Welcome to River Review

River Review (RR) is an OSS framework that turns your team's review judgment into versioned, repo-owned skills and runs them across SDLC gates. It operates over artifacts such as plan, diff, test-cases, JUnit, and prior review outputs, acting as the team-owned audit layer for AI-assisted development.

This page is a short introduction for first-time readers. The full concept — the problems, the core model, the responsibility boundary, and the non-goals — is collected in Concept.

The foundation stays the same: it turns your team's tacit knowledge into versioned, repo-owned Skills (the Skill Registry) that you reuse as a shared asset. River Review delivers that foundation along three core axes:

• Capability pack: a bundle of skills / agent definitions that strengthens an AI agent's review ability. It usually needs no LLM key; only the headless GitHub Action / river run path requires one.
• Review skills (Skill Registry): the foundation that shares team judgment criteria as versioned, repo-owned Skills.
• Review team: a dedicated review agent (agents/river-review.md) plus a review team that runs perspective-based reviewers in parallel. In a generate → review → revise loop it acts as the verdict-bearing critic for the review stage.

What River Review reviews

River Review is not a tool that looks only at the PR diff. It treats the requirements, design, plan, diff, and report produced during AI-assisted development as review targets, applying the team's criteria consistently from before the work starts through to after it completes.

Target	Goal	Examples
Requirement	Reduce ambiguity in purpose, success conditions, and scope	Issue, PBI, user request, acceptance criteria
Design	Check consistency with the existing design, separation of concerns, over-build	ADR, design memo, architecture direction
Plan	Check that work breakdown, risks, and verification policy exist up front	Plan, Work Packet, test policy
Diff	Check that the implementation matches requirements, design, and plan	PR diff, changed files, test diff
Report	Check that rationale, verification results, and open items remain	Final report, review results, evidence

River Review therefore serves both pre-execution review and post-execution review: requirements, design, and plan before implementation; diff, tests, and report after it.

Core Model

• Skills define judgment — A skill describes how a review decision should be made (security, accessibility, migration safety, dependency policy, plan conformance, ...). Skills are written as YAML frontmatter + Markdown and validated against schemas/skill.schema.json.
• Gates execute judgment — river review plan / exec / verify run those skills at the right point in the delivery flow — not only after the PR is already complete.
• Riverbed remembers judgment — Review outcomes and decisions persist as operating memory, with suppression and prior-decision recall keeping future reviews consistent (Riverbed Memory).

This documentation covers:

• Explanation: Design philosophy and the three-layer model in depth.
• Tutorials: Hands-on guides for creating skills.
• How-to: Practical guides for GitHub Actions integration, tracing, etc.
• Reference: Schema definitions and CLI references.

Which page to read next depends on what you need. For the overall concept see Concept; for features, usage, and the execution model see What is River Review. The breakdown of review targets is collected in Review scope and use cases. How human supervision is allocated across the cliff, hill, and field tiers is covered by Human Judgment Focus. The internal SSoT for the concept lives at docs/vision.md in the repository root.