By seokan-jeong
Orchestrate multi-agent development workflows with structured guardrails: autonomous coding from requirements through verification, adversarial code reviews, multi-lens debate for architecture decisions, budget controls, persistent project memory, and observability analytics for Claude Code sessions.
Analyze work tracker events for observability insights
Deep analysis with Hiroshi(Oracle)
Autonomous execution from idea to working code
Deterministic adversarial code review (Workflow tier) for high-stakes scope
Delete specific memories
All execution agents follow these four principles before writing a single line of code.
> **Note**: This file is a **reference template** shipped with the plugin.
- **Category**: architecture
- **Category**: performance
- **Category**: security
Use when you need to analyze work tracker data for agent metrics or session stats.
Use when you need deep analysis of code, bugs, performance, or architecture issues.
Use when you want autonomous completion from requirements to verification without intervention.
Use when you need backend development for APIs, databases, servers, or endpoints.
Use when you have a large-scale, multi-phase project requiring orchestrated execution.
Matches all tools
Hooks run on every tool call, not just specific ones
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Sign in to claimBased on adoption, maintenance, documentation, and repository signals. Not a security audit or endorsement.
Executes bash commands
Hook triggers when Bash tool is used
Executes bash commands
Hook triggers when Bash tool is used
Modifies files
Hook triggers on file write and edit operations
Modifies files
Hook triggers on file write and edit operations
Uses power tools
Uses Bash, Write, or Edit tools
Uses power tools
Uses Bash, Write, or Edit tools
Has parse errors
Some configuration could not be fully parsed
Has parse errors
Some configuration could not be fully parsed
Guardrails, Observability, Ontology, and Quality Gates for AI-Powered Development
15 specialist agents with structured workflows, project ontology, budget controls, analytics, and self-learning.
AI agents are powerful but unpredictable. Without constraints, they hallucinate architecture, skip reviews, blow through token budgets, and forget past decisions. An Agent Harness solves this by wrapping agents in guardrails, quality gates, and feedback loops -- the same way a test harness wraps code in assertions.
Team-Shinchan turns Claude Code into a harnessed multi-agent system where 15 specialists debate, plan, execute, and learn -- all within well-defined boundaries.
| Without a Harness | With Team-Shinchan |
|---|---|
| Agent starts blind, re-reads the whole codebase | Project Ontology auto-builds a knowledge graph on first session |
| Agents skip stages, jump to code | Workflow Guard enforces stage-tool matrix |
| No visibility into agent behavior | Analytics with trace IDs track every action |
| Unlimited token burn | Budget Guard caps spend per session |
| No quality signal on the harness itself | Harness Lint checks plugin integrity |
| Past decisions forgotten | Memory + Ontology + session bridging persist context |
| Code reviewed ad-hoc (or not at all) | Action Kamen reviews every phase (mandatory) |
Team-Shinchan is built on 5 Harness Engineering principles:
Load the right knowledge at the right time.
session-wrap and resume for cross-session continuityHard boundaries that prevent structural drift.
Automated checks that prevent bad outcomes.
Observability and continuous improvement.
src/analytics.js)src/harness-lint.js)src/gen-architecture-map.js generates agent hierarchy, workflow, invariant rules, and entry points; --check flag integrates into CIsrc/eval-schema.js, src/regression-detect.js)Durable state across sessions and workflows.
npx claudepluginhub seokan-jeong/team-shinchan --plugin team-shinchanHarness-native ECC plugin for engineering teams - 67 agents, 271 skills, 92 legacy command shims, reusable hooks, rules, MCP conventions, and operator workflows for Claude Code plus adjacent agent harnesses
Comprehensive skill pack with 66 specialized skills for full-stack developers: 12 language experts (Python, TypeScript, Go, Rust, C++, Swift, Kotlin, C#, PHP, Java, SQL, JavaScript), 10 backend frameworks, 6 frontend/mobile, plus infrastructure, DevOps, security, and testing. Features progressive disclosure architecture for 50% faster loading.
Access thousands of AI prompts and skills directly in your AI coding assistant. Search prompts, discover skills, save your own, and improve prompts with AI.
Develop, test, build, and deploy Godot 4.x games with Claude Code. Includes GdUnit4 testing, web/desktop exports, CI/CD pipelines, and deployment to Vercel/GitHub Pages/itch.io.
Upstash Context7 MCP server for up-to-date documentation lookup. Pull version-specific documentation and code examples directly from source repositories into your LLM context.
Tools to maintain and improve CLAUDE.md files - audit quality, capture session learnings, and keep project memory current.