MCP App StoreMCP App StoreThis skill detects available CJK (Chinese/Japanese/Korean) fonts on a system and configures matplotlib (and static Plotly exports) so that labels, titles and legends render correctly instead of showing tofu/blank squares. It provides a helper script and inline snippets that either set global rcParams when a .ttf is available or fall back to explicit FontProperties for .ttc collections. The skill also documents installation suggestions and cache-clearing steps for common Docker/Linux environments.
Use before running any plotting code that includes non‑ASCII CJK text — titles, axis labels, legend entries, annotations, or saved static images. It's intended for headless environments (CI, Docker) and local workstations where font availability is uncertain. Run it as the first step in visualization pipelines that will generate PNG/SVG exports.
Designed for use with coding assistants that can run Python (Claude Code, Codex, Copilot/Copilot Labs, local Python helpers). It is a local helper rather than a UI component and integrates smoothly into automated plotting workflows.
CJK font detection and matplotlib configuration skill. Script ran cleanly, detecting no CJK fonts in this environment and providing helpful install hints. Code is well-structured with clear fallback logic (ttf→ttc→install), good docstrings, and thoughtful .ttc workaround documentation. No security concerns — purely local filesystem reads and matplotlib config.
Solid niche utility skill. SKILL.md has excellent progressive disclosure with a dedicated 'key pitfall' section on .ttc vs .ttf behavior — genuinely useful practical knowledge. Script is clean, no network calls or security concerns.
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