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Bb Workflow

Community
Encod3d-Sec
bb-workflow

Autonomous bug-bounty campaign driver. Runs a full programme end to end with no operator approvals - the deterministic driver (scripts/campaign.py) owns pass state, generates the killchain board from recon, and prints the exact next action (including which Skill and tool to run) every turn. Use when starting or resuming a bug-bounty engagement, "run the bb workflow", "hunt this program", "9-pass campaign", or when handed a *.scope wildcard to test for TIER1 findings. Single agent, refuter-verified, wiki-first, tool-first.

Overview

PublisherEncod3d-Sec
RepositoryTORCH
Skill namebb-workflow
Stars
334
Forks
48
Bundled files
Instructions only
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.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by Encod3d-Sec on GitHub. Read the source before you install it.

Installation

Install the Bb Workflow 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/Encod3d-Sec/TORCH/tree/main/skills/workflow/bb-workflow
  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/Encod3d-Sec/TORCH.git /tmp/TORCH
mkdir -p .claude/skills
cp -r /tmp/TORCH/skills/workflow/bb-workflow .claude/skills/bb-workflow
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Bb Workflow 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 Bb Workflow 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 Bb Workflow 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.

bb-workflow

The driver is the plan. You run one command, do exactly what it prints, record the result, repeat. Nothing here is advisory: the gates are enforced by scripts/campaign.py, so follow its output literally rather than improvising. This exists because prose routing failed - 22 Skill calls against 341 hand-rolled curls in the reference campaign; the board fixes that by making the mandate tool output, fresh every turn.

The loop

python3 scripts/campaign.py next        # prints ONE required-action block
# do EXACTLY what it lists, in order
python3 scripts/campaign.py note <row> --arsenal <slug>     # after Skill(wiki-arsenal)
python3 scripts/campaign.py done <row> --poc <img> --kind req   # | --dead R | --park Q | --find F
# repeat

Start / resume

  1. python3 scripts/campaign.py init --type bb - validates scope.md + the autonomy envelope, repairs engagement_type/schema, prints the Deadends size. If it exits 2, fix what it names (an empty scope.md is the one thing it cannot invent - fill it from the programme brief first).
  2. Passes 0-3 (OSINT/Wayback, crawl, fingerprint, CVE) feed state.md. Read every JS bundle and handler end to end (Skill-less; volume-reduce first: source-map, drop vendor, beautify, read). Grep never substitutes for the read.
  3. python3 scripts/campaign.py board - writes the killchain 4a rows. Refuses on an empty state.
  4. Enter the loop. next serves one row, depth-first, one open row at a time.

Browser observation (chrome-devtools MCP)

The programme's targets are internet-reachable, so drive them through a real browser, not just curl - a modern site's real attack surface is only visible rendered. Use the chrome-devtools MCP:

  • Pass 1 crawl: navigate_page to each app, then list_network_requests - the XHR/fetch calls a page makes reveal the API endpoints/routes a static crawl never sees (this is exactly the lead a curl-only recon misses). take_snapshot for the rendered DOM; evaluate_script to read client config / __NEXT_DATA__ / JS globals.
  • Confirmation + evidence: DOM-XSS fires in the real DOM (evaluate_script / console); a rendered take_screenshot of the exploited state is a valid web PoC (G3).

Caveat: chrome-devtools drives a local browser, so it only reaches internet targets. For a VPN-boxed host it cannot connect - use the VM-side browser (scripts/browser.sh / capture.sh web) as the equivalent. pt-workflow / ctf-workflow default to the VM browser for that reason.

Manual login / MFA the agent cannot do headlessly (Smart-ID, Mobile-ID, a CAPTCHA) -> Skill(chrome-devtools-browser): a VISIBLE chromium on the VM desktop (scripts/browser-visible.sh) the operator logs into, driven + observed live via the chrome-devtools MCP - capture the authenticated session and the real /… API calls, then feed the hunt skills.

Gates (the driver enforces; do not fight them)

  • G1 no exploit action until the row's arsenal card exists (Skill(wiki-arsenal) fills it).
  • G2 a row cannot close unless its mapped Skill(hunt-*) actually fired.
  • G3 a row cannot close without typed evidence: req (default), burp, or web (visual classes only). A page render is not evidence of a bug.
  • G8 run the mapped tool before hand-rolling; the driver warns if you skipped it.

