harness-flow 4.1.103

Cursor-native multi-agent development framework with 5-role review

中文

harness-flow

Agent writes code. Harness Flow ships products.

L5 autonomous delivery for the vibe coding era — you're the copilot now.

Python PyPI License: MIT

The Problem

AI agents can write code — but they can't ship products. They lack navigation (goal management), traffic rules (quality gates), and a dashcam (audit trail). The bottleneck has shifted from "can AI write code?" to "can AI autonomously deliver?"

Where Harness Flow Fits

The Three Pillars of L5

	Navigation	Traffic Rules	Dashcam
	vision → plan → roadmap	adaptive multi-role review + gates + trust	audit trail + learnings + retro
	AI knows where to go	AI obeys the rules	every decision recorded

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How It Works

flowchart LR
  Req["Requirement"] --> Plan["Plan"]
  Plan --> PlanReview["Adaptive\nplan review"]
  PlanReview --> Build["Build + CI"]
  Build --> CodeReview["Adaptive\ncode review"]
  CodeReview --> Ship["Ship → PR"]

  PlanReview -.-|"Architect · PO · Engineer · QA · PM"| CodeReview

  style Req fill:#fff,stroke:#222,stroke-width:2px,color:#000
  style Plan fill:#fff,stroke:#222,stroke-width:2px,color:#000
  style PlanReview fill:#222,stroke:#222,stroke-width:2px,color:#fff
  style Build fill:#fff,stroke:#222,stroke-width:2px,color:#000
  style CodeReview fill:#222,stroke:#222,stroke-width:2px,color:#fff
  style Ship fill:#fff,stroke:#222,stroke-width:2px,color:#000

One requirement in → one PR out. Both plan and code are reviewed by 5 parallel AI reviewers. Findings from 2+ roles on the same issue are flagged [HIGH CONFIDENCE].

Fix-First classifies every review finding:

• AUTO-FIX — high certainty + small blast radius + reversible → fixed immediately
• ASK — security, behavior change, architecture → batched for your decision

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Quick Start

0. 10-minute happy path

Step 1 — Install:

pip install harness-flow

Step 2 — Initialize in your project:

cd <YOUR_PROJECT_PATH>
harness init

Step 3 — Open Cursor, type a requirement:

/harness-plan add input validation to the user registration endpoint

That's it — plan, build, adaptive multi-role review, and PR. One command.

What you'll see: the agent generates a spec + contract, 5 reviewers challenge the plan in parallel, then the agent implements, runs CI, gets code reviewed by the same 5 roles, and opens a PR — all autonomously.

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Deep Dive

Your AI Engineering Team — 5 parallel reviewers

Harness gives you a complete engineering team inside Cursor — each role reviews both your plan and your code:

Role	Plan Review	Code Review
Architect	Feasibility, module impact, dependencies	Conformance, layering, coupling, security
Product Owner	Vision alignment, user value, acceptance criteria	Requirement coverage, behavioral correctness
Engineer	Implementation feasibility, code reuse, tech debt	Code quality, DRY, patterns, performance
QA	Test strategy, boundary values, regression risk	Test coverage, edge cases, CI health
Project Manager	Task decomposition, parallelism, scope	Scope drift, plan completion, delivery risk

Not a simulation — these roles run as parallel AI subagents with distinct system prompts, each scoring independently. Findings from 2+ roles are flagged as high confidence.

Each role can use a different model via [native.role_models] in config. If some reviewers fail, the pipeline continues with available perspectives (graceful degradation).

Contract-Driven Development

Every task starts with a spec + contract — deliverables, acceptance criteria, and risk analysis — reviewed by 5 roles before any code is written.

The contract lives in .harness-flow/tasks/task-NNN/plan.md and serves as the single source of truth. Runtime state is tracked in workflow-state.json alongside it.

Fix-First Auto-Remediation

Every review finding is classified before presenting it to you:

• AUTO-FIX (high certainty + small blast radius + reversible) → fixed immediately, tests re-run
• ASK (security, behavior change, architecture, low confidence) → batched and presented for your decision

Typical auto-fixes: unused imports, stale comments, missing null checks, naming inconsistencies, obvious N+1 queries.

