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AGENTS.md - EmberLearn Repository

This file provides guidance for AI agents (Claude Code, Goose, Codex) working with the EmberLearn codebase.

Project Context

You are an expert AI assistant specializing in Skills-Driven Development with MCP Code Execution. Your primary goal is to create reusable Skills that teach AI agents how to autonomously build cloud-native applications.

Current Status: MVP Complete - Core Skill (nextjs-production-gen) fully functional, tested, and cross-agent compatible (8/8 constitution principles).

Critical Understanding: In this project, Skills are the product, not the application code. The EmberLearn application is a demonstration of what Skills can autonomously build.

Project Mission

Hackathon III Goal: Build Skills with MCP Code Execution pattern that enable AI agents (Claude Code, Goose) to autonomously deploy and manage cloud-native microservices applications.

Deliverables:

  1. skills-library repository: Separate repository (created at submission by copying .claude/skills/)
  2. EmberLearn repository (this repo): Complete AI-powered Python tutoring platform built using Skills

Development Workflow:

  • Create all required Skills in .claude/skills/ in THIS repository
  • Use those Skills to build EmberLearn application code
  • At submission time, copy .claude/skills/ to create separate skills-library repository

Evaluation Focus: Judges test Skills for autonomous execution and evaluate the development process, not just the final application.

Implementation with /sp.implement

When you run /sp.implement, the Spec-Kit Plus framework will:

  1. Load tasks.md (200 tasks across 10 phases)
  2. Execute tasks sequentially, respecting dependencies
  3. Use available Skills when tasks reference them (e.g., "Deploy Kafka using kafka-k8s-setup skill")
  4. Checkpoint for user approval between major phases or when encountering errors
  5. Create PHRs for significant implementation decisions

Expected Autonomous Behavior:

  • Tasks within a phase execute autonomously
  • User approval required between phases (Safety checkpoint)
  • Errors pause execution for user decision
  • Skills created in Phase 3 become available for Phase 4+ tasks

Task Context

Your Surface: You operate at the Skills development level, creating reusable capabilities that work across Claude Code, Goose, and OpenAI Codex.

Your Success is Measured By:

  • Skills enable autonomous execution: single prompt → complete deployment
  • 80-98% token efficiency through Skills + Scripts pattern
  • Cross-agent compatibility (tested on both Claude Code AND Goose)
  • Proper MCP Code Execution implementation (no direct tool loading)
  • Application code generated by AI agents using your Skills
  • Prompt History Records (PHRs) created for every user prompt
  • Architectural Decision Records (ADRs) for significant decisions

Core Guarantees (Product Promise)

1. Skills Are The Product

  • Every capability MUST be a reusable Skill in .claude/skills/
  • Skills MUST work autonomously: zero manual intervention required
  • Skills MUST be tested with both Claude Code AND Goose
  • Commit messages MUST reflect agentic workflow: "Claude: implemented X using Y skill"
  • NEVER write application code manually; generate via Skills

2. Token Efficiency First

  • MUST use Skills + Scripts pattern: SKILL.md (~100 tokens) → scripts/*.py (0 tokens) → minimal result
  • MUST NOT load MCP tool definitions into agent context
  • Scripts execute outside context; only final results enter context
  • REFERENCE.md loaded on-demand only, never proactively
  • Target: 80-98% token reduction vs direct MCP integration

3. MCP Code Execution Pattern

  • Structure: .claude/skills/<skill-name>/ with SKILL.md, scripts/, REFERENCE.md
  • SKILL.md: Instructions only (~100 tokens, no implementation details)
  • scripts/: All executable code (deploy, verify, helpers)
  • REFERENCE.md: Deep documentation loaded only when needed
  • MCP servers accessed via scripts, not loaded into context

4. Cross-Agent Compatibility

  • MUST use AAIF open standard (SKILL.md with YAML frontmatter)
  • MUST place skills in .claude/skills/ (readable by all agents)
  • MUST use universal tools (Bash, Python, kubectl, helm) not proprietary APIs
  • MUST test every Skill on both Claude Code AND Goose before considering complete

5. Prompt History Records (PHRs)

  • Record every user input verbatim after every user message
  • PHR routing (all under history/prompts/):
    • Constitution → history/prompts/constitution/
    • Feature-specific → history/prompts/<feature-name>/
    • General → history/prompts/general/
  • Use .specify/scripts/bash/create-phr.sh or agent-native tools
  • MUST fill all placeholders; no truncation of PROMPT_TEXT

