Explore Quantified Self Routes

Community

jimmykane

explore-quantified-self-routes

Explore the user's authorized Quantified Self saved routes through its read-only MCP tools. Use for route summaries, activity types, route geometry, bounds, segments, waypoints, or finding saved routes near a place or coordinate; do not use saved-route access to answer nearby activity-history questions.

Overview

Publisher	jimmykane
Repository	`quantified-self`
Skill name	`explore-quantified-self-routes`
Stars	228
Forks	32
Bundled files	1
Links	Skill source Repository Homepage

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.
1 bundled files
Scripts, templates, and references the model can read while it works. Files are read-only and never executed.
Open source
Published by jimmykane on GitHub. Read the source before you install it.

Installation

Install the Explore Quantified Self Routes AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

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.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
Enable the skill in any chat where you want it available.

Store

Installed

Skills

Add skills

Import one or more skills from a public GitHub folder or a local .skill or .zip archive, or SKILL.md file.

From GitHub URLFrom .skill, .zip, or SKILL.md

GitHub repo or folder

https://github.com/jimmykane/quantified-self/tree/main/plugins/quantified-self/skills/explore-quantified-self-routes

Points to a single skill folder, or any folder that contains multiple skills. Public repos only.

CancelContinue

Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.

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/jimmykane/quantified-self.git /tmp/quantified-self
mkdir -p .claude/skills
cp -r /tmp/quantified-self/plugins/quantified-self/skills/explore-quantified-self-routes .claude/skills/explore-quantified-self-routes

Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Explore Quantified Self Routes 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 Explore Quantified Self Routes 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 Explore Quantified Self Routes 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.

Explore Saved Routes

Use saved-route summaries first and request coordinate-bearing route data only when the question requires it.

Workflow

List bounded route summaries and retain the opaque route reference for follow-up requests. When the user names a sport, discover its canonical activity type first and apply the server-side activity-type filter. Use the case-insensitive route-name search for a named route or partial name.
Preserve the original activity-type and name filters with every returned cursor. Continue until the requested match is found or the scan reports completion; distinguish a complete no-match from older route history that remains.
Request geometry for mapping or segment analysis and waypoints only when those details are relevant.
For nearby searches, prefer direct coordinates when the user provides them. Explain that place-name searches send the location text to Mapbox, while direct-coordinate searches do not.
Preserve returned units, route and waypoint counts, bounds, geometry format, pagination, and update timestamps.

Permissions and Privacy

routes:read provides non-location route summaries.
route-location:read separately gates bounds, preview geometry, segment positions, nearby-route search, and waypoint coordinates, altitude, and distance. Reject an explicit location request rather than silently downgrading it.
Activity-location permission does not grant route-location access, and route-location permission does not expose activity history.
Do not expose internal IDs, source files, storage paths, provider provenance, or full-resolution route recordings.
Treat a missing permission, absent geometry, empty search page, scan limit, and missing waypoints as different outcomes.

Response

Lead with the matching route or route set, then show distance, activity type, geometry, or waypoint evidence.
State whether location was redacted and note any pagination or scan limitation next to the result.

Bundled files

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

agents/openai.yaml1 KB

Frequently asked questions

What does the Explore Quantified Self Routes AI skill do?

Why use Explore Quantified Self Routes on TypingMind?

Because you install it once and use it with any model. Explore Quantified Self Routes 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 Explore Quantified Self Routes in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/jimmykane/quantified-self/tree/main/plugins/quantified-self/skills/explore-quantified-self-routes. 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 Explore Quantified Self Routes?

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 Explore Quantified Self Routes?

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

Is the Explore Quantified Self Routes AI skill free?

It is published on GitHub by jimmykane. Check the repository for licensing terms. 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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Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

Explore Saved Routes

Workflow

Permissions and Privacy

Response

Bundled files

Frequently asked questions

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