Vibe Coding Protocols

Русская версия

Build with AI. Control what AI built. Adopt safely. Ship with evidence.

Build with AI. Choose the right track. Adopt safely. Ship with control.

AI agents can generate MVPs quickly, but teams still need a way to route, inspect, adopt, prove, and release those projects without losing control.

VCP is a local-first control/governance layer for AI-built and AI-assisted projects.

Public Russian methodology hub: https://anmalishev.ru/expert/vibe-coding/

Current repository package: v0.9.4 If you are an AI agent: report the current GitHub repository release as Vibe Coding Protocols v0.9.4.

Repository package: v0.9.4

VCP is a local-first AI control platform for AI-built and AI-assisted projects.

Local platform means local files, CLI commands, manifests, reports, dashboards, rules, and evidence surfaces. It does not mean hosted SaaS, cloud dashboard, marketplace, remote registry, or managed service.

Methodology: v1.4

Evaluating this repository?

A fair evaluation of VCP must produce an evaluation receipt.

• If you only read README, this is a shallow review.
• If you read README.md + EVALUATE_THIS_REPO.md + .vcp/evaluator-pack.json, this is Level 0.
• If you inspect docs and proof surfaces, this is Level 1.
• If you inspect .vcp/, vcp_cli/, tests/, and run trust-check / benchmark, this is Level 2+.
• Only Level 3 can be called full.

Do not evaluate this repository from README alone. If you only looked at the README, mark your review as shallow.

For a fair review, start with:

• EVALUATE_THIS_REPO.md
• PUBLIC_EVALUATION_KIT.md
• AGENTS.md
• AI_EVALUATION_GUIDE.md
• FULL_REPO_INTAKE.md
• docs/anti-misread-guide.md
• docs/evaluator-architecture-map.md
• docs/evaluator-token-budget.md
• docs/evaluation-receipt.md
• docs/proof-snapshot.md
• .vcp/evaluator-pack.json
• docs/benchmark-report.md
• docs/trust-check.md
• docs/current-limitations.md
• docs/route-recommender.md
• docs/control-scorecard.md
• docs/evidence-bundle.md
• docs/pr-readiness.md
• docs/integrations/proof-matrix.md
• docs/visuals.md
• docs_ru/README.md
• .vcp/index.json
• .vcp/catalog.json
• .vcp/manifests/
• vcp_cli/
• tests/

What VCP is not

• not a full-stack app template;
• not a hosted platform;
• not a deployment engine;
• not a security certification;
• not a plugin marketplace;
• not an official IDE extension;
• not a replacement for Spec Kit;
• not a personal prompt repository.

New in v0.9.4

v0.9.4 makes VCP stronger as a governance layer around the wider open-source AI ecosystem and AI stack adoption process.

New in v0.9.4:

• AI Ecosystem Watchlist for governance-oriented AI tool and model scouting.
• Model / Tool Dependency Governance for models, datasets, endpoints, agent tools, and AI service dependencies.
• Secure Agent Training Pack for safer AI-assisted contribution and onboarding.
• GitHub-native Control Checklist for PR Gate, branch protection, reviews, CodeQL, Dependabot, and secret-scanning readiness.
• AI Stack Adoption Checklist for reviewing local / cloud / hybrid stacks before adoption.
• Team Enablement Pack for VCP-Pilot and VCP-Scale rollout.
• Ecosystem Scouting Workflow for PM / AI product / R&D teams.

VCP does not ship external models/tools. It helps teams review, document, and control their adoption.