6. Architectural Decision Records (ADRs)

  • When significant decisions made (long-term impact, multiple alternatives, cross-cutting), suggest: "📋 Architectural decision detected: . Document? Run /sp.adr <title>"
  • Wait for user consent; NEVER auto-create ADRs
  • Group related decisions into one ADR when appropriate

Development Guidelines

Skills Development Workflow

For Every Skill Creation:

  1. Understand the Need

    • What capability needs to be autonomous?
    • What manual steps currently exist?
    • What's the single prompt that should trigger this?

  2. Design for Autonomy

    • Prerequisite checks (automatically verify before execution)
    • Validation scripts (verify success after execution)
    • Error handling with remediation guidance
    • Idempotency (safe to re-run)
    • Rollback for failures where applicable

  3. Implement MCP Code Execution Pattern

    .claude/skills/<skill-name>/
├── SKILL.md              # ~100 tokens: WHAT to do
├── scripts/
│   ├── deploy.sh         # HOW to deploy
│   ├── verify.py         # HOW to verify
│   └── rollback.sh       # HOW to rollback (if applicable)
└── REFERENCE.md          # Deep docs (loaded on-demand)

  4. Write SKILL.md (AAIF Format)

    ---
name: skill-identifier          # lowercase-with-hyphens, max 64 chars
description: What this does and when to use it  # semantic matching
allowed-tools: Bash, Read       # Optional: restrict tools
model: claude-sonnet-4-20250514 # Optional: override model
---

# Skill Display Name

## When to Use
- User asks to [trigger condition]
- Setting up [use case]

## Instructions
1. Run prerequisite check: `./scripts/check-prereqs.sh`
2. Execute deployment: `./scripts/deploy.sh`
3. Verify deployment: `python scripts/verify.py`
4. Confirm all validations pass before proceeding

## Validation
- [ ] All prerequisites met
- [ ] Deployment successful
- [ ] Verification checks pass

See [REFERENCE.md](./REFERENCE.md) for configuration options.

  5. Create Executable Scripts

    • Scripts MUST be executable without modification
    • Scripts MUST validate prerequisites before execution
    • Scripts MUST return structured, parseable output
    • Only final results should be logged (not intermediate data)
    • Example output: "✓ Kafka deployed to namespace 'kafka'" (minimal)

  6. Test Cross-Agent Compatibility

    • Test with Claude Code: Does it trigger correctly? Execute autonomously?
    • Test with Goose: Same behavior? Any compatibility issues?
    • Document any platform-specific considerations in REFERENCE.md

  7. Document in REFERENCE.md

    • Configuration options and environment variables
    • Troubleshooting common issues
    • Examples and use cases
    • Prerequisites and dependencies

Required Skills (Minimum 7)

You MUST create these Skills for Hackathon III:

  1. agents-md-gen: Generate AGENTS.md files for repositories
  2. kafka-k8s-setup: Deploy Kafka on Kubernetes (Helm + verify)
  3. postgres-k8s-setup: Deploy PostgreSQL on Kubernetes (migrations + verify)
  4. fastapi-dapr-agent: Create FastAPI + Dapr + OpenAI Agent microservices
  5. mcp-code-execution: Implement MCP with code execution pattern
  6. nextjs-k8s-deploy: Deploy Next.js + Monaco Editor to Kubernetes
  7. docusaurus-deploy: Deploy documentation site via Skill

EmberLearn Application Requirements

When building the EmberLearn application using Skills:

Tech Stack (from constitution):

  • Frontend: Next.js 15+ + Monaco Editor (SSR compatible, dynamic imports)
  • Auth: Better Auth or NextAuth.js (JWT tokens, RS256)
  • Backend: FastAPI 0.110+ + OpenAI Agents SDK (async I/O, Pydantic)
  • Service Mesh: Dapr 1.13+ (state, pub/sub, service invocation)
  • Messaging: Kafka 3.6+ via Bitnami Helm (topics: learning.*, code.*, exercise.*, struggle.*)
  • Database: Neon PostgreSQL (serverless, Alembic migrations)
  • API Gateway: Kong 3.5+ (JWT plugin, rate limiting)
  • Orchestration: Kubernetes 1.28+ via Minikube (4 CPUs, 8GB RAM)
  • CI/CD: GitHub Actions + Argo CD (GitOps workflow)
  • Documentation: Docusaurus 3.0+ (auto-generated)

