decepticon_engagement_state for the OPPLAN/phase.
Resume later. decepticon_list_engagements() → reuse any thread_id.

Polling cadence (phone / chat friendly)

Don't spam tools. While running, poll the transcript every ~15–30s and give the user a 1–2 line update per poll, not raw dumps.
Use the returned next_index as your cursor so each update covers only new activity.
decepticon_watch blocks up to max_seconds (≤45) — use it for a quick live glimpse, not as your main loop.

Interpreting results (fast guide)

transcript.messages: role is user/assistant/tool. tool_calls like task(recon) means a specialist was dispatched. tool messages carry results.
status: pending/running = working; success = finished; error/timeout/interrupted = stopped (say why; offer resume/restart); none = no run yet.
findings: available=false → not persisted yet, keep polling. level_counts maps SARIF level → count (error = critical/high, warning = medium, note = low). Pass include_sarif=true to mine reproduction details.
engagement_state.values: OPPLAN, objectives, scope, phase, working files.

Errors & recovery

Connection failure → the Decepticon server isn't up at DECEPTICON_API_URL. Ask the user to start it; don't loop.
findings_available=false for a while → normal early on; keep watching the transcript and report progress.
status=error/timeout → read the last transcript messages for the cause, summarise it, and offer to send_message a fix or start fresh.
No active run on watch/cancel → the engagement is idle/finished; use transcript/findings instead.