analyze
Analyze recon output with AI to suggest high-value targets and attack strategies. Usage: /analyze <target>
Resume a previous hunt. Shows hunt history, untested endpoints, memory-informed suggestions. Usage: /resume target.com
$ npx -y skills add H-mmer/pentest-agents --skill resume --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
/resumeContext preview
The summary Claude sees to decide when to auto-load this skill.
Resume a previous hunt. Shows hunt history, untested endpoints, memory-informed suggestions. Usage: /resume target.com
name: resume description: "Resume a previous hunt. Shows hunt history, untested endpoints, memory-informed suggestions. Usage: /resume target.com" disable-model-invocation: false
Resume hunt on: $ARGUMENTS
1. Read brain for target: `uv run python3 $CLAUDE_PROJECT_DIR/tools/brain.py brief $ARGUMENTS` 2. Read brain exhausted: `uv run python3 $CLAUDE_PROJECT_DIR/tools/brain.py exhausted $ARGUMENTS` 3. **Read chain-pending advisory**: `cat .claude/agent-memory-local/chain-pending.md 2>/dev/null` — feeder findings from prior session waiting for chain dispatch. SURFACE THESE FIRST — they have proven dollar value if chained, vs untested surface which is speculative. 4. **Generate / refresh chain plan**: `uv run python3 $CLAUDE_PROJECT_DIR/tools/chain_plan.py $ARGUMENTS` — writes `evidence/<target>/CHAIN_PLAN.md` with 3-5 candidate next-links per pending feeder finding plus the agent to dispatch for each. Read this BEFORE picking the next vuln class — proven feeders trump speculative untested surface. 5. Read recon data from recon/ directory 6. Show what's been tested vs what remains 7. Suggest next techniques based on brain patterns
RESUME: target.com
═══════════════════
Brain Status:
Confirmed findings: N
Exhausted techniques: N
Effective techniques: N
Untested Surface:
1. /api/v2/users/{id}/export — not tested
2. /api/v2/users/{id}/share — not tested
...
Suggestions:
- Tech stack [X] matches pattern where IDOR was found before
- Sibling endpoints of confirmed finding not yet tested
Actions:
[h] /hunt target.com — resume hunting
[s] /surface target.com — re-rank attack surface
[r] /recon target.com — re-run recon (surface may have changed)
[m] /monitor check — check for target changes since last sessionResume by value, not chronology.
Priority order: 1. `chain-pending` confirmed feeders with clear next link 2. validated partials needing one proof artifact 3. changed assets from `/monitor` 4. untested P1 crown-jewel endpoints 5. high-confidence class hypotheses from prior wins 6. stale recon refresh
Show what not to redo. If an area is exhausted, include the exact blocker and matrix coverage. A resumed hunt should start with a single best command, not a menu of every possible command.
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
Analyze recon output with AI to suggest high-value targets and attack strategies. Usage: /analyze <target>
Autonomous hunt orchestrator. INSATIABLE in --autonomous mode: enforces an EXHAUSTION CONTRACT (26 canonical hunter classes, surface probe A-I, depth-engine…
Manage the engagement brain. Subcommands: 'init' to set up, 'brief <target>' for pre-flight, 'status' for overview, 'exhausted [target]' to see dead ends.
Build deep exploit chains — dispatches chain-builder agent. Given bug A, recursively walks the chain graph. Usage: /chain (then describe bug A)
Run the finding correlation engine to discover attack chains from individual findings.