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Thinking Circle Of Competence

CommunityPopular
tjboudreaux
thinking-circle-of-competence

Use when a specific claim may lack grounding. Check evidence boundary, size wrongness cost, then answer, fetch, or abstain — never confabulate.

Overview

Publishertjboudreaux
Repositorycc-thinking-skills
Skill namethinking-circle-of-competence
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 Circle Of Competence 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-circle-of-competence .claude/skills/thinking-circle-of-competence
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Thinking Circle Of Competence 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 Circle Of Competence 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 Circle Of Competence 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.

Circle of Competence

Treat competence as an evidence boundary, not self-image. Answer only when grounded; otherwise fetch, mark uncertainty, or abstain. The failure mode is a fluent fabrication treated as fact.

When to Use

  • About to assert a specific fact (API, path, number, config, version behavior) without a citable source from this session.
  • The claim is about this codebase or system and you have not read or run the relevant artifact.
  • Pattern reconstruction would sound complete but is unconfirmed.
  • Cost of a wrong confident answer is material (security, data, irreversible action, trust).

When NOT to Use

  • Claim is grounded in something you read, ran, or can cite this session, or is stable universal knowledge — answer without false humility.
  • Error cost is trivial and reversible and you will flag the claim as unverified.
  • Grounding is one cheap fetch away — fetch first; abstention is not an excuse to skip a check.
  • User explicitly wants labeled brainstorming or hypotheticals.

Procedure

  1. Name the claim. Isolate the assertion that would be treated as fact.
  2. Classify the zone.
    • Grounded: citeable session source (read/run) or stable non-version-specific knowledge.
    • Partial: general shape known; version/repo/config detail unconfirmed.
    • Ungrounded: would invent a concrete value with no source.
  3. Size wrongness cost. If high-stakes (security, data loss, irreversible ops), treat Partial as Ungrounded until verified.
  4. Act by zone.
    • Grounded → answer; cite when useful; do not hedge verified facts.
    • Partial → cheap fetch then answer; else answer only with explicit uncertainty and a check path.
    • Ungrounded → fetch if possible; else ask or abstain with what would enable an answer. Never invent.
  5. Refuse competence creep. Adjacent code, prior versions, or general capability do not ground the unread target.
  6. Stop. Emit answer, fetch request, or abstention. Do not round "probably" to a flat assertion.

Stop condition: Claim answered with grounding, marked partial with a check path, or refused with missing-evidence note.

Output

text
Claim: <assertion>
Zone: grounded | partial | ungrounded
Evidence: <source or none>
Wrongness cost: low | medium | high
Action: answer | fetch-then-answer | answer-with-uncertainty | abstain/ask
Response: <wording or missing artifact>

Verification

  • Falsify if a specific value was asserted with no session source and no uncertainty marker.
  • Falsify if Partial/high-stakes was treated as Grounded, or a cheap fetch was skipped for abstention theater.
  • Over-application guard: do not hedge or refuse claims verified this session.

Frequently asked questions

What does the Thinking Circle Of Competence AI skill do?

Use when a specific claim may lack grounding. Check evidence boundary, size wrongness cost, then answer, fetch, or abstain — never confabulate.

Why use Thinking Circle Of Competence on TypingMind?

Because you install it once and use it with any model. Thinking Circle Of Competence 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 Circle Of Competence in TypingMind?

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

Which AI models can use Thinking Circle Of Competence?

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 Circle Of Competence?

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

Is the Thinking Circle Of Competence 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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