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agents-progressive-disclosure 中文说明 is an agent skill for refactoring bloated , , or similar agent instruction files into a compact routing entrypoint plus focused on-demand docs or rules files. It helps keep always-loaded instructions small while preserving detailed rules in on-demand documentation. Purpose Agent instruction files tend to grow over time. Teams add command examples, search policies, package-manager rules, deployment notes, tool-specific caveats, and project conventions until the root file becomes a dense rule warehouse. That creates two problems: The most important rules lose signal because they sit beside low-frequency details. Every task pays the context cost for instructions that only matter in specific situations. This skill turns the root instruction file back into a router. Philosophy The core idea is progressive disclosure: Keep high-frequency, long-lived, must-always-follow rules in the entrypoint. Move detailed task-specific guidance into focused on-demand docs or rules files. Tell future agents exactly which doc or rule file to read for each task type. Preserve rule intent while reducing always-loaded context.
| Stars | 62 |
| Forks | 4 |
| Category | AI Tool |
| Quality Score | 65.2574720742818/100 |
| Last Updated | 2026-06-30 |
| Created | 2026-05-28 |
| Platforms | claude-code |
| Est. Tokens | ~3k |
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.NET MAUI skills for GitHub Copilot and Claude Code
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agents-progressive-disclosure is A skill to refactor bloated AGENTS.md, CLAUDE.md, or similar agent instruction files into a compact routing entrypoint plus focused docs/ reference files.. It is categorized as a AI Tool with 62 GitHub stars.
You can find installation instructions and usage details in the agents-progressive-disclosure GitHub repository at github.com/Caph-dev/agents-progressive-disclosure. The project has 62 stars and 4 forks, indicating an active community.
The top alternatives to agents-progressive-disclosure on Agent Skills Hub include redesigned-pancake, maui-skills. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.