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Skill

/pipeline

Prepare the battlefield — recon, scanning, and surface ranking. Stops before hunting. Run /hunt or /autopilot after. Usage: /pipeline or /pipeline <target>

From plugin
pentest-agents
79439 skills50 agents3 hooks2 MCP
Install
$ npx -y skills add H-mmer/pentest-agents --skill pipeline --agent claude-code

How it fires

How this skill gets triggered: by you, by Claude, or both.

  • Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
  • You can call itInvoke it directly when you want it.
  • Slash command/pipeline

Context preview

The summary Claude sees to decide when to auto-load this skill.

Prepare the battlefield — recon, scanning, and surface ranking. Stops before hunting. Run /hunt or /autopilot after. Usage: /pipeline or /pipeline <target>

SKILL.md

pipeline.SKILL.md
name: pipeline
description: "Prepare the battlefield — recon, scanning, and surface ranking. Stops before hunting. Run /hunt or /autopilot after. Usage: /pipeline or /pipeline <target>"
disable-model-invocation: false

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

4. Dispatch `recon` agent (model: inherit) with policy preamble and scope 5. After recon: dispatch `config-auditor` agent (model: inherit) for header/TLS/cookie review 6. After config: dispatch `js-analyzer` agent (model: inherit) for JavaScript analysis 7. Brain update: `uv run python3 $CLAUDE_PROJECT_DIR/tools/brain.py record <target> recon "<results summary>"`

Phase 2: SCANNING (parallel, max 3)

8. Dispatch in parallel (all model: inherit, all with policy preamble):

  • `vuln-scanner` agent with nuclei on discovered hosts
  • `waf-profiler` agent on primary targets

9. Brain update with scan results

Phase 3: RANK

10. Dispatch `recon-ranker` agent (model: inherit) with recon data + brain knowledge 11. 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.

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