Pentest Engagement logo

Pentest Engagement

Organization
transilienceai
pentest-engagement

Run a professional penetration engagement OR a network vulnerability scan from a scope. WEB mode (apex domains / app URLs) — mandatory surface expansion, systematic OWASP attack-class coverage, reversible active exploitation, authoritative validation, Transilience PDF. NETWORK mode (a list of IPs/CIDRs, e.g. 1500 hosts) — machine-prudent BATCHED tool-first nmap sweep (host discovery + bounded common+less-common port/service scan + CVE surfacing) with a uniform per-IP output tree, then bounded deep-dives on the highest-value hosts. The general (non-CTF) analogue of hackthebox/htb-solve.

Overview

Publishertransilienceai
Repositorycommunitytools
Skill namepentest-engagement
Stars
565
Forks
81
Bundled files
1
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 1 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by transilienceai on GitHub. Read the source before you install it.

Installation

Install the Pentest Engagement AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/transilienceai/communitytools/tree/main/skills/pentest-engagement
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/transilienceai/communitytools.git /tmp/communitytools
mkdir -p .claude/skills
cp -r /tmp/communitytools/skills/pentest-engagement .claude/skills/pentest-engagement
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Pentest Engagement in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Pentest Engagement on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Pentest Engagement is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Pentest Engagement

Orchestrates a scoped pentest end-to-end via the pentest-engagement workflow. It is the breadth-complete, coverage-gated counterpart to the flag-shaped htb-solve — same engine (coordinator-loop, now with interleaved per-finding validation built into the loop), but driven by an attack-class coverage matrix instead of a flag, with surface expansion and root-cause severity baked in.

When to use

A real (non-CTF) engagement defined by a scope — either:

  • WEB — web / API / cloud apps defined by apex domains / asset URLs, or
  • NETWORK — a list of IPs / CIDRs / ranges (e.g. 1500 hosts) to scan for live services and vulnerabilities.

The workflow auto-detects the mode in Setup (engagement_kind): predominantly IPs/CIDRs → network; apex domains / app URLs → web. For HackTheBox/CTF use hackthebox (htb-solve) instead.

Run it

WEB — from a scope file or inline:

Workflow('pentest-engagement', { scope_file: 'projects/pentest/<engagement>-scope.md' })
Workflow('pentest-engagement', { scope: { engagement_name, apex_domains:[], assets:[...], creds_env:[...], roe, business_tier } })

NETWORK — inline IP/CIDR list or a scope file containing one (a plain newline list of IPs/CIDRs is accepted):

Workflow('pentest-engagement', { targets: ['10.0.0.0/24', '192.0.2.0/24', '198.51.100.7'] })
Workflow('pentest-engagement', { scope_file: 'projects/pentest/<engagement>-ips.txt', scan_profile: 'standard' })

Options (shared): maxConcurrent (default = prudent, derived from CPU cores — ~half the cores, capped 2–8; never hundreds/thousands of parallel tasks), dryRun, max_experiments, business_tier, report (default true). Options (network): scan_profile light (bounded 1-1024 + curated less-common, for large/fast sweeps) / standard (full-range -p- on every reachable host, DEFAULT; two-stage SYN→-sV on found-open ports, host-count-guarded, message-bus + non-443-TLS aware) / full (-p- + bounded UDP); udp (top-50 UDP; off for light/standard, auto-on for full); slice_size (hosts per scan worker, auto ≈64 IP-list / 2 CIDR-heavy); deepen_top (deep-dive the N highest-value hosts, default 10, 0 to skip); geo_vantages (≤2 gcp zones for the 2nd-vantage allowlist re-probe; overrides the US+EU default), auto_provision (default true; false = detect+flag only, no cloud spend). On a source-IP/geo-allowlist signature the workflow auto-provisions a 2nd-geography vantage and re-probes the filtered hosts before concluding "no surface."

Write the scope file per reference/scope-file-format.md. Credentials are referenced by env-var name only and read from the repo .env via python3 tools/env-reader.py — never inline secret values.

Phases (what the workflow does)

