pipeline

SkillSecurity

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.

Available today. Use it from your connected AI after setup.

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

  1. Read scope.yaml — resolve and verify targets
    • If $ARGUMENTS is empty: uv run python3 $CLAUDE_PROJECT_DIR/tools/scope_check.py --list
    • If $ARGUMENTS is a domain: uv run python3 $CLAUDE_PROJECT_DIR/tools/scope_check.py $ARGUMENTS
  2. Read policy.md — extract policy preamble for all agent dispatches
  3. 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>

Phase 1: RECON

  1. Dispatch recon agent (model: inherit) with policy preamble and scope
  2. After recon: dispatch config-auditor agent (model: inherit) for header/TLS/cookie review
  3. After config: dispatch js-analyzer agent (model: inherit) for JavaScript analysis
  4. Brain update: uv run python3 $CLAUDE_PROJECT_DIR/tools/brain.py record <target> recon "<results summary>"

Phase 2: SCANNING (parallel, max 3)

  1. Dispatch in parallel (all model: inherit, all with policy preamble):
    • vuln-scanner agent with nuclei on discovered hosts
    • waf-profiler agent on primary targets
  2. Brain update with scan results

Phase 3: RANK

  1. Dispatch recon-ranker agent (model: inherit) with recon data + brain knowledge
  2. 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.

  1. Scope first: every generated target must be in-scope or tagged out-of-scope with reason.
  2. Normalize assets into stable inventories: hosts, endpoints, JS files, APIs, auth flows, cloud buckets, repos, mobile packages, and third-party integrations.
  3. Rank during collection. Do not wait until the end to identify crown jewels.
  4. Preserve raw evidence and parsed summaries. A hunter should be able to replay the exact source of every target.
  5. End with P1, P2, and Kill lists 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