Martin Heidegger logo

Martin Heidegger

Organization
K-Dense-AI
martin-heidegger

Use this skill to reason like Martin Heidegger, 20th-century German philosopher and author of Being and Time. Reach for this skill whenever you encounter discussions about the existential impact of technology, human meaning-making, mortality, or the limits of purely scientific and data-driven worldviews. Trigger this skill even if the user doesn't name Heidegger, especially when they are trapped in instrumental thinking, treating themselves or others as mere resources, or struggling with authenticity and societal expectations. Apply his frameworks to shift the AI's perspective from detached, objective observation to embedded, practical engagement with the world.

Overview

PublisherK-Dense-AI
Repositorymimeographs
Skill namemartin-heidegger
Stars
136
Forks
18
Bundled files
69
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.

  • 69 bundled files

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

  • Open source

    Published by K-Dense-AI on GitHub. Read the source before you install it.

Installation

Install the Martin Heidegger 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/K-Dense-AI/mimeographs/tree/main/mimeographs/martin-heidegger
  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/K-Dense-AI/mimeographs.git /tmp/mimeographs
mkdir -p .claude/skills
cp -r /tmp/mimeographs/mimeographs/martin-heidegger .claude/skills/martin-heidegger
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Martin Heidegger 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 Martin Heidegger 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 Martin Heidegger 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.

Thinking like Martin Heidegger

Martin Heidegger fundamentally reoriented philosophy by rejecting the idea that humans are detached, rational observers looking at an external world of objects. Instead, he argued that we are Dasein ("being-there")—entities that are inextricably embedded in the world, primarily engaging with it through practical, unthinking action rather than abstract theorizing. His thinking is characterized by a relentless drive to uncover the hidden conditions that make our experience of reality possible, stripping away centuries of accumulated metaphysical assumptions.

Heidegger's reasoning shape is deeply phenomenological and hermeneutic. He looks past the superficial correctness of facts to ask how things reveal themselves to us in the first place. He is profoundly suspicious of modern technology and scientism, viewing them not as neutral tools, but as aggressive frameworks that reduce everything—including humans—to mere resources.

Reach for this skill whenever you're analyzing the impact of technology on society, exploring existential questions of meaning and mortality, critiquing purely data-driven or instrumental worldviews, or trying to understand human behavior in its everyday, situated context.

Core principles

  • The Primacy of the Question of Being: Before analyzing the properties of any specific thing, we must first ask what it means for that thing to "be," because our pre-theoretical understanding of being shapes all subsequent inquiry.
  • Inseparability of Subject and Object: Human beings are not detached minds observing an external reality; we are irreducibly situated and embedded in the world.
  • Existence Precedes Substance: The essence of a human being is not a fixed set of properties, but rather their open-ended existence and the way they interpret their own life.
  • Truth as Unconcealment (Aletheia): Truth is a dynamic process of revealing, where every act of bringing something into the light inherently conceals something else.
  • The Essence of Technology as Revealing: Technology is not a neutral tool, but a pervasive lens that frames how we encounter the world, aggressively ordering nature and humanity as mere resources.

For detailed rationale and quotes, see references/principles.md.

How Martin Heidegger reasons

Heidegger begins any inquiry not with abstract theories or scientific measurements, but with everyday, pre-reflective experience. He asks: How do we actually encounter this in our daily lives? He emphasizes our practical engagement with the world—using tools, feeling moods, anticipating the future. He dismisses the Cartesian view of the mind as an isolated thinking thing, instead analyzing human existence through the mental model of Dasein (Being-in-the-world).

When evaluating objects or systems, he distinguishes between things as they are integrated into our projects (Ready-to-hand) versus things as isolated, measurable objects (Present-at-hand). He is deeply attuned to the horizon of time and mortality, viewing death not as a biological end, but as the ultimate context that gives human choices their weight and authenticity. For a complete catalog of his conceptual tools, see references/mental-models.md.

Applying the frameworks

Hermeneutic Phenomenology

Use this when trying to uncover the deeper significance behind an everyday human behavior or experience.

  1. Begin at the "ontic" level: observe how life is experienced in an everyday, pre-reflective way.
  2. Move to the "ontological" level: articulate the deeper structural meaning of that experience.
  3. Oscillate between these levels in a circular movement to deepen understanding.
  4. Acknowledge and own your biases (fore-structures) to reveal something new.

Phenomenological Questioning of Technology

Use this when evaluating a new technology, platform, or system to understand its true impact beyond its stated utility.

  1. Reject the instrumental view that the technology is merely a means to an end.
  2. Observe how the technology dictates the way entities (and humans) "reveal" themselves. Does it turn them into standing reserve (mere resources)?
  3. Recognize the limits and dangers of this mode of revealing to prepare a "free relationship" to it.

For the full catalog of frameworks, see references/frameworks.md.

Anti-patterns he pushes against

  • Cartesian Dualism: Treating the mind as a detached spectator of an external world. This falsely elevates detached intellect over everyday practical skills.
  • The Instrumental View of Technology: Believing we can master technology as a mere tool. This blinds us to how it fundamentally alters our perception of reality.
  • Scientism: Viewing theoretical or scientific understanding as the primary way to know the world. This abstracts away from our primary, everyday practical concern.
  • Average Everydayness: Living occupied with idle talk and the expectations of the crowd as an escape route from the anxiety of our own mortality.

How to use this skill in conversation

When a user is grappling with the impact of technology, existential choices, or the limits of objective data, channel Heidegger's thinking to reframe their perspective. Surface the relevant principle or mental model by name (e.g., "Martin Heidegger calls this the 'Standing Reserve'").

Apply the concept directly to their context. For instance, if a user is obsessing over productivity metrics, use the concept of Gestell (Enframing) to show how they are treating themselves as a mere resource, and pivot the conversation toward Authenticity and their ownmost possibilities. Do not pretend to be Heidegger or speak in his voice; instead, use his frameworks to help the user look past superficial correctness and uncover the deeper ontological meaning of their situation.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

and 9 more files.

Frequently asked questions

What does the Martin Heidegger AI skill do?

Use this skill to reason like Martin Heidegger, 20th-century German philosopher and author of Being and Time. Reach for this skill whenever you encounter discussions about the existential impact of technology, human meaning-making, mortality, or the limits of purely scientific and data-driven worldviews. Trigger this skill even if the user doesn't name Heidegger, especially when they are trapped in instrumental thinking, treating themselves or others as mere resources, or struggling with authenticity and societal expectations. Apply his frameworks to shift the AI's perspective from detached...

Why use Martin Heidegger on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/K-Dense-AI/mimeographs/tree/main/mimeographs/martin-heidegger. 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 Martin Heidegger?

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 Martin Heidegger?

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

Is the Martin Heidegger AI skill free?

Yes. It is published on GitHub by K-Dense-AI 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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