Thinking Lindy Effect logo

Thinking Lindy Effect

CommunityPopular
tjboudreaux
thinking-lindy-effect

Use when longevity of a non-perishable option matters. Treat survival duration as a remaining-life prior, then check domain drift before favoring the proven.

Overview

Publishertjboudreaux
Repositorycc-thinking-skills
Skill namethinking-lindy-effect
Stars
1.3K
Forks
158
Bundled files
Instructions only
LicenseMIT
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 tjboudreaux on GitHub. Read the source before you install it.

Installation

Install the Thinking Lindy Effect 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/tjboudreaux/cc-thinking-skills.git /tmp/cc-thinking-skills
mkdir -p .claude/skills
cp -r /tmp/cc-thinking-skills/skills/thinking-lindy-effect .claude/skills/thinking-lindy-effect
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Thinking Lindy Effect 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 Thinking Lindy Effect 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 Thinking Lindy Effect 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.

Lindy Effect

For non-perishable ideas, technologies, and practices, expected remaining life scales with current survival age. Prefer proven survivors unless the new option clears a burden of proof or the domain has drifted.

When to Use

  • Choosing languages, frameworks, databases, protocols, patterns, or dependencies where long-term survival matters.
  • Skill or architecture bets whose value depends on lasting relevance.
  • Ranking options when ages differ materially and the choice outlives a short experiment.

When NOT to Use

  • Perishable targets: specific SaaS vendors, hardware, fashion, or products that can shut down regardless of concept age.
  • Active paradigm discontinuity where age in the old regime is weak evidence.
  • Throwaway work where longevity is irrelevant — optimize for fit and speed.
  • Treating "older" as "optimal for a new requirement"; survival predicts further survival, not best fit.

Procedure

  1. Confirm non-perishable scope. Concept/tech/practice continues; vendor/device → score fit/risk only and stop.
  2. Record survival age. First significant production use and current age (ecosystem-relative if the ecosystem is young).
  3. Form the Lindy prior. Expected remaining life ≈ current age; mark confidence from age and continued active use.
  4. Run domain-drift checks. Problem class changed? Paradigm shift invalidating old assumptions? New option uniquely closes a real present gap?
  5. Assign burden of proof. Default to the older adequate option. Accept newer only for a stated necessary advantage the Lindy option cannot meet at acceptable cost.
  6. Decide with residual risk. Pick primary; note impact if the prior is wrong and any fallback.

Stop condition: Primary chosen with age prior, drift check, and why new did or did not meet burden of proof.

Output

text
Options: <name, age, Lindy prior>
Drift: stable | discontinuous — <note>
Burden: on new | waived because <gap>
Decision: <primary>
Rejected: <one line each>
If Lindy wrong: <impact + fallback>

Verification

  • Falsify if age was used without non-perishable scope, or a paradigm shift was ignored.
  • Falsify if a new option was rejected solely for youth despite a documented necessary gap.
  • Over-application guard: skip throwaway prototypes and perishable vendor bets where fit and exit cost dominate.

Frequently asked questions

What does the Thinking Lindy Effect AI skill do?

Use when longevity of a non-perishable option matters. Treat survival duration as a remaining-life prior, then check domain drift before favoring the proven.

Why use Thinking Lindy Effect on TypingMind?

Because you install it once and use it with any model. Thinking Lindy Effect 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 Thinking Lindy Effect in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/tjboudreaux/cc-thinking-skills/tree/main/skills/thinking-lindy-effect. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Thinking Lindy Effect?

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 Thinking Lindy Effect?

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

Is the Thinking Lindy Effect AI skill free?

Yes. It is published on GitHub by tjboudreaux 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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