Autonomy

No approvals. Out-of-envelope work parks to decisions.md and the loop moves on - it never blocks and never asks a question. A confirmed TIER1 is written up and chained but does not stop the run; the campaign ends when the board is exhausted (two dry reframe rounds) or the request budget is spent.

Model routing

Explicit per-role model assignment (the driver enforces the verifier gate; the rest is operating policy):

  • Main brain - Opus 5 1M: the campaign.py loop, board/state, strategy, Burp/chrome-devtools, finding write-ups. This session.
  • Verifier - Opus (fresh context), MANDATORY: before any CONFIRMED, campaign.py verify <F> prints an Opus refuter prompt; dispatch ONE fresh Opus agent that reads the raw PoC, tries to REFUTE, and writes verdicts/<F>.json. done --find refuses unless that verdict exists, is refuted:false, and cites the finding's PoC (anti-rubber-stamp). Fails CLOSED.
  • RTL - Opus (fresh context): Skill(redteamlead) for direction at a fork or when a vector stalls.
  • Short tasks - Haiku: Skill(wiki-arsenal) deep, Skill(delegate) (mechanical exploit-run), Skill(ingest) recon-parse. Bounded, fully-specified, single-shot ONLY.

The line that keeps quota safety: Haiku is allowed for a bounded single job, never for open-ended parallel hunting.

Discipline (carried, not restated - hunt-core owns the gates)

  • Do NOT invoke superpowers:brainstorming or superpowers:writing-plans mid-campaign, and keep no parallel TaskCreate list. The board is the plan.
  • Model routing (see ## Model routing): Opus-1M drives the loop; bounded short tasks go to Haiku; the mandatory Opus verifier gates every finding (done --find refuses without a passing verdicts/<F>.json). Still no open-ended hunter fan-out (it exhausted the weekly quota last time) - Haiku is only for single, fully-specified jobs.
  • Load-bearing exploit requests go through Burp Repeater when it is reachable; degrade to capture.sh req when it is not. Never block on Burp.
  • Scope and the enumeration ceiling come from scope.md, never from this skill.
  • RCE-first: with >1 vector open, take the code-exec one first - it is the attack vector with impact; chase lower-impact classes only after code-exec is ruled out or the target needs them.
  • Read whole, not grep: on a post-foothold host where a sudoer has no findable password and the usual vectors dead-end, READ /etc/pam.d/{sudo,su} + /etc/sudoers.d/* and linpeas output WHOLE - a pam_ssh_agent_auth/pam_exec/NOPASSWD line is the tell a grep skips. See [[linux-privesc]].
  • Long tools: run pspy/linpeas via bash scripts/vm-bg.sh <eng> <win> '<tool>' (stages to /dev/shm, runs in the stabilized shell, --read/--wait 120 the logfile); never retry a broken tool pattern >2x - switch method or call Skill(redteamlead).

Close-out

When next prints the close-out chain, run it: Skill(triage) -> Skill(evidence) -> Skill(report) -> Skill(learn).

If the driver is unavailable

Manual fallback, same gates by hand: read Approach.md, take the top open row for the current asset, run its wiki lookup then its hunt skill, capture req evidence, mark it [x]; on exhaustion one Deadends.md line and [!].

Frequently asked questions

What does the Bb Workflow AI skill do?

Autonomous bug-bounty campaign driver. Runs a full programme end to end with no operator approvals - the deterministic driver (scripts/campaign.py) owns pass state, generates the killchain board from recon, and prints the exact next action (including which Skill and tool to run) every turn. Use when starting or resuming a bug-bounty engagement, "run the bb workflow", "hunt this program", "9-pass campaign", or when handed a *.scope wildcard to test for TIER1 findings. Single agent, refuter-verified, wiki-first, tool-first.

Why use Bb Workflow on TypingMind?

Because you install it once and use it with any model. Bb Workflow 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 Bb Workflow in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Encod3d-Sec/TORCH/tree/main/skills/workflow/bb-workflow. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Bb Workflow?

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 Bb Workflow?

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

Is the Bb Workflow AI skill free?

Yes. It is published on GitHub by Encod3d-Sec 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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