chuspeeism/dashi-taskboard

现代化可灵活嵌入的任务面板,支持 Codex、DeepSeek Harness

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chuspeeism/dashi-taskboard has 3k+ stars and 500+ forks on GitHub, is written in JavaScript, and was last pushed on Oct 16, 2026. Data as of . Signed-in members get hourly updates — create a free account.

Summary Information

Updated 1 hour ago
Added to GitGenius on September 21st, 2026
Created on July 24th, 2026
Open Issues & Pull Requests: 37 (+0)
GitHub issues: Enabled
Number of forks: 501
Total Stargazers: 3,359 (+0)
Total Subscribers: 18 (+0)

Repository Insights (GitGenius)

Median issue/PR response: 16.3 hours
Mean response time: 44.5 hours
90th percentile: 5.9 days
Tracked items: 84

How this project is maintained

About 13% of issues opened in the past year have never received a reply. 95% of open issues come from outside the core team, so the backlog reflects real-world use rather than internal planning. 78% of issues opened in the past year have been closed, leaving a working backlog. Three people close 92% of everything that gets resolved.

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Issue Activity (beta)

Open issues: 21
New in 7 days: 3
Closed in 7 days: 2
Avg open age: 22 days
Stale 30+ days: 16
Stale 90+ days: 0

Recent activity

Opened in 7 days: 3
Closed in 7 days: 2
Comments in 7 days: 0
Events in 7 days: 0

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Detailed Description

Dashi Taskboard is a modern, embeddable task panel that integrates with Codex and DeepSeek Harness.

The tool addresses the need for a flexible task management interface that can be embedded into existing applications and development environments. It works by providing a taskboard component designed to work alongside AI coding assistants, specifically supporting Codex and DeepSeek Harness platforms. The embeddable architecture allows developers to integrate task management directly into their workflows without requiring a standalone application.

Developers should consider this tool if they are working within Codex or DeepSeek Harness ecosystems and need task tracking capabilities that integrate seamlessly with their AI-assisted development environment. The project is particularly suited for teams building plugins or extensions for these platforms, as it is designed as a plugin-compatible component. Those seeking a general-purpose taskboard without specific AI assistant integration requirements may find more conventional alternatives better suited to their needs.

The project shows active development with regular commits and ongoing refinement of its core functionality. The codebase demonstrates consistent maintenance patterns with attention to the plugin architecture that enables integration with Codex and related tools.