pipeline
SkillSecurityThis command runs recon, scanning, and surface ranking — everything needed BEFORE hunting. It does NOT hunt, validate, or report. Use /hunt or /autopilot for that.
Available today. Use it from your connected AI after setup.
No other account needed.
Connect ahel once, and every AI you use reads what you have installed.
Then ask your AI: use the pipeline skill
About this capability
Prepare the battlefield — recon, scanning, and surface ranking. Stops before hunting. Run /hunt or /autopilot after. Usage: /pipeline or /pipeline <target>
What this skill tells your AI
The instructions your AI receives, as published by h-mmer/pentest-agents in .claude/skills/pipeline/SKILL.md and read by ahel’s review.
Prepare the battlefield for: $ARGUMENTS
This command runs recon, scanning, and surface ranking — everything needed BEFORE hunting.
It does NOT hunt, validate, or report. Use /hunt or /autopilot for that.
Phase 0: SETUP
- Read
scope.yaml— resolve and verify targets- If
$ARGUMENTSis empty:uv run python3 $CLAUDE_PROJECT_DIR/tools/scope_check.py --list - If
$ARGUMENTSis a domain:uv run python3 $CLAUDE_PROJECT_DIR/tools/scope_check.py $ARGUMENTS
- If
- Read
policy.md— extract policy preamble for all agent dispatches - Brain init or brief:
- If no brain exists:
uv run python3 $CLAUDE_PROJECT_DIR/tools/brain.py init - If brain exists:
uv run python3 $CLAUDE_PROJECT_DIR/tools/brain.py brief <target>
- If no brain exists:
Phase 1: RECON
- Dispatch
reconagent (model: inherit) with policy preamble and scope - After recon: dispatch
config-auditoragent (model: inherit) for header/TLS/cookie review - After config: dispatch
js-analyzeragent (model: inherit) for JavaScript analysis - Brain update:
uv run python3 $CLAUDE_PROJECT_DIR/tools/brain.py record <target> recon "<results summary>"
Phase 2: SCANNING (parallel, max 3)
- Dispatch in parallel (all model: inherit, all with policy preamble):
vuln-scanneragent with nuclei on discovered hostswaf-profileragent on primary targets
- Brain update with scan results
Phase 3: RANK
- Dispatch
recon-rankeragent (model: inherit) with recon data + brain knowledge - Output P1/P2/Kill list
Complete
Battlefield ready.
P1 targets: [list]
P2 targets: [list]
Kill list: [list]
Next steps:
/hunt <target> — manual hunting on a specific target
/autopilot — autonomous hunting across all P1 targets
/surface — re-rank surface with current brain knowledge
Sync brain: uv run python3 $CLAUDE_PROJECT_DIR/tools/global_brain.py sync-from-local
Top-Tier Pipeline Standard
The pipeline prepares a battlefield, not a folder of scan files.
- Scope first: every generated target must be in-scope or tagged
out-of-scopewith reason. - Normalize assets into stable inventories: hosts, endpoints, JS files, APIs, auth flows, cloud buckets, repos, mobile packages, and third-party integrations.
- Rank during collection. Do not wait until the end to identify crown jewels.
- Preserve raw evidence and parsed summaries. A hunter should be able to replay the exact source of every target.
- End with
P1,P2, andKilllists plus the best first vuln class for each P1. If no P1 exists, say why and recommend monitoring or a different program.
Signals
- GitHub stars
- 908
- Forks
- 169
- Last commit
- Jun 2026
Advanced
- Catalog kind
- skill
- Gateway key
pipeline-2- Source
- github.com/h-mmer/pentest-agents