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Ghost Exo

Organization
ghostsecurity
ghost-exo

The single interface for building, improving, and debugging exo workflows. Routes to one of three intents. BUILD takes a rough idea, or an agent template slug from the ghostsecurity.ai catalog, through interrogation, assessment, resource creation in dependency order, and one manual run, then hands off. IMPROVE runs the observe-and-iterate loop over recent runs, proposing and applying changes behind two gates. DEBUG diagnoses one failed or misbehaving run by walking the dependency graph of everything it touched. Use whenever the user wants to create a new exo workflow, iterate on or improve an existing one, or find out why a specific run failed.

Overview

Publisherghostsecurity
Repositoryskills
Skill nameghost-exo
Stars
417
Forks
25
Bundled files
9
Licenseapache-2.0
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.

  • 9 bundled files

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

  • Open source

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

Installation

Install the Ghost Exo 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/ghostsecurity/skills/tree/main/plugins/exo/skills/exo
  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/ghostsecurity/skills.git /tmp/skills
mkdir -p .claude/skills
cp -r /tmp/skills/plugins/exo/skills/exo .claude/skills/ghost-exo
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Ghost Exo 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 Ghost Exo 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 Ghost Exo 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.

exo

One front door for the exo workflow lifecycle. This skill is a router. It classifies the request into one of three intents, loads the shared substrate, then loads the matching intent recipe and follows it.

Prerequisites

Exo is an agent workflow platform. Every intent drives it through an exo MCP server.

Pick the connection

Several exo connections coexist normally, one per workspace, sometimes several on one deployment. Enumerate the exo MCP servers this session has before doing anything else.

With exactly one, use it. With more than one, ask which through the structured question tool and stop until the user answers, because a wrong guess writes to the wrong workspace. Do not switch connections partway through an intent. Start over if the target changes.

Call whoami on the chosen server. It confirms the connection and returns the workspace ID. Report that ID so the user can see which workspace they are about to change. If no exo MCP tools are present or whoami fails, read resources/bootstrap.md and follow it, then call whoami again. Do not classify an intent until it succeeds.

scripts/exo-skill.py reaches the same workspace over REST, and it has to reach the same one. It reads EXO_API_URL, EXO_API_KEY, and EXO_WORKSPACE_ID from the process environment, falling back to the profile named by --profile, which is the file ${XDG_CONFIG_HOME:-~/.config}/exo/<name>.env. Pass --profile with the name of the MCP server you chose on every call, so the two cannot point at different workspaces.

Always read first

Read resources/common.md. It holds the shared substrate every intent uses: the run-walking read primitives, the worker capability rules that decide which tools a skill may use and how it declares them, the DTO discovery discipline, the resource write primitives, the runtime contract for output, metrics, completion, and memory that every step runs under, the unprobeable nodes, and the note on why the improve intent reads the debug recipe inline rather than invoking it.

Classify the intent

If the request is aboutIntentRead
Why a specific run failed, what went wrong with a run_id, diagnosing one rundebugintents/debug.md
Improving, iterating on, tightening, or speeding up an existing workflow over its recent runsimproveintents/improve.md
Turning an idea into a new workflow, building, creating, or scaffolding a workflow, or building from an agent template slugbuildintents/build.md

Pick exactly one. If the request is genuinely ambiguous between intents, ask the user which one in a single question rather than guessing.

User interaction

Put every question to the user through the harness's structured question tool, whatever it is called here. Free-text prose questions with bullet lists or "Q1/Q2/Q3" prompts are not allowed, even when the question feels open-ended. Bucket open areas into concrete options and let the user type a custom answer instead. Batch related questions into one call so the user answers a structured form rather than a thread of replies. Respect the current limits of the tool you have, and ask directly in prose only when no structured tool is available or the answer is inherently free-form, such as a name, a metric, or a path. This applies to intent disambiguation, the build interrogation in intents/build.md, the proposal and rerun gates in intents/improve.md, and any candidate-disambiguation prompt in intents/debug.md.

Require an explicit answer at every approval, production write, credential, and rerun gate. Never attach auto-resolution to those questions.

The boundaries between intents are intentional gates, not friction to remove. A build that ends in a first run does not auto-continue into improve, because the user owns when to cross from constructing to iterating. An improve pass that finds a failed run reads the debug walk inline rather than switching intents, because the diagnosis is a sub-procedure of the loop, not a separate request.

Paths

All paths in the intent files are relative to this skill's root directory: scripts/ for executables, resources/ for shared docs and templates, intents/ for the three recipes. agents/ holds per-harness interface metadata that no recipe reads.

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 Ghost Exo AI skill do?

The single interface for building, improving, and debugging exo workflows. Routes to one of three intents. BUILD takes a rough idea, or an agent template slug from the ghostsecurity.ai catalog, through interrogation, assessment, resource creation in dependency order, and one manual run, then hands off. IMPROVE runs the observe-and-iterate loop over recent runs, proposing and applying changes behind two gates. DEBUG diagnoses one failed or misbehaving run by walking the dependency graph of everything it touched. Use whenever the user wants to create a new exo workflow, iterate on or improve...

Why use Ghost Exo on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/ghostsecurity/skills/tree/main/plugins/exo/skills/exo. 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 Ghost Exo?

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 Ghost Exo?

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

Is the Ghost Exo AI skill free?

Yes. It is published on GitHub by ghostsecurity under the apache-2.0 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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