How does this skill differ from popular third-party GitHub profile widgets?

This skill eliminates reliance on third-party widget servers that often experience downtime or rate limits. By generating static SVGs and committing them directly to your repository, your GitHub profile remains fast, reliable, and entirely self-hosted.

What are the technical requirements for running this skill?

The skill requires a Python 3.10+ environment and a GitHub Personal Access Token (PAT). It is designed to work seamlessly with any AI agent capable of running Python scripts or managing GitHub Actions workflows.

What exactly is included in the purchase?

You receive the complete Python rendering engine, a set of terminal-themed SVG templates, the JSONL tracking system, and pre-configured GitHub Action YAML files for automated daily updates.

Do I need to install any heavy external dependencies or rendering engines?

No, the skill is designed to be lightweight and uses the Python standard library for SVG generation. It does not require headless browsers or heavy rendering engines, making it ideal for fast execution in CI/CD environments.

Can I track my coding progress over time with these cards?

Yes, the skill includes a specific historical tracking feature that logs your stats to a JSONL file. This allows it to calculate and display your 'activity drift'—showing how your coding habits have changed over a rolling 30-day window.

Is it possible to customize the colors of the terminal-themed cards?

While the skill comes with optimized terminal themes (phosphor, amber, and paper), the SVG generation logic is modular. Users comfortable with CSS color values can easily adjust the theme flags to match their specific profile aesthetics.

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openclaw profile cards

Generate 13 terminal-themed SVG stat cards and self-updating README tables for GitHub profiles using Python.

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openclaw profile cards

Example session with this skill installed

Generate a full set of amber-themed profile cards for the GitHub user 'octocat' and save them to the assets/ directory.

Read your context and instructions
Compiled the openclaw profile cards

I have generated the GitHub profile cards for user

octocat using the amber theme. The process included a live GraphQL data fetch, generation of 13 SVG assets, and an update to the stats table markers.

Profile Assets Generated

Card Name	File Path	Description
Whoami	`assets/cards/ubuntu.svg`	Terminal-style user identity block
Heatmap	`assets/cards/heatmap.svg`	Annual contribution calendar with quartile shading
Skyline	`assets/cards/city.svg`	Isometric repository activity visualization
Rhythm	`assets/cards/rhythm.svg`	24-hour by 7-day commit frequency map
Drift	`assets/cards/drift.svg`	30-day language usage change analysis
Registry	`assets/cards/registry.svg`	Repositories formatted as deployed ML models
Oscilloscope	`assets/cards/scope.svg`	Daily activity rendered as a waveform

The history.jsonl file has been updated with a new snapshot to track future activity drift.

Next steps

Commit the new .svg files in assets/cards/ to your repository.
Add <img src="assets/cards/heatmap.svg" width="100%"> to your profile README.md.
Set up examples/self-updating.yml in .github/workflows/ for automated daily updates.

Connects securely to your tools. The creator never sees your data.

About this skill

GitHub profile READMEs often rely on third-party widget services that suffer from downtime or rate limits. This skill provides a self-hosted alternative that generates 13 high-fidelity, terminal-themed SVG stat cards directly from GitHub's GraphQL API. It produces static assets you can commit to your own repository, ensuring your profile stays fast and functional without external dependencies.

What it does

Live Data Fetching gathers contribution, repository, and language stats using a single GitHub GraphQL round trip.
SVG Generation renders 13 distinct visualisations including heatmaps, city skylines, and terminal-style whoami cards.
Visual Theming applies consistent phosphor, amber, or paper color palettes across all generated assets.
Historical Tracking maintains a JSONL snapshot log to calculate activity drift over 30-day windows.
README Integration builds and updates a markdown stats table within protected comment markers.
Automated Workflow provides GitHub Action templates for daily stat refreshes and auto-commits.

How it works

Fetch runs the rendering script with a GITHUB_TOKEN to pull live account data.
Theme applies the --theme flag to set the visual style for all SVGs.
Build updates the table.build markdown block between specific comment markers in the README.
Deploy sets up the self-updating.yml action to automate the daily render and commit cycle.

Frameworks & tools

Python 3.10+ (standard library), GitHub GraphQL API, and SVG. No external rendering engines or heavy dependencies required.

Why this beats prompting it yourself

Generating valid, hand-drawn SVGs via raw prompting is prone to syntax errors and broken layout logic. This skill provides a tested rendering engine that handles complex data mapping for specific visualizations like 7x24 rhythm heatmaps and converging loss curves.

Use cases

Create a terminal-themed GitHub profile that doesn't rely on external widget servers.
Track coding "drift" by comparing current language usage against 30-day snapshots.
Generate a 3D-style city skyline of repository activity without third-party tools.
Automate profile updates using a local GitHub Action to keep stats fresh daily.

Known limitations

Requires a GitHub PAT with repo scope for private data. SVGs use system monospace fonts only to ensure compatibility with GitHub's camo proxy.

How to use

Skills on Agensi run inside your AI agent. Copy the prompt, paste it into your agent, and it connects to Agensi and installs this skill for you. If a payment is needed, your agent shows it to you first.

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openclaw profile cards