Detect Magic logo

Detect Magic

Community
Hmbown
detect-magic

Use this skill when you need a fast, structured scan for where the real magic is hiding in a repo, workflow, or system.

Overview

PublisherHmbown
RepositoryWizards-of-the-Ghosts
Skill namedetect-magic
Stars
106
Forks
10
Bundled files
Instructions only
LicenseCC0-1.0
Links
Skill sourceRepository
  • 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 Hmbown on GitHub. Read the source before you install it.

Installation

Install the Detect Magic 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.
  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/Hmbown/Wizards-of-the-Ghosts.git /tmp/Wizards-of-the-Ghosts
mkdir -p .claude/skills
cp -r /tmp/Wizards-of-the-Ghosts/generated/hermes/investigation-and-preparation/detect-magic .claude/skills/detect-magic
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Detect Magic 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 Detect Magic 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 Detect Magic 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.

Detect Magic

Surface hidden AI affordances, agents, automations, and tool hooks before acting.

What This Skill Does

Use this skill when you need a fast, structured scan for where the real magic is hiding in a repo, workflow, or system. In this grimoire, Detect Magic is treated as a metaphorical spell with a shipping-now delivery profile. Canonical reference input: Detect Magic (spell).

When To Use

  • You need a preflight scan of a repo or system before making any changes.
  • You need to map where automation, model behavior, and side effects actually live.
  • You want to inventory hidden capability surfaces: model providers, tool registries, shell bridges, webhooks, schedulers.
  • You need to identify surprising affordances, dangerous edges, or missing observability.
  • The request involves AI tooling, agents, starter kits, or model behavior scanning.

Prerequisites

  • No extra runtime dependencies beyond Hermes Agent and the normal toolset for this session.

Procedure

  1. Restate the target, the success condition, and any no-touch boundaries before taking action.
  2. Inventory obvious entrypoints: README, package manifests, setup docs, scripts/, CI/CD folders, env templates.
  3. Trace outward to hidden capability surfaces: model providers, tool registries, function-calling schemas, MCP config, plugin loaders, shell bridges.
  4. Identify background jobs, cron, schedulers, queues, workers, webhooks, event consumers, notification hooks.
  5. Call out surprising affordances, dangerous edges, missing observability, and fan-out points.
  6. Return a compact map of confirmed mechanisms, inferred mechanisms, and unknowns needing follow-up.
  7. Separate confirmed findings from inference every time — use explicit uncertainty language.
  8. Package the result as the deliverables below, with confidence, assumptions, and unresolved risk called out explicitly.

Deliverables

  • A concise capability inventory mapping all discovered execution surfaces.
  • A risk list covering hidden side effects or untrusted execution paths.
  • A shortlist of follow-up skills or next actions (e.g. $identify, $zone-of-truth, $glyph-of-warding).

Pitfalls / Guardrails

  • Keep the metaphor anchored to a real mechanism instead of drifting into lore.
  • Do not claim magic where there is only speculation — separate proof from suspicion.
  • Do not execute risky hooks, automations, deploys, rollbacks, webhooks, or billing mutations just to prove they exist.
  • Treat dependency or environment artifacts carefully — a binary suggests tooling is installed but does not prove the repo ships that capability.
  • Use explicit uncertainty language: Confirmed, Inferred not confirmed, Unknown from repo evidence.
  • Do not drift into generic security-review prose — sound like a structured capability-scan ritual.

Verification

  • Check that the result includes every deliverable promised above.
  • Check that confirmed facts, assumptions, and inferences are visibly separated.
  • Check that the metaphor still maps cleanly to a real operational mechanism.

Example Invocation

text
/detect-magic scan this repo for hidden AI tooling, agents, MCP servers, and automation hooks before we change anything

Frequently asked questions

What does the Detect Magic AI skill do?

Use this skill when you need a fast, structured scan for where the real magic is hiding in a repo, workflow, or system.

Why use Detect Magic on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Hmbown/Wizards-of-the-Ghosts/tree/main/generated/hermes/investigation-and-preparation/detect-magic. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Detect Magic?

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 Detect Magic?

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

Is the Detect Magic AI skill free?

Yes. It is published on GitHub by Hmbown under the CC0-1.0 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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