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Fit Ride Studio

Community
op7418
fit-ride-studio

Analyze one or more cycling, running, or hiking FIT/KML activity tracks, generate evidence-based single or aggregate reports, open the bundled local Ride Relief report page when the environment supports it, enrich results with user-confirmed public location context, and create shareable PNG or H.264 MP4 route stories on request. Use whenever a user wants to analyze, compare, visualize, or export .fit or .kml activities, including requests that mention a workout but do not yet include track files.

Overview

Publisherop7418
Repositoryguizang-sports-skill
Skill namefit-ride-studio
Stars
149
Forks
12
Bundled files
38
LicenseAGPL-3.0
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.

  • 38 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by op7418 on GitHub. Read the source before you install it.

Installation

Install the Fit Ride Studio 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.
    https://github.com/op7418/guizang-sports-skill/tree/main
  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/op7418/guizang-sports-skill.git \
  .claude/skills/fit-ride-studio
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Fit Ride Studio 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 Fit Ride Studio 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 Fit Ride Studio 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.

FIT & KML Activity Studio

Turn local cycling, running, and hiking tracks into an evidence-based report and local visual experience. Follow this sequence:

  1. Ask for FIT/KML files when none were provided, or use the bundled synthetic demo when requested.
  2. Check the installation, install missing locked dependencies automatically, and analyze every valid file.
  3. Present the result, stage private runtime files, and open the bundled Ride Relief report page when local browser access is available.

Enter the export composer only when the user asks for a shareable image, animation, or video. Ride Relief is the web app bundled inside this Skill, not a separate product to install.

Workflow

1. Get the track files

  • When the user explicitly asks for a demo, use <skill-root>/samples/demo.kml. State that it is fictional data.
  • Otherwise, if no readable .fit or .kml was supplied, ask in the user's language for either an attachment or a local path. Example in Chinese: “请把 .fit 或 .kml 拖进对话,或告诉我文件路径(例如 ~/Downloads/ride.fit);可以一次给多个。”
  • Stop after requesting missing input. Do not invent activity data and do not ask the user to configure the project.
  • Proceed immediately when at least one file is accessible. Infer single versus aggregate mode from valid files.
  • Accept a directory only when the user placed it in scope; expand only top-level FIT/KML files.
  • Process tracks locally. Do not upload exact GPS tracks or expose coordinates in the response.
  • Continue a batch when one file is corrupt, provided another track is valid. Report skipped basenames.
  • Detect likely duplicates by start time, distance, and duration. Exclude them from totals and identify them.

2. Check readiness and analyze

Resolve <skill-root> as the directory containing this SKILL.md. Run from that directory:

bash
node scripts/check-install.mjs

Handle its single-line status deterministically:

  • OK — continue.
  • NEED_INSTALL — run npm ci, rerun the check, then continue. Use the lock file; do not substitute npm install.
  • UNSUPPORTED_NODE — explain the reported Node requirement and stop before running the app.
  • ERROR — report the missing/broken Skill files and reinstall or update the Skill before continuing.

Treat npm ci as internal preparation, not a routine user step. If a sandbox blocks network access, request network permission when the environment supports approvals. If permission is unavailable, tell the user to run npm ci once from <skill-root>. If the Skill directory is read-only, explain that a writable installation or checkout is required; do not loop on installation or claim success.

Run the deterministic analyzer:

bash
node scripts/analyze-fit.mjs --format json <fit-or-kml-path> [more-track-paths...]

Use --format markdown when a ready-to-read local report is sufficient. Use --keep-duplicates only after the user confirms matching activities are distinct. Read references/report-contract.md when interpreting fields, checking weighting rules, or changing report behavior.

3. Present the result

Start with the result, not dependency or parser details.

For one activity, present:

  1. A compact terrain/route verdict and the most important observation.
  2. Distance and ascent when available; show duration, speed/pace, calories, and pauses only when supported.
  3. Sport-appropriate recorded metrics: cycling speed/cadence, running pace/step cadence, hiking elevation/ascent, plus heart rate, power, temperature, and grade when present.
  4. Data-quality coverage, especially cadence, heart rate, power, and grade.
  5. Two to eight concrete suggestions ordered by impact.

For multiple activities, present:

  1. Overall totals and date range.
  2. A per-activity table with sport and source format.
  3. Longest, most climbing, and fastest only within comparable activities with time data.
  4. Earliest-to-latest deltas as observations, not proof of fitness improvement.
  5. Sensor consistency, duplicates, and skipped files.
  6. Aggregate suggestions plus important activity-specific exceptions.

Keep absent data distinct from zero. Say “未记录” / “not recorded” or “覆盖不足” / “insufficient coverage,” never zero when a sensor was unavailable. Do not invent FTP, power zones, heart-rate zones, VO₂max, training load, recovery status, or medical conclusions unless the user supplies the necessary thresholds and explicitly requests that analysis.

Apply these rules:

  • Compute aggregate average speed as total distance divided by total moving time.
  • Never infer duration, speed, pace, pauses, or photo-time matching from geometry-only KML.
  • Prefer KML document metadata for declared sport, distance, ascent, and descent. Use the longest LineString or gx:Track and retain the source basename.
  • Weight cadence, heart rate, power, and temperature by valid sample counts.
  • Treat calories as device estimates and temperature as ambient/device temperature.
  • Describe cadence as an activity-pattern observation, not a universal prescription. Confirm the running-cadence convention before interpreting spm.
  • When grade coverage is adequate, report distance-weighted grade bands, smoothed P99 representative grade, and up to three continuous climbs; label the device maximum as raw.
  • Match heat/hydration suggestions to recorded moving duration.
  • Warn before sharing a highly closed route; recommend hiding the first and last 300–500 m.