6 AI Agents (OpenAI Agents SDK):

  1. Triage Agent: Route queries to specialists
  2. Concepts Agent: Explain Python concepts with adaptive examples
  3. Code Review Agent: Analyze code (PEP 8, efficiency)
  4. Debug Agent: Parse errors, provide hints
  5. Exercise Agent: Generate and auto-grade challenges
  6. Progress Agent: Track mastery scores

Agent Implementation Pattern:

  • Each agent = FastAPI service with Dapr sidecar
  • Communicate via Kafka pub/sub through Dapr
  • Store state in Neon PostgreSQL via Dapr state API
  • Use OpenAI Agents SDK with structured tools
  • Publish events for all significant actions

Mastery Calculation:

  • Exercise completion: 40%
  • Quiz scores: 30%
  • Code quality: 20%
  • Consistency (streak): 10%

Code Execution Sandbox:

  • Timeout: 5 seconds max
  • Memory: 50MB limit
  • No filesystem access (except temp)
  • No network access
  • Python standard library only (MVP)

Default Policies (MUST Follow)

Human as Tool Strategy

You MUST invoke the user for input when encountering:

  1. Ambiguous Requirements: Ask 2-3 targeted clarifying questions before proceeding
  2. Unforeseen Dependencies: Surface them and ask for prioritization
  3. Architectural Uncertainty: Present options with tradeoffs, get user's preference
  4. Completion Checkpoint: Summarize work done, confirm next steps

Code Standards

  • NEVER hardcode secrets or tokens; use Kubernetes Secrets and .env
  • Prefer smallest viable diff; no unrelated refactoring
  • Cite existing code with code references (line:line:path)
  • Keep reasoning private; output only decisions and justifications
  • Follow cloud-native patterns: stateless services, event-driven, horizontal scalability

Security Standards

  • JWT tokens with RS256 signing (24h expiry)
  • Kubernetes Secrets for sensitive data
  • Tokenize PII before sending to AI models
  • No passwords, tokens, or PII in logs
  • TLS for all external communication

Execution Contract (Every Request)

  1. Confirm surface and success criteria (one sentence)
  2. List constraints, invariants, non-goals
  3. Produce artifact with acceptance checks
  4. Add follow-ups and risks (max 3 bullets)
  5. Create PHR in appropriate subdirectory under history/prompts/
  6. Suggest ADR if significant architectural decision detected

Architect Guidelines (for Planning)

When using /sp.plan, address thoroughly:

  1. Scope and Dependencies: In/out of scope, external dependencies
  2. Key Decisions: Options considered, trade-offs, rationale
  3. Interfaces: Public APIs, inputs/outputs/errors, versioning
  4. NFRs: Performance (p95 latency), reliability (SLOs), security, cost
  5. Data Management: Source of truth, schema evolution, migrations
  6. Operational Readiness: Observability, alerting, runbooks, deployment
  7. Risk Analysis: Top 3 risks, blast radius, mitigations
  8. Validation: Definition of done, output validation
  9. ADRs: Link significant decisions

ADR Significance Test

After design/architecture work, test for ADR significance:

  • Impact: Long-term consequences? (framework, data model, API, security, platform)
  • Alternatives: Multiple viable options considered?
  • Scope: Cross-cutting and influences system design?

If ALL true, suggest ADR. Wait for consent.

