82c86b8z86-stack
dsh-engineering-workflow
Engineering workflow layer for DeepSeek Harness (dsh): a disciplined-engineer agent preset with five gated phases — requirements clarification, plan approval, TDD, parallel subagent execution, and verified finishing. Skills adapted from obra/superpowers.
- Stars
- 1
- Language
- JavaScript
- Created
- Aug 16, 2026
- Updated
- Aug 16, 2026
Introduction
dsh-engineering-workflow
An engineering workflow layer for DeepSeek Harness (dsh). One install adds the 工程工作流 (Engineering Workflow) agent preset — a disciplined-engineer mode with five hard-gated phases — plus six workflow skills that carry the methodology.
The workflow methodology is adapted from obra/superpowers (MIT): brainstorming, writing-plans, TDD, subagent-driven development, and verification-before-completion, reworked for dsh's native tools (plan mode + exit_plan_mode, background subagent/subagent_fork, workflow orchestration, goals, and the preset/skill system).
The five phases
| Phase | Skill | Gate | dsh mechanism |
|---|---|---|---|
| ① Requirements clarification | workflow-requirements | Intent approved before any code | ask_user_question, one question at a time |
| ② Plan approval | workflow-planning | Plan approved via exit_plan_mode | plan mode, todo_write after approval |
| ③ TDD implementation | workflow-tdd | Failing test before production code | pwsh/bash test runs |
| ④ Parallel subagent execution | workflow-subagents | Per-task review + ledger | background subagent, send_message, list_agents |
| ⑤ Verified finishing | workflow-verification | Fresh evidence before claims | full suite + branch-finish menu |
The master skill engineering-workflow routes every non-trivial task to the right phase and enforces the discipline rules (rationalization red flags included).
Install
dsh plugin --profile <name> add github:82c86b8z86-stack/dsh-engineering-workflow
(Or npm install the package into your profile and add dsh-engineering-workflow to dsh.profile.bundles.)
Restart dsh once so the host plugin mounts. On startup it syncs the preset into ~/.dsh/.agent-presets/engineering-workflow; the preset then appears in the new-session preset picker as 工程工作流. The sync is idempotent — upgrading the plugin updates the preset and its skills automatically.
Manual / development fallback without a restart:
node scripts/sync-presets.mjs
dsh re-discovers presets on every roster read, so the synced preset is selectable immediately.
How it works
dsh-engineering-workflow (bundle)
├── cordis.patch.yml inserts one host plugin row
└── lib/index.js host plugin: syncs presets/ → ~/.dsh/.agent-presets,
│ announces the workflow via a system-prompt section
└── presets/engineering-workflow/
├── agent.cordis.yml full toolset composition (adapted from the shipped
│ cordis preset, MIT): shell, filesystem, jobs, goals,
│ plan mode, compaction, delegation (subagent/subagent_fork/
│ workflow/ralph), ask-user, todo, web, skills
├── preset.yml roster metadata (name / description / order)
├── skills/ 6 workflow skills (one SKILL.md per directory)
└── NOTICE attribution
The preset wires its skills through @deepseek-ai/dsh-skill-filesystem with customSkillDirs rooted at the preset's own directory — the same pattern the shipped cordis preset uses, so the skill catalog travels with the preset wherever it is installed.
Development
pnpm install
pnpm test # preset-sync unit tests
pnpm run validate # structural validation of the bundled preset
pnpm run sync # sync the preset into ~/.dsh/.agent-presets
License
MIT. The preset composition is adapted from the DeepSeek Harness built-in cordis preset (MIT); the workflow methodology is adapted from obra/superpowers (MIT); the preset-sync host-plugin pattern follows @linxin666/dsh-liangshen (Apache-2.0). See presets/engineering-workflow/NOTICE.