analyze
Analyze recon output with AI to suggest high-value targets and attack strategies. Usage: /analyze <target>
Full security assessment with brain coordination. Multi-phase, skips known-exhausted areas, builds on prior knowledge.
Claude Code
$ npx -y skills add H-mmer/pentest-agents --skill fullscan --agent claude-code
Codex
$ npx skills add H-mmer/pentest-agents --skill fullscan -a codex -y
Cursor
$ npx skills add H-mmer/pentest-agents --skill fullscan -a cursor -y
Gemini CLI
$ npx skills add H-mmer/pentest-agents --skill fullscan -a gemini-cli -y
GitHub Copilot
$ npx skills add H-mmer/pentest-agents --skill fullscan -a github-copilot -y
OpenCode
$ npx skills add H-mmer/pentest-agents --skill fullscan -a opencode -y
Windsurf
$ npx skills add H-mmer/pentest-agents --skill fullscan -a windsurf -y
name: fullscan description: "Full security assessment with brain coordination. Multi-phase, skips known-exhausted areas, builds on prior knowledge." disable-model-invocation: false
ALL agents dispatched by this command MUST use `model: "inherit"` in the Agent tool call.
Full security assessment on: $ARGUMENTS
1. `uv run python3 $CLAUDE_PROJECT_DIR/tools/brain.py init` (if first run) 2. `uv run python3 $CLAUDE_PROJECT_DIR/tools/brain.py brief $ARGUMENTS` 3. `uv run python3 $CLAUDE_PROJECT_DIR/tools/scope_check.py $ARGUMENTS`
Launch `recon` agent with brain context. Focus on discovering NEW subdomains and services beyond what's already known.
Launch IN PARALLEL, each with brain context about what's been scanned before:
Record all results to the brain after each agent returns.
Based on Phase 2 + brain knowledge, selectively launch ONLY agents targeting UNTESTED or ACTIVE vectors:
Record all results to the brain.
1. `uv run python3 $CLAUDE_PROJECT_DIR/tools/dedup_findings.py --scan-dir scans/ --db findings.json --stats` 2. `uv run python3 $CLAUDE_PROJECT_DIR/tools/brain.py status` 3. Launch `poc-builder` for each confirmed finding 4. Launch `report-writer` with full brain context 5. `uv run python3 $CLAUDE_PROJECT_DIR/tools/brain.py log "Full scan completed on $ARGUMENTS"`
A full scan must leave a defensible coverage ledger.
1. Start with the crown jewels: auth, tenant data, billing, admin, integrations, uploads, webhooks, exports, AI/tool surfaces, and source/repo exposure. 2. For each phase, record both positives and negatives. "Scanned" is invalid unless it names tools, targets, timestamps, and skipped areas. 3. Do not let scanner output dominate. Use scanners to find leads, then spend human-grade effort on state changes, authorization boundaries, and parser differentials. 4. Before reporting, run `/correlate` or `/chain` on all confirmed and partial findings. Single low bugs should not escape the scan if a plausible chain remains. 5. Final output must contain: confirmed findings, killed false positives, exhausted high-value paths, untested residual risk, and the next best 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.
Analyze recon output with AI to suggest high-value targets and attack strategies. Usage: /analyze <target>
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