Agent skill

Fit Ride Studio

by op7418 in op7418/guizang-sports-skill

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…

AGPL-3.0Auto-check passedProductivity & Automation

Install Fit Ride Studio

skills CLI
$ npx skills add op7418/guizang-sports-skill --skill fit-ride-studio -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install op7418/guizang-sports-skill fit-ride-studio --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
fit-ride-studio
GitHub stars
147
Token cost
~2.8k tokens
SKILL.md length
1,493 words
Files
48 (incl. scripts, references)
Skills in repo
1
Repo updated
First seen
Licence
AGPL-3.0

At a glance

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…

  • Works in 5 steps: Get the track files → Check readiness and analyze → Present the result → …
  • A user wants to analyze
  • SKILL.md covers Workflow, Usability defaults and Examples
  • Calls npm and node

What it does

Fit Ride Studio is an agent skill from op7418/guizang-sports-skill. 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.

Its SKILL.md is about 2.8k tokens, which your agent loads only when the skill is triggered. The skill folder holds 52 other files, including scripts and reference files (for example `README.md`, `agents/openai.yaml` and `package-lock.json`).

It sits in Productivity & Automation, covering Health and fitness tracking. It works with Three.js. The repository describes itself as: 🚴 Claude Code / Codex skill for FIT & KML sports analysis — cycling, running, hiking, sensor and grade insights, local 3D route reports, PNG & H.264 MP4 exports. 运动轨迹分析与 3D 路线故事. The licence is AGPL-3.0.

When your agent uses it

  • A user wants to analyze
  • .kml activities
  • Including requests that mention a workout but do not yet include track files

Example prompts

  • “/fit-ride-studio”

Requirements

  • Node.js

Workflow steps

5 steps, taken from the step headings in SKILL.md.

  1. Get the track files
  2. Check readiness and analyze
  3. Present the result
  4. Stage and open the report page
  5. Handle export requests

What it can do on your machine

Read from SKILL.md and the folder at commit f165bf2. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships 1 file in scripts/, which the agent can run.

    Shell commands in SKILL.md call:

    • npm
    • node

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md. Its commands use npm, which can reach the network depending on how they are called.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Fit Ride Studio loads about 2.8k tokens when it runs, and up to ~7k if it reads all its reference files. Until then it costs about 129 tokens; SKILL.md has 1,493 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~129
When it runs · the whole SKILL.md, loaded when a task matches
~2.8k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~7k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.

SKILL.md

The full file from op7418/guizang-sports-skill at commit f165bf2, republished under its AGPL-3.0 licence (© op7418). 1,493 words, ~2,814 tokens.

Download SKILL.mdSave it as .claude/skills/fit-ride-studio/SKILL.md (or your agent's skills folder). This skill also uses 47 other files; get the full folder from GitHub.
name
fit-ride-studio
description
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.

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.

Show full SKILL.md (693 more words)Show less
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.

© op7418, AGPL-3.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 47 other files (scripts, references) in the repository root of op7418/guizang-sports-skill.

  • SKILL.md
  • .gitignore
  • LICENSE
  • README.md
  • agents/openai.yaml
  • docs/media/.gitkeep
  • index.html
  • package-lock.json
  • package.json
  • public/README.txt
  • public/backgrounds/abstract-chrome.png
  • public/backgrounds/abstract-green.jpg
  • public/backgrounds/abstract-wave.png
  • public/backgrounds/blue-blocks-QM6sJUVoGgI.jpg
  • public/backgrounds/blue-panels-Epjx67LMVqY.jpg
  • public/backgrounds/clear-spiral-FV8ZkyFXbzs.jpg
  • … and 32 more

Open the folder on GitHubat commit f165bf2

Compare with similar skills

Fit Ride Studio next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Fit Ride Studio compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Fit Ride Studio this skillop7418/guizang-sports-skill147—~2.8kAutomated safety check: PassAGPL-3.0
Healthkitdpearson2699/swift-ios-skills1.2k—~4.2kAutomated safety check: PassCustom licence
NutrigxClawBio/ClawBio1.2k1 repos~3.7kAutomated safety check: PassMIT
Coachfelixrieseberg/claude-coach1991 repos~4.9kAutomated safety check: PassMIT
Withings Health Data Readerwin4r/MuseAI-Skills3431 repos~1.3kAutomated safety check: PassNone
Healthmd CLICodyBontecou/health-md230—~4.8kAutomated safety check: PassAGPL-3.0

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Works with

Questions about Fit Ride Studio

What does Fit Ride Studio 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…. Fit Ride Studio is an agent skill from op7418/guizang-sports-skill.264 MP4 route stories on request.

When should I use Fit Ride Studio?

Fit Ride Studio fits situations like: A user wants to analyze; .kml activities; including requests that mention a workout but do not yet include track files.

How do I install Fit Ride Studio in Claude Code?

Run `npx skills add op7418/guizang-sports-skill --skill fit-ride-studio -a claude-code`. Or copy the skill folder (the op7418/guizang-sports-skill repository) into .claude/skills/fit-ride-studio in your project. Claude Code loads it when a task matches its description.

How do I install Fit Ride Studio in Codex?

Run `npx skills add op7418/guizang-sports-skill --skill fit-ride-studio -a codex`. Or copy the skill folder (the op7418/guizang-sports-skill repository) into .agents/skills/fit-ride-studio in your project. Codex loads it when a task matches its description.

Can I use Fit Ride Studio in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add op7418/guizang-sports-skill --skill fit-ride-studio -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/fit-ride-studio, .gemini/skills/fit-ride-studio, .github/skills/fit-ride-studio and .opencode/skills/fit-ride-studio in your project.

What does Fit Ride Studio need to run?

Going by SKILL.md and its folder, Fit Ride Studio needs the command-line tools its instructions call (npm and node). Our summary lists: Node.js.

Does Fit Ride Studio access the network?

SKILL.md contains no URLs. Its commands use npm, which can reach the network depending on how they are called. This is read from the text; nothing was executed.

Is Fit Ride Studio safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Fit Ride Studio use?

Fit Ride Studio is published under the AGPL-3.0 licence (from the LICENSE file in the skill folder). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Fit Ride Studio use?

About 2.8k tokens (SKILL.md is roughly 11k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 4.2k tokens, read only when the agent opens those files.

What are the alternatives to Fit Ride Studio?

Skills that share tags, products or a category with Fit Ride Studio: Healthkit (dpearson2699/swift-ios-skills, 1.2k stars), Nutrigx (ClawBio/ClawBio, 1.2k stars), Coach (felixrieseberg/claude-coach, 199 stars) and Withings Health Data Reader (win4r/MuseAI-Skills, 343 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Fit Ride Studio?

op7418 (a GitHub user) maintains it in op7418/guizang-sports-skill, which has 147 GitHub stars. The repository was last updated on August 9, 2026.

Source: op7418/guizang-sports-skill on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.