AI Agent Hub
Back to plugins
🧰

dsh-tool-radar

Web Tools Updated 2026.09.15

Run the following command in DeepSeek Harness:

dsh plugin install he-yufeng/dsh-tool-radar

Paste the following prompt into your AI chat to install this plugin:

Run dsh plugin install he-yufeng/dsh-tool-radar in DeepSeek Harness to install this plugin; the source lives at https://github.com/he-yufeng/dsh-tool-radar .

About this plugin

The most painful moment with an open-source contribution agent is burning an entire session only to discover the target repo simply does not merge external PRs—maintainers go months without a single reply, the queue has hundreds of stale entries, and every merge is an internal one. dsh-tool-radar steps in before the agent starts working and delivers a clear, evidence-backed answer.

It exposes a single model-facing call, repo_radar, that returns a 0-to-100 score with itemised evidence: median days from open to merge on recent PRs, open-queue size and stale ratio, median time to the first maintainer comment, and the actual merge rate for outside contributors (NONE/CONTRIBUTOR/FIRST_TIMER). Every scoring rule carries a plain-language note plus machine-readable risk flags such as stale_pr_backlog or slow_maintainer_response. Scores of 70+ are labelled Contribute, 45-69 Consider, and below 45 Avoid. API usage is capped at five calls, unauthenticated by default; a token, when supplied, only raises the rate limit.

It is built for developers who run dsh-powered open-source workflows: whether you need an agent to shortlist upstream projects worth contributing to, or you want a quick health check before a batch of PRs, repo_radar turns a gut-feel decision into one grounded in live data.

Use Cases

  • Before an agent dives into an unfamiliar repo, quickly decide if it is worth joining
  • Screen a batch of upstream projects before feeding them into a contribution pipeline
  • Confirm maintainer responsiveness and external-merge ratio before sending a batch of PRs

Best For

  • Developers building dsh-powered open-source workflows
  • Teams that need data-driven contribution decisions instead of gut feel
  • Engineers who want to cap API costs and prevent agents from wasting sessions on unkind repos