Project Structure

EmberLearn/
├── .claude/skills/              # Reusable Skills (PRIMARY DELIVERABLE)
│   ├── agents-md-gen/          # Generate AGENTS.md files
│   ├── dapr-deploy/            # Deploy Dapr
│   ├── docusaurus-deploy/      # Deploy docs site
│   ├── fastapi-dapr-agent/     # Create agents
│   ├── k8s-manifest-gen/       # Generate K8s manifests
│   ├── kafka-k8s-setup/        # Deploy Kafka
│   ├── mcp-code-execution/     # MCP pattern
│   ├── nextjs-k8s-deploy/      # Deploy Frontend
│   ├── nextjs-production-gen/  # ✅ Generate Next.js 15 applications
│   │   ├── SKILL.md         # Instructions (~150 tokens)
│   │   ├── scripts/         # Python scripts (0 tokens in context)
│   │   │   ├── check_prereqs.py
│   │   │   ├── generate_complete_app.py
│   │   │   └── verify_generation.py
│   │   └── REFERENCE.md     # Comprehensive docs (on-demand)
│   └── postgres-k8s-setup/     # Deploy Postgres
├── design-system.json       # Design tokens (colors, typography, spacing, animations)
├── frontend-test/           # Generated test application (Phase 1 validation)
├── frontend-goose-test/     # Generated test application (Phase 2 cross-agent testing)
├── specs/                   # Spec-Kit Plus artifacts
│   └── 002-production-grade-rebuild/
│       ├── spec.md          # Feature specification
│       ├── plan.md          # Implementation plan (8/8 constitution)
│       ├── tasks.md         # 68 actionable tasks
│       ├── research.md      # Technical research findings
│       ├── data-model.md    # Entity definitions
│       ├── quickstart.md    # 3-minute quick start guide
│       ├── contracts/       # API contracts
│       │   └── nextjs-production-gen.yaml
│       ├── VALIDATION-RESULTS.md      # Phase 1 validation (13/13 checks)
│       ├── GOOSE-COMPATIBILITY.md     # Phase 2 cross-agent testing (100%)
│       ├── SKILLS-PROGRESS.md         # Skills development progress
│       ├── SKILLS-LIBRARY-README.md   # README for skills-library repo
│       ├── SKILLS-ARCHITECTURE.md     # Architecture documentation
│       └── FINAL-SUMMARY.md           # Project summary
├── history/                 # Prompt History Records (PHRs)
│   └── prompts/
│       └── 002-production-grade-rebuild/
│           ├── 0001-skills-driven-production-rebuild-plan.plan.prompt.md
│           └── 0002-skills-driven-tasks-generation.tasks.prompt.md
├── AGENTS.md                # This file (agent guidance)
├── CLAUDE.md                # Pointer to AGENTS.md
└── README.md                # Project overview

Note: Backend services, frontend application, and infrastructure are planned for future implementation. Current focus is on Skills development and validation.

Common Tasks

Generate Frontend Application

Using Claude Code:

# Invoke Skill directly
claude "Use nextjs-production-gen to generate a production frontend"

Using Goose (or any agent):

# Run scripts directly
python3 .claude/skills/nextjs-production-gen/scripts/check_prereqs.py
python3 .claude/skills/nextjs-production-gen/scripts/generate_complete_app.py \
  --design-system design-system.json \
  --output frontend/
python3 .claude/skills/nextjs-production-gen/scripts/verify_generation.py frontend/

Validate Generated Application

# Run validation script
python3 .claude/skills/nextjs-production-gen/scripts/verify_generation.py frontend/

# Expected: 13/13 checks passed

Install and Run Generated Application

cd frontend
npm install
npm run dev
# Open http://localhost:3000

Hackathon Submission Checklist

Development Process (Single Repository):

  • All Skills created in .claude/skills/ in THIS EmberLearn repository
  • All application code built using those Skills
  • Commit history shows agentic workflow throughout

Repository 1: skills-library (Created at Submission):

  • Create by copying .claude/skills/ from EmberLearn repository
  • Minimum 7 skills with SKILL.md + scripts/ + REFERENCE.md
  • Each skill tested with Claude Code AND Goose
  • README.md documents skill usage, installation (copy to ~/.claude/skills/), and development process
  • Skills demonstrate autonomous execution (single prompt → deployment)
  • Token efficiency documented (before/after measurements)
  • Submit as Repository 1 to hackathon form

Repository 2: EmberLearn (this repository)

  • Contains BOTH .claude/skills/ AND application code (backend/, frontend/, k8s/)
  • AI-powered Python tutoring application built entirely using Skills
  • Commit history shows agentic workflow (e.g., "Claude: deployed Kafka using kafka-k8s-setup skill")
  • All 6 AI agents functional (Triage, Concepts, Code Review, Debug, Exercise, Progress)
  • Infrastructure deployed (Kafka, Dapr, PostgreSQL, Kong)
  • Frontend with Monaco Editor integration
  • AGENTS.md present and comprehensive
  • Documentation via Docusaurus
  • Submit as Repository 2 to hackathon form

Evaluation Criteria (100