  1. Setup — env-reader creds, parse scope, classify kind (web|network), read CPU cores → prudent parallel-task cap, OUTPUT_DIR = projects/pentest/<date>_<engagement>/, STARTED Slack (gated).
  2. Expand (WEB, the #1 fix) — MANDATORY CT-log / passive-DNS / origin-discovery across every in-scope apex (crt.sh, certspotter, subfinder, origin-discovery for CDN/WAF-fronted hosts). Scope = the discovered surface, not the handoff. Builds the per-asset work list + seeds each asset's coverage matrix. Scan (NETWORK, replaces Expand) — slice the IP/CIDR set into machine-prudent batches; one nmap worker per slice (agents scale with slices ≈ dozens, never with IP count) runs the SAME pipeline: host discovery (reachability is unknown) → bounded common+less-common port/service scan → CVE surfacing (nmap --script vulners, nuclei, each CVE-ID enriched via tools/nvd-lookup.py) → writes a uniform per-IP tree hosts/<ip>/{recon,host.json,findings} + a merged recon/inventory/.
  3. Assess (single interleaved stage — no separate downstream validation pass) — WEB: each asset → coordinator-loop (coverage mode), which validates each candidate the instant it is materialized on fresh blind agents (strict per-finding cure/drop loop) before search continues. NETWORK: bounded coordinator-loop deep-dives on only the deepen_top highest-value hosts, same interleaved per-finding validation (everything else is the uniform tool-scan, not a per-host agent). Coverage-by-VALID: a class is covered only by a VALID/REPAIRED finding, a justified N/A, or a genuine negative — a class whose candidates were all rejected/dropped stays pending and search keeps going.
  4. Correlate — attack-path-stitcher + risk-prioritiser across all validated findings → ranked org roadmap.
  5. Report (deterministic — no agent authors the report) — JS hands the resolved engagement block + exact commands to ONE finalize runner: tools/report_data_build.py merges the namespaced interim finding-JSONs into the canonical report_data.json (the sole-owner assembly), then the format-dispatched renderer runs — transilience → the canonical generate_report.py PDF skill, custom → custom_report_cmd (or a Markdown fallback). JS then hard-gates: report_data assembled ∧ (transilience: WROTE ∧ bytes>0 ∧ the [assets: …/formats/transilience-report-style] provenance tag), retry-once → BLOCKED. Only VALID/REPAIRED findings appear (drop-entirely — validated/ is confirmed-only by construction); REJECTED (false-positives/) and uncured DROPPED (dropped/) never appear and there is no gaps/assurance section. The finalize runner also runs network_coverage_map.py (swept-host tail) + coverage_gate.py over the whole engagement, writes reports/coverage-matrix.json, and the deliverable includes a deterministic Attack Pattern Coverage section (surface-unit × attack-class). COMPLETE is a hard 100% gate: it requires the report to assemble+render AND the coverage gate to report complete:true (every applicable cell covered) — for BOTH web and network (network additionally requires scan-completion). Any untested applicable cell → INCOMPLETE_coverage / BLOCKED.
  6. Package & deliver — a verified <report_id>_deliverable.zip (reports/ input/ logs/ artifacts/), a short stats summary (summary.md: agents, findings by severity, elapsed; tokens/cost render unavailable — no runtime token counter), and the workflow returns slack_offer: true.

Post-run (main loop, outside the workflow): because coordinators must not call AskUserQuestion, the invoking agent shows the returned summary and asks whether to post the deliverable_zip to Slack; on yes, python3 tools/slack-send.py --channel "$PENTEST_SLACK_CHANNEL_ID" (gated on a successful, COMPLETE engagement).

Report options: report_format transilience (default) | custom; custom_report_cmd (the custom renderer, receives the report_data.json path + reports/ dir); prior_report (a prior PDF or report_data.json — its title/sector/scope are metadata-only, never seeding the work list) + version to mint the cover version + "Supersedes" line.

Determinism (be honest)

The decision layer is a frozen pure-JS computeVerdict (parity-guarded, fixture-pinned), its operands are frozen per engagement via NVD/KEV --cache-dir snapshots (artifacts/nvd-cache/, artifacts/kev-snapshot.json), and the adversarial quorum is raised to 3. Given identical inputs the verdict is provably identical. But a fresh live run is highly reproducible, not 100% — LLM sampling produces the booleans/numbers that feed the verdict, and live-target drift moves the inputs; that ceiling is inherent and stated plainly, not papered over. Byte-identical results are a guarantee 100% only on replay of a frozen evidence set (the Phase-2 replay cache, artifacts/validation-cache/) — that is the sole context in which "same request → same result" is a guarantee rather than a strong tendency.

Boundaries

  • Orchestrator only — never run the coordinator loop inline; the coverage/bookkeeping discipline needs the workflow boundary.
  • A missing credential is not a global block — the unauthenticated surface is always tested; only a no-reachable-asset scope blocks.
  • Reversible own-org/own-tenant writes are authorized by default (create-then-delete is non-destructive); destructive ops, DoS, brute force, and out-of-scope tenants are prohibited (set in RoE).

References

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Pentest Engagement AI skill do?

Run a professional penetration engagement OR a network vulnerability scan from a scope. WEB mode (apex domains / app URLs) — mandatory surface expansion, systematic OWASP attack-class coverage, reversible active exploitation, authoritative validation, Transilience PDF. NETWORK mode (a list of IPs/CIDRs, e.g. 1500 hosts) — machine-prudent BATCHED tool-first nmap sweep (host discovery + bounded common+less-common port/service scan + CVE surfacing) with a uniform per-IP output tree, then bounded deep-dives on the highest-value hosts. The general (non-CTF) analogue of hackthebox/htb-solve.

Why use Pentest Engagement on TypingMind?

Because you install it once and use it with any model. Pentest Engagement is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Pentest Engagement in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/transilienceai/communitytools/tree/main/skills/pentest-engagement. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Pentest Engagement?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Pentest Engagement?

As many as you like. As long as a model supports skills, you can use Pentest Engagement with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Pentest Engagement AI skill free?

Yes. It is published on GitHub by transilienceai under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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