Choose two to six evidence-backed deep dives. Omit empty or speculative dimensions.

When place-aware advice would materially improve the result, read references/location-enrichment.md. Confirm ambiguous public place names, search by the confirmed name rather than coordinates, and time-stamp live conditions. Do not treat an old activity as evidence that a route is currently open.

4. Stage and open the report page

Attempt this automatically after successful analysis, but adapt to the environment.

  1. Read references/ui-report-contract.md.
  2. Write the model-authored report contract to a writable temporary JSON file. Do not store per-user analysis in public/skill-analysis.json.
  3. For a batch, select the user's requested activity; otherwise use the longest non-duplicate as the representative route while keeping the text response aggregate.
  4. Stage the selected track and analysis in the private OS temporary directory:
bash
node scripts/prepare-report.mjs --track <selected-track> --analysis <temporary-report-json>
  1. Read the returned JSON object. Keep its url and session; the URL expires after 24 hours, stale files are cleaned on the next staging run, and nothing is stored in the repository.
  2. Fetch http://127.0.0.1:5174/ and treat it as this project only when the returned HTML contains both <title>Route Relief and id="fit-input". Otherwise start npm run dev from <skill-root>. The server uses port 5174 with strictPort; if another application owns the port, report the conflict instead of silently switching ports.
  3. Use any available browser-opening or browser-automation capability to open the returned URL. The URL auto-loads the track and analysis; do not rely on setting a file input.
  4. With screenshot/visual capability, verify the loaded source, the Fit Ride Studio Skill analysis badge, recommendation layout, and clipping. Without visual capability, skip visual claims and ask the user to inspect the opened page.

Apply these fallbacks honestly:

  • If the Agent can open URLs but cannot automate page controls, open the URL; no further interaction is required for the report.
  • If no browser tool exists but the user shares the same machine, provide the returned URL and say to open it manually.
  • If running remotely or in a cloud container where the user's browser cannot reach that loopback address, deliver the text report and temporary analysis JSON instead. Do not claim the page opened.
  • If staging is impossible because every writable temporary location is blocked, fall back to the existing file-input workflow and give concise manual steps.

Track the terminal/process handle used to start Vite. Before finishing, state whether the local server remains running and how to stop that exact process (for example, Ctrl+C in its terminal). Do not use a broad kill command. Clean a staged report after the user is finished when practical:

bash
node scripts/prepare-report.mjs --cleanup <session-id>

Opening the report page does not authorize export or switching to 导出创作.

5. Handle export requests

Read references/export-workflow.md before operating the export UI.

  • Follow the user's activity selection; otherwise use a clear preference such as latest, longest, or most climbing.
  • Prefer user media. Disclose when a built-in abstract background will be used.
  • Default to 9:16, H.264 MP4 for motion, whole-activity summary card, watermark enabled, and safe placement. Use a 10-second single-background rotation or 2.5 seconds per route photo.
  • Before rendering, tell the user that Chromium may open a native save picker that requires their own click; otherwise the browser downloads folder is used.
  • Inspect the preview when visual capability exists. Without it, do not claim bounds were verified; ask the user to check the preview before the expensive render.
  • Choose PNG for a still and MP4 for animation/video.
  • Never claim success until rendering finishes and save/download is confirmed.

Usability defaults

  • Use basenames in reports and watermarks; omit absolute paths.
  • Explain missing sensors once in the data-quality section.
  • Preserve the current export layout when revisiting the composer.
  • Avoid one “average activity” conclusion for mixed sports.
  • Keep user files and generated reports out of the repository. Runtime copies live in the OS temporary directory; npm packages live in the Skill directory and may be replaced by updates.

Examples

  • “帮我分析一次运动。” with no file → Ask in Chinese for a dragged file or local path.
  • “Show me the demo.” → Use samples/demo.kml, state that it is synthetic, analyze it, and open the local report when reachable.
  • The user provides one FIT → Check/install, analyze, present, stage, and open the URL-loaded report.
  • “把这 8 次骑行综合分析一下。” → Analyze valid non-duplicates and open the longest ride as the representative report unless another is named.
  • “只看看数据,先别导出。” → Analyze and open the report page, but do not enter the export composer.
  • A remote Agent cannot expose loopback → Deliver the text report and JSON, explain why the local page is unavailable, and do not pretend it opened.

Bundled files

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

Frequently asked questions

What does the Fit Ride Studio AI skill do?

Analyze one or more cycling, running, or hiking FIT/KML activity tracks, generate evidence-based single or aggregate reports, open the bundled local Ride Relief report page when the environment supports it, enrich results with user-confirmed public location context, and create shareable PNG or H.264 MP4 route stories on request. Use whenever a user wants to analyze, compare, visualize, or export .fit or .kml activities, including requests that mention a workout but do not yet include track files.

Why use Fit Ride Studio on TypingMind?

Because you install it once and use it with any model. Fit Ride Studio 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 Fit Ride Studio in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/op7418/guizang-sports-skill/tree/main. 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 Fit Ride Studio?

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 Fit Ride Studio?

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

Is the Fit Ride Studio AI skill free?

Yes. It is published on GitHub by op7418 under the AGPL-3.0 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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