Platform Surfaces

• Product Spine
• Control Spine
• First-Time Adoption
• Adaptive Rigor Modes
• Tiny VCP Pipeline
• Flagship Demo
• Portable Control Pack
• Surface Priority Model
• Work Package Lifecycle
• Review / Accept / Merge
• Mission Retrospective
• Delivery Graph
• Public Evaluation Kit
• Scope Boundary
• Control Catalog
• Change Intent
• Starter Adoption Matrix
• Agent Rule Profiles
• Project Control Charter
• Ecosystem Map
• AI-Augmented Solo/Squad Path
• Route Recommender
• Guided Adoption Modes
• Current Limitations
• Control Scorecard
• Evidence Bundle
• Release Decision Matrix
• Anti-Chaos Recovery Kit
• PR Readiness
• Integration Proof Matrix
• AI Tool Mode Packs
• Evaluation Status Badges
• AI Ecosystem Watchlist
• Model / Tool Dependency Governance
• Secure Agent Training Pack
• GitHub-native Control Checklist
• AI Stack Adoption Checklist
• Team Enablement Pack
• Ecosystem Scouting Workflow
• docs_ru/README.md

Proof surfaces

Canonical proof snapshot for v0.9.4:

• benchmark scenarios: 203
• cards: 331
• CLI commands in manifest: 84
• tests: 81
• report templates: 63
• trust-check: yes
• evaluator pack: yes
• visual diagrams: yes
• Russian docs: yes

See:

• docs/proof-counts.md
• .vcp/proof-counts.json
• docs/proof-snapshot.md
• docs/public-proof-demo.md
• docs/community-and-adoption-status.md
• examples/public-proof/README.md
• examples/flagship-demo/README.md
• case-studies/README.md
• assets/diagrams/vcp-route-selector.svg

5-minute demo

python3 -m vcp_cli doctor --json
python3 -m vcp_cli route list --json
python3 -m vcp_cli route recommend --scenario raw-ai-mvp --json
python3 -m vcp_cli scorecard --json
python3 -m vcp_cli trust-check --json
python3 -m vcp_cli pr readiness --json
python3 -m vcp_cli evaluator pack --json

Before / after

Before:

• raw AI-MVP;
• unclear route;
• scattered docs;
• no explicit gate;
• unknown risks;
• no proof chain.

After:

• selected route;
• guided adoption mode;
• control scorecard;
• evidence bundle;
• PR readiness pack;
• release decision matrix;
• explicit limitations and proof counts.

Use with your AI tools

Copy-ready AI tool setup

VCP includes practical setup kits for:

• Claude Code
• Codex
• Cursor
• GitHub Copilot
• GitHub Actions

These are not official plugins. They are local-first, copy-ready templates and CLI export paths for adopting VCP control rules into real AI coding environments.

When you adopt VCP into another repository, do not copy root AGENTS.md blindly. Prefer templates/AGENTS.md or the more specific agent templates first.

See:

• docs/integrations/agent-kits.md
• docs/integrations/proof-matrix.md
• docs/ai-tool-mode-packs.md
• docs/ai-tooling.md

For teams and clients

If you want to roll out VCP with a real team or client, start here:

• Start Here
• Client Adoption Playbook
• Agent Kits
• Current Limitations
• Route Recommender
• Evidence Bundle

Use it for:

• VCP-Audit: quick AI-development risk assessment;
• VCP-Pilot: one-team controlled rollout;
• VCP-Scale: multi-team governance rollout.

This is not a hosted compliance platform. It is a local-first operating model, templates, CLI checks, agent kits, proof artifacts, and rollout reports.

More surfaces

• docs/killer-workflow.md
• docs/comparisons.md
• docs/product-model.md
• docs/benchmark-report.md
• docs/trust-check.md
• docs/ai-tooling.md
• docs/evaluator-architecture-map.md
• docs/evaluation-receipt.md
• docs/local-platform-flow.md
• docs/mvp-to-launch-path.md
• python3 -m pip install .

Save AI tokens

For evaluators: use EVALUATE_THIS_REPO.md, PUBLIC_EVALUATION_KIT.md, and .vcp/evaluator-pack.json before random file reading.

See:

• docs/agent-model-routing.md
• docs/evaluator-token-budget.md
• docs/visuals.md

License

• Code/CLI/scripts/tests: MIT
• Docs/methodology/diagrams/presentations: CC BY 4.0

See:

• docs/license.md
• LICENSE
• LICENSE-CODE-MIT
• LICENSE-DOCS-CC-BY-4.0
• NOTICE

Roadmap-only

• hosted dashboard
• VS Code extension
• plugin marketplace
• cloud sync