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Thinking Map Territory

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tjboudreaux
thinking-map-territory

When a claim, doc, test, metric, or assumption conflicts with observed behavior, stop theorizing from the map and verify the live code or data; let territory overrule.

Overview

Publishertjboudreaux
Repositorycc-thinking-skills
Skill namethinking-map-territory
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 Map Territory 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-map-territory .claude/skills/thinking-map-territory
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Thinking Map Territory 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 Map Territory 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 Map Territory 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.

Map Territory

Docs, tests, diagrams, metrics, comments, and mental models are maps. Running code and actual data are the territory. When they disagree, verify territory and update the model — never force reality to match the description.

When to Use

  • Observed behavior contradicts a doc, test expectation, diagram, comment, dashboard, or prior assumption.
  • A claim about the system rests on a secondary source rather than current code or data.
  • Tests pass but production or manual behavior is wrong.
  • You are about to theorize why something happens before inspecting what happens.
  • A decision depends on whether a model, schema, or metric is still current.

When NOT to Use

  • The map is the artifact you are asked to edit (doc, diagram, spec) — that artifact is the task territory.
  • Same path already verified this session — reuse that observation.
  • Map is authoritative and generative (codegen types, derived schema) with no claimed drift.
  • The mismatch cannot change the decision — note and move on.
  • Competing causal hypotheses after territory is confirmed — switch to scientific-method.
  • Security exploit construction — use red-team; this skill only settles model-versus-observation.

Procedure

  1. Name the map. State the exact representation trusted: which doc, test, metric, diagram, comment, or assumption. Quote the claim, not a paraphrase.
  2. Name the territory check. Specify the observation that would prove or disprove the claim: code path, runtime value, query, reproduction, recent change, or live metric.
  3. Verify source and freshness. Confirm origin of the map (author, generator, last update) and whether it could be stale relative to deploy, config, or data change. Prefer primary current sources over summaries.
  4. Observe the territory. Read the real code path, run or instrument it, query real data, or reproduce the behavior. Do not predict from signatures or names alone.
  5. Record the delta and update. If territory contradicts the map, territory wins for shipped behavior. Document the gap, revise the model, then decide the fix (code, map, or both). Note unmapped paths — likely next failure sites.
  6. Stop when settled. Once the claim is confirmed or overturned with a concrete observation, stop re-checking the same path.

Output

text
Map: <claim + source + freshness>
Territory check: <what was inspected/run/queried>
Observation: <concrete result, not interpretation>
Delta: <aligned | map wrong | territory incomplete>
Updated model: <corrected understanding>
Action: <fix code | update map | no decision impact>
Uncovered: <paths no map covers>

Verification

  • Falsify if you reasoned from the map without a territory observation, trusted a stale secondary source, or forced code to match a wrong doc without checking either.
  • Stop when the contradiction is resolved or irrelevant to the decision.
  • Over-application guard: do not re-verify the whole system when editing the map, when the map is the generative source of truth, or when the gap cannot change the next action.

Frequently asked questions

What does the Thinking Map Territory AI skill do?

When a claim, doc, test, metric, or assumption conflicts with observed behavior, stop theorizing from the map and verify the live code or data; let territory overrule.

Why use Thinking Map Territory on TypingMind?

Because you install it once and use it with any model. Thinking Map Territory 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 Map Territory in TypingMind?

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

Which AI models can use Thinking Map Territory?

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 Map Territory?

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

Is the Thinking Map Territory 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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