/pipeline
Prepare the battlefield — recon, scanning, and surface ranking. Stops before hunting. Run /hunt or /autopilot after. Usage: /pipeline or /pipeline <target>
$ npx -y skills add H-mmer/pentest-agents --skill pipeline --agent claude-codeHow 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.mdname: 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.
Read more
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.
Bug bounty agent framework for Claude Code, Codex, Gemini, Cursor, Windsurf, Copilot, and OpenClaw — 48 agents, 26 commands, 19 CLI tools, 2 MCP servers, autonomous hunt loops, exploit chain builder.
Repo: H-mmer/pentest-agents
Other skills on pentest-agents.
- /analyze
Analyze recon output with AI to suggest high-value targets and attack strategies. Usage: /analyze <target>
Open skill - /autopilot
Autonomous hunt orchestrator. INSATIABLE in --autonomous mode: enforces an EXHAUSTION CONTRACT (26 canonical hunter classes, surface probe A-I, depth-engine ≥25 attempts/class, wall-clock floor 90 min/target, PRE-COMPLETION GATE before any summary). No early stops, no clarifying
Open skill - /brain
Manage the engagement brain. Subcommands: 'init' to set up, 'brief <target>' for pre-flight, 'status' for overview, 'exhausted [target]' to see dead ends.
Open skill - /chain
Build deep exploit chains — dispatches chain-builder agent. Given bug A, recursively walks the chain graph. Usage: /chain (then describe bug A)
Open skill - /correlate
Run the finding correlation engine to discover attack chains from individual findings.
Open skill - /cost
Show cost tracking and ROI for this engagement.
Open skill