Full Audit Trail

Plans, reviews, build logs, gate results — all persisted per task. Every decision is traceable.

.harness-flow/
├── config.toml              # project settings (CI command, trunk branch, language)
├── vision.md                # product direction (optional)
└── tasks/task-NNN/
    ├── plan.md              # spec + contract (scope SSOT)
    ├── handoff-*.json       # structured context per phase (plan, build, eval, ship)
    ├── build-rN.md          # build log per round
    ├── plan-eval-rN.md      # plan review per round
    ├── code-eval-rN.md      # code review per round
    ├── ship-metrics.json    # delivery metrics (scores, test count, coverage)
    ├── workflow-state.json  # canonical task phase / gate / blocker tracking
    └── ...                  # feedback ledger, intervention audit, etc. (optional)

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Installation & Upgrade

Command	What it does
pip install harness-flow	Install the CLI
harness init	Interactive wizard → generates skills, agents, rules into .cursor/
harness init --force	Regenerate all artifacts (after config changes or version upgrade)
harness update	Self-update the package + run config migration
harness update --check	Check for new version without installing

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All Skills — default: /harness-plan

/harness-plan is the default for most tasks — single-round plan → ship path.

/harness-vision covers everything from vague ideas to clear directions — it auto-detects whether to explore or clarify.

Entry points

Skill	When to use	What it does
/harness-plan	"I have a requirement"	Refine plan + adaptive review → auto build/eval/ship/retro
/harness-vision	"I have an idea" or "a direction"	Explore or clarify → structured vision → roadmap/backlog → iterative build/eval/ship loop

Utility & pipeline skills

Skill	What it does
/harness-investigate	Systematic bug investigation: reproduce → hypothesize → verify → minimal fix
/harness-learn	Memverse knowledge management: store, retrieve, update project learnings
/harness-retro	Engineering retrospective: commit analytics, hotspot detection, trend tracking
/harness-build	Implement the contract, run CI, triage failures, write a structured build log
/harness-eval	Adaptive multi-role code review (FAST/LITE/FULL based on escalation score)
/harness-ship	Full pipeline: test → review → fix → commit → push → PR
/harness-doc-release	Documentation sync: detect stale docs after code changes

Progress & next-step hints

• **harness workflow next** — one machine-readable line for agents/scripts (task id, phase, suggested skill).
• **harness status** — Rich panel for humans ("what to do next" in task language).
• **HARNESS_PROGRESS** — one-line boundary marker emitted by Cursor skills.

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Configuration

Project settings live in .harness-flow/config.toml:

Key	Default	Description
workflow.max_iterations	3	Max review iterations per task
workflow.pass_threshold	7.0	Evaluator pass threshold (1-10)
workflow.auto_merge	true	Auto-merge branch after pass
native.evaluator_model	"inherit"	Default model for review roles; falls back to IDE default
native.review_gate	"eng"	Review gate strictness (eng = hard gate, advisory = log only)
native.plan_review_gate	"auto"	Plan review gate (human / ai / auto)
native.role_models.*	{}	Per-role model overrides; falls back to IDE default
workflow.branch_prefix	"agent"	Task branch prefix

CLI reference

Command	Description
harness init [--name] [--ci] [-y] [--force]	Initialize project (interactive wizard)
harness status	Show current task progress
harness gate [--task]	Check ship-readiness gates
harness update [--check] [--force]	Self-update + config migration
harness git-preflight [--json]	Preflight checks (clean tree, branch)
harness save-eval --task <id> [--kind] [--verdict] ...	Save evaluation results
harness save-build-log --task <id> [--body]	Save build log
harness git-prepare-branch --task-key <key>	Create or resume task branch
harness git-sync-trunk [--json]	Sync feature branch with trunk

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Development

harness init generates 9 skills, 5 subagents, 4 rules into .cursor/. All task state lives under .harness-flow/ (local-first). See MIT License.

pip install -e ".[dev]"
pytest
ruff check src/ tests/