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Thinking Via Negativa

CommunityPopular
tjboudreaux
thinking-via-negativa

Use when the reflex is to add a feature, layer, or process. Prefer removing harmful or nonessential elements first, with an irreversibility guard before deletion.

Overview

Publishertjboudreaux
Repositorycc-thinking-skills
Skill namethinking-via-negativa
Stars
1.3K
Forks
158
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 tjboudreaux on GitHub. Read the source before you install it.

Installation

Install the Thinking Via Negativa 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-via-negativa .claude/skills/thinking-via-negativa
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Thinking Via Negativa 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 Via Negativa 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 Via Negativa 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.

Via Negativa

Improve by subtraction before addition. Prefer removing harm, waste, and nonessential complexity; add only when a demonstrated need remains after removal candidates are exhausted.

When to Use

  • About to add a feature, abstraction, dependency, process, or control to fix a problem.
  • Simplifying a system or workflow where complexity is the tax.
  • Prioritizing by deciding what not to keep, build, or maintain.
  • Performance or reliability work where eliminating a bad path beats bolting on mitigation.

When NOT to Use

  • Load-bearing controls: auth, validation, tests, rate limits, retries, safety checks — presume necessary until proven dead.
  • A demonstrated requirement that cannot be met by removal or simplification.
  • Aesthetic minimalism without evidence of non-use or net harm.
  • Irreversible deletion without a rollback path when impact is unknown.

Procedure

  1. Pause the add reflex. State the goal and the proposed addition in one line.
  2. Ask the subtraction question first. List what could be removed or stopped to achieve the same goal with less surface area.
  3. Catalog candidates with evidence. Prefer unused, redundant, high-cost/low-value, or harmful elements. Require usage, call-graph, metrics, or experiment evidence — not taste.
  4. Apply the irreversibility guard. Classify reversible vs hard-to-restore; identify dependents; refuse deleting unproven mystery guards. Plan staged removal or flag when risk is non-trivial.
  5. Remove the safest high-value candidate first. Subtract, monitor, and verify absence of needed behavior before the next removal.
  6. Add only if the goal still fails. If subtraction cannot meet the need, add the minimum change and record why removal was insufficient.
  7. Stop when the goal is met by absence, or remaining candidates fail the irreversibility/evidence bar and a minimal addition is justified.

Stop condition: Goal achieved via removal, or residual need documented after evidence-backed subtraction failed.

Output

text
Goal: <desired outcome>
Proposed add (if any): <thing>
Removal candidates: <element — evidence — risk — reversible?>
Action: remove <X> | staged remove <X> | add minimal <Y> because <why removal failed>
Verification plan: <how absence/success is checked>
Do-not-touch: <load-bearing items preserved>

Verification

  • Falsify if something was deleted without non-use/harm evidence, or a safety control was removed as "complexity."
  • Falsify if an addition shipped without a prior subtraction pass on the same goal.
  • Over-application guard: do not delete for line-count or purity when the element is load-bearing or the need is demonstrated.

Frequently asked questions

What does the Thinking Via Negativa AI skill do?

Use when the reflex is to add a feature, layer, or process. Prefer removing harmful or nonessential elements first, with an irreversibility guard before deletion.

Why use Thinking Via Negativa on TypingMind?

Because you install it once and use it with any model. Thinking Via Negativa 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 Via Negativa in TypingMind?

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

Which AI models can use Thinking Via Negativa?

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 Via Negativa?

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

Is the Thinking Via Negativa 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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