bkit - Vibecoding Kit
PDCA methodology + CTO-Led Agent Teams + AI coding assistant mastery for AI-native development
bkit is a Claude Code plugin that transforms how you build software with AI. It provides structured development workflows, automatic documentation, and intelligent code assistance through the PDCA (Plan-Do-Check-Act) methodology.
What is Context Engineering?
Context Engineering is the systematic curation of context tokens for optimal LLM inference—going beyond simple prompt crafting to build entire systems that consistently guide AI behavior.
Traditional Prompt Engineering:
"The art of writing good prompts"
Context Engineering:
"The art of designing systems that integrate prompts, tools, and state
to provide LLMs with optimal context for inference"
bkit is a practical implementation of Context Engineering, providing a systematic context management system for Claude Code.
bkit's Context Engineering Architecture
bkit implements Context Engineering through three interconnected layers:
| Layer | Components | Purpose |
|---|---|---|
| Domain Knowledge | 37 Skills | Structured expert knowledge (phases, levels, specialized domains) |
| Behavioral Rules | 32 Agents | Role-based constraints with model selection (opus/sonnet/haiku) |
| State Management | ~620+ Functions | PDCA state machine, workflow engine, automation control, audit, quality gates, intent detection, team coordination |
6-Layer Hook System
Context injection occurs at six distinct layers:
Layer 1: hooks.json (Global) → SessionStart, UserPromptSubmit, PreCompact, PostCompact, PreToolUse, PostToolUse, Stop, StopFailure + 10 more (18 events)
Layer 2: Skill Frontmatter → Domain-specific hooks (deprecated in v1.4.4, use hooks.json)
Layer 3: Agent Frontmatter → Task-specific hooks with constraints
Layer 4: Description Triggers → Semantic matching in 8 languages
Layer 5: Scripts (21 modules) → Actual Node.js execution logic with unified handlers
Layer 6: Plugin Data Backup → ${CLAUDE_PLUGIN_DATA} persistent state management
Learn more: See Context Engineering Principles for detailed implementation.
Features
- CC v2.1.86 Compatibility + Skills Discoverability (v2.0.8) - 34 skills description optimized for 250-char cap (/skills listing), Hook
ifconditional field documentation (CC v2.1.85+), Enterprise org policy documentation, CC v2.1.86+ recommended, 52 consecutive compatible releases (v2.1.34~v2.1.86) - Living Context System + Self-Healing + PDCA Handoff Fix (v2.0.6) - 4-Layer Living Context (
lib/context/7 modules), Self-Healing agent (opus) for automated error recovery, Deploy skill with 3-environment state machine, PDCA Handoff Loss Fix Phase 2+3 (PRD→Code context preservation 30-40% → 75-85%), 11 infrastructure templates (ArgoCD, Terraform, observability), 88 lib modules, 37 skills, 32 agents, 57 scripts - PM Skills Integration + Interactive Checkpoints (v2.0.3) - PM Agent Team expanded from 9→43 frameworks (pm-skills MIT), PDCA Interactive Checkpoints 1~5 (AskUserQuestion-gated), code-analyzer Confidence-Based Filtering (≥80%), Design phase 3 Architecture Options (Minimal/Clean/Pragmatic), btw CTO Team Integration, pm-prd template v2.0 with Execution Deliverables (Pre-mortem, User/Job Stories, Test Scenarios, Stakeholder Map, Growth Loops)
- Cross-Project Isolation Fix (v2.0.1) - PLUGIN_DATA backup/restore project identity guard via meta.json, globalCache project namespace, prevents cross-project PDCA state leakage (#48)
- AI Native Development OS (v2.0.0) - Declarative PDCA state machine (20 transitions), YAML workflow DSL (3 presets), L0-L4 controllable AI automation, CLI dashboard (progress-bar, workflow-map, control-panel), audit logging + decision tracing, quality gates (7 stages), checkpoint/rollback, destructive operation detector (8 rules), MCP servers (bkit-pdca + bkit-analysis), 3,175+ TC, ~620+ exports, 88 lib modules, 37 skills, 32 agents, 18 hook events