MostAshraf/ai-sdlc-harness

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# ai-sdlc-harness · v3.0

**A governed multi-agent SDLC pipeline for Claude Code** — a ground-up rewrite of [ai-sdlc-harness](https://github.com/MostAshraf/ai-sdlc-harness). Drives a real engineering workflow — fetch → scope-confirmed plan → independent plan review → proven-red TDD → review → security → PR → comment rounds → reconcile → metrics — across one or many repos. No application code lives here: only the pipeline manifest, the Python core that enforces it, and the agents, skills, and hooks that run it.

| Command | Purpose |
|---|---|
| `/init-workspace` | One-time setup interview: provider, repos, discovered toolchain, verification gate |
| `/dev-workflow <work-item-id>` | Take a work item from requirements to merged PR end-to-end |
| `/story-workflow <command> <work-item-id>` | Shape a story's quality before it's built: `analyze` · `refine` · `improve` · `groom` |
| `/workflow-status` | Read-only dashboard: cursor, tasks, gates, flagged events, run-health verdict per run |
| `/workspace-config` | Change one config section without re-running the interview |
| `/add-repo` | Register one new repo into an already-bootstrapped workspace |
| `/migrate-workspace` | Adopt a v2.x workspace: config carries over, run history stays archived in place |
| `/repo-map-refresh` | Regenerate the auto-generated codebase map the planner grounds its plans in |

## The rewrite, in one table

The original harness works, but almost all of its accumulated complexity compensates for one root cause: *orchestration logic lived in markdown prose an LLM had to faithfully execute, with hooks bolted on to catch the cases where it didn't.* The rewrite moves every mechanical rule into code that just runs, and reserves the model for judgment.

| Concern | ai-sdlc-harness (v2.x) | ai-sdlc-harness (v3.0) |

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