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Walkthrough

Community
Encod3d-Sec
walkthrough

Assemble a report-ready walkthrough.md for a SOLVED engagement - populate the Evidence gallery from the poc/ images captured during the engagement, and draft the step-by-step reproduction from state/loot/log (Killchain.md for pentest/bugbounty, state.md's Chain/Status for ctf) without fabricating. Use when asked to "write the walkthrough", "assemble the walkthrough", "close out the box/engagement", or at close-out once an engagement is marked SOLVED.

Overview

PublisherEncod3d-Sec
RepositoryTORCH
Skill namewalkthrough
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 Walkthrough 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/walkthrough
  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/walkthrough .claude/skills/walkthrough
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Walkthrough 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 Walkthrough 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 Walkthrough 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.

Walkthrough auto-assembly

Turn a solved engagement into the full, report-ready targets/<eng>/walkthrough.md: rendered evidence, a populated ## Evidence gallery, and a drafted narrative, so the operator only reviews/polishes instead of assembling from scratch.

Convention: mark close-out explicitly

At close-out, write a STATUS heading into state.md:

## STATUS: SOLVED

(OWNED / ROOTED / COMPLETE also count.) This is the close-out signal: once present, the CLAUDE.md execution loop runs Skill(walkthrough) (then Skill(learn)).

Steps

(a) Confirm the evidence is on disk (capture any missing key state now)

Evidence is captured LIVE during the engagement, straight into poc/ (via capture.sh / Skill(screenshot)) -- there is no staging/drain step anymore. Confirm the PNGs are on disk, and if a key state (foothold shell, the flag, an exploited render) was never captured, capture it now before assembling:

bash
ls targets/<eng>/poc/*.png targets/<eng>/poc/**/*.png 2>/dev/null
# missing a key state? capture it live, e.g.:
#   bash scripts/capture.sh ev <eng> <slug> <url> "<cmd-label>"

A walkthrough with an empty gallery means evidence was not captured as steps landed -- fix that by capturing the reproducible states now, not by fabricating.

(b) Scaffold + gallery

bash
python3 scripts/build-walkthrough.py <eng>

Idempotent: scaffolds the walkthrough structure from the framework template if missing, and populates the ## Evidence gallery from every rendered card on disk. Never clobbers existing narrative -- safe to re-run after step (a).

(c) Draft the narrative -- never fabricate

Read state.md (for ctf, also its ## Chain/## Status sections, the live working copy of the attack path and the SOLVED/flags marker) and loot.md for the active engagement; for pentest/bugbounty also read Killchain.md and log.md (a ctf engagement has neither; its live chain and narrative live in state.md instead). Write the step-by-step reproduction into the non-Evidence sections (Access -> Recon -> Foothold -> Privilege escalation -> root/flag), using the EXACT commands, creds, and per-step results already captured in those files. If a fact needed for a section is not present in the state files, do NOT invent it; leave a clearly marked _TODO: <what is missing>_ for the operator instead.

Record HOW each vuln was DISCOVERED, not only the exploit. Every Foothold/Privesc step carries a short "found via:" note - the probe/test/observation that revealed the bug (e.g. "found via: registered a tag-marker username, saw it reflected unescaped in the users table"), so the reader can re-FIND it, not just re-fire the payload. The discovery step IS part of the reproduction; a walkthrough that only shows the working exploit hides how you got there.

Rewrite each command to the HUMAN form for the reader (technical team / client). log.md holds the messy automation that actually ran (base64/pty wrappers, exports, ;-merged diagnostic pipelines) - the walkthrough must NOT. Each step is ONE standalone command a person would type: concrete values + FULL paths, no $VAR/export (inline an env var on the one command, KRB5CCNAME=/tmp/x.ccache impacket-... ), no ;/&& chains, no echo banners. A merged diagnostic from log.md becomes 2-3 clean single commands here. Interactive sub-steps (an smbclient get, an su + password) are shown as the plain human action, not scripted.

(d) Preserve exploit scripts

Copy any exploit script (payload, escape/forge script, webshell) into targets/<eng>/poc/scripts/, the same exploit-script-preservation discipline ctf-box and screenshot use -- the reviewer needs the code and the state together, not just a screenshot. Save it as <name>.md with the code in a ```py/```sh/```js fence, NOT a bare .py/.sh/.js - Obsidian only previews .md/images in the GUI, so a raw-extension script is invisible to the operator reading the walkthrough. Reference the .md from the matching step; inline the actual payload as a fenced block in the step itself so the walkthrough is self-contained.

(e) Verify complete

Before finishing, confirm:

  • no unfilled template markers remain (<entrypoint>, <foothold, or a bare - target: / - reach: stub line), and
  • the ## Evidence gallery has at least one rendered image reference row.

If either check fails, go back and fill the gap (more evidence to drain, more narrative to draft) rather than leaving the walkthrough half-done.

Scope and safety

All output stays under targets/<eng>/ (gitignored) -- never write client data into session/* or wiki/. Every step here is best-effort: build-walkthrough.py is idempotent and no-clobber. Real client engagements still go through Skill(evidence) for redaction before the walkthrough ships in a report; CTF/lab work can skip that.

On demand

Invoke this skill directly whenever you are ready to write the walkthrough, assemble the walkthrough, or close out the box/engagement -- the same steps apply.

Frequently asked questions

What does the Walkthrough AI skill do?

Assemble a report-ready walkthrough.md for a SOLVED engagement - populate the Evidence gallery from the poc/ images captured during the engagement, and draft the step-by-step reproduction from state/loot/log (Killchain.md for pentest/bugbounty, state.md's Chain/Status for ctf) without fabricating. Use when asked to "write the walkthrough", "assemble the walkthrough", "close out the box/engagement", or at close-out once an engagement is marked SOLVED.

Why use Walkthrough on TypingMind?

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

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

Which AI models can use Walkthrough?

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 Walkthrough?

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

Is the Walkthrough 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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