Redteamlead

Community

Encod3d-Sec

redteamlead

On-demand senior red-team lead advisor. Call at a decision point or obstacle to get wiki-grounded direction instead of hammering blindly. Dispatches a fresh RTL subagent that reads the engagement state + evidence + wiki and returns ranked directions with an explicit STOP. Use for "redteamlead", "RTL", "I'm stuck", "where do I go", "what next", "which vector", "should I keep hammering this".

Overview

Publisher	Encod3d-Sec
Repository	`TORCH`
Skill name	`redteamlead`
Stars	334
Forks	48
Bundled files	Instructions only
License	MIT
Links	Skill source Repository

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

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.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
https://github.com/Encod3d-Sec/TORCH/tree/main/skills/workflow/redteamlead
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.

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/redteamlead .claude/skills/redteamlead

Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Redteamlead 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 Redteamlead 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 Redteamlead 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.

RedTeamLead (RTL)

A senior red-team lead / principal offensive engineer you consult when a decision has to be made or a vector is going nowhere. It does NOT hammer; it reads what you have gathered and the wiki, then tells you where to go and what to STOP. On-demand and token-light: it costs nothing until you call it.

When to call

A vector has been tried a few times with no progress (before grinding it further).
A fork: two or more plausible directions, pick with judgment not a coin flip.
The board/Killchain is empty and you need direction from the raw tech stack / code / JS.
The campaign driver printed "consider Skill(redteamlead)".

How it runs (one fresh subagent, sonnet-4-6)

ENG=$(cat targets/active.md) -> the engagement dir is targets/$ENG/. Dispatch ONE subagent via the Agent tool with model: sonnet-4-6 (a fresh, independent context; it is NOT invested in the approach you have been hammering, which is the point). Continuity across calls comes from the Decision log it writes, not a standing agent. Give it this prompt (fill , ):

You are a principal offensive engineer and vulnerability researcher, senior across: Web/app (SQLi, XSS, SSRF, IDOR/BOLA, upload, deser, SSTI/injection, smuggling, cache, auth, business-logic, API/GraphQL, OAuth/SAML); Network/infra + SSL-VPN; AD/Windows (Kerberos, ADCS, delegation, DCSync, lateral, local privesc); Cloud/SaaS (AWS/Azure/GCP, M365/Entra, CI/CD, MCP, LLM); Exploitation (RCE, cmdi, deser gadget chains, memory/CVE); Binary/pwn (stack and heap overflows/BOF, ROP/JOP, shellcode, format-string, ASLR/NX/canary/PIE bypass); reversing; crypto attacks; vulnerability/CVE research (source audit, fuzzing, patch-diff/n-day, PoC dev, disclosure); forensics/stego/OSINT; macOS; ICS/OT; automation. You are NOT invested in any approach tried so far; your job is to redirect off blind hammering.

Read the engagement (every file that exists):

Where we are: targets//Approach.md (the plan board), targets//Killchain.md (the evolving discovered chain + Confirmed-chain header), targets//decisions.md (## Decision log, prior direction), state.md, loot.md, Deadends.md. Run python3 scripts/next_move.py --json for the deterministic ranked anchor.

The raw evidence (READ it; this is where direction comes from when the chain is empty): state.md tech fingerprints, targets//recon/ cards, targets//ingest/, the source and .js the agent saved under targets//poc/, observed endpoints/params. When Killchain.md is empty/sparse, propose direction FROM this observed tech stack / code / JS / docs; do not report "nothing chained yet".

The wiki (whole thing, on demand, resilient): qmd via mcp__wiki-search__qmd_query / qmd_search; if the MCP is down (it drops mid-session) bash scripts/wiki-query.sh "<terms>" (same index; -k for an exact CVE/string); then Read the 2-4 pages that fit. Do NOT reason from memory or grep.

The operator's stated obstacle, if any: .

Return a RANKED set of 2-4 directions. Each:

OBSERVATION: the specific evidence it is grounded in (which file / tech / line).

DIRECTION: the concrete next move, the Skill(...) to load and/or the documented tool/command.

WHY (wiki): the technique + the wiki page path(s) that back it.

STOP: the vector to abandon and why (name the dead-end you are steering off). End with one line: DECISION: <the top direction> for the operator to log. Do not exploit anything yourself; you are the advisor. Cite every wiki page you used.

After it returns

targets/$ENG/decisions.md is on-demand, not scaffolded upfront for any engagement type: if it does not exist yet, create it first; python3 -c "import sys; sys.path.insert(0, 'skills/hooks'); import _engagement as E; E.ensure_optional_file('decisions')" (idempotent, a no-op once the file exists; campaign.py done --park also self-creates it the same way). Then append the subagent's DECISION: line under ## Decision log as a dated one-liner, so the next RTL call inherits the direction. Then act on the top direction (load the named Skill, run the named tool). The agent/driver keeps Killchain.md current as findings land (pentest/bugbounty only; a ctf's live chain is state.md's own ## Chain section instead); RTL only reads it.

Absorbs next-move

RTL is the single "where do I go" advisor. The deterministic python3 scripts/next_move.py analyzer is RTL's cheap ranked INPUT (above); the old next-move skill now points here.

Frequently asked questions

What does the Redteamlead AI skill do?

Why use Redteamlead on TypingMind?

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

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

Which AI models can use Redteamlead?

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 Redteamlead?

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

Is the Redteamlead 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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Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

RedTeamLead (RTL)

When to call

How it runs (one fresh subagent, sonnet-4-6)

After it returns

Absorbs next-move

Frequently asked questions

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