AI Agent Hub
Back to plugins
dsh-issue2pr preview

dsh-issue2pr

Workflow Updated 2026.09.03

Run the following command in DeepSeek Harness:

dsh plugin install LONGSASASASASA/dsh-issue2pr

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

Run dsh plugin install LONGSASASASASA/dsh-issue2pr in your DeepSeek Harness terminal to install the plugin; browse the source at https://github.com/LONGSASASASASA/dsh-issue2pr

About this plugin

Most Issue-to-PR coding agents chase a one-shot result, skipping understanding, planning, and verification. dsh-issue2pr takes the opposite approach: it decomposes the entire path into 11 non-skippable stages, each with numbered artifacts on disk, input/output contracts, and explicit failure signals. The deliverable is not just a diff but a full, auditable evidence chain from Issue to merged PR.

Core capabilities include multi-agent collaborative coding or delegation to Claude Code CLI and native DSH agents; a Patch Pipeline with a ledger that rolls back only agent-introduced changes; real test execution in a cloned repository with full output persisted for traceability; a three-dimensional review gate covering diff scope, API and security, and test reinforcement; and four human review checkpoints where you approve or reject with feedback. Every LLM call, git operation, and test run is logged in trace/events.jsonl, and a floating smart assistant lets you ask real-time questions about progress or failures.

It is built for teams and individual developers who want to bring AI-assisted coding under engineering control rather than treating it as a black box — especially those using the DSH host workflow, needing auditable PR descriptions and rollback-able patches, or preferring a staged delivery model that eases from manual gating toward full automation.

Screenshots

Use Cases

  • Drive a GitHub Issue through to a reviewed, merged PR with every step producing a numbered artifact
  • Require rollback-able patches and real test evidence instead of trusting a single opaque generation
  • Phase your workflow from manual per-stage gating toward semi-automated pipeline delivery

Best For

  • Teams using the DSH host that need engineering-grade control over AI-generated code
  • Individual developers who want to move from black-box generation to auditable, traceable, rollback-able delivery
  • Tech leads who need staged gatekeeping to reduce risk and gradually increase autonomy in AI-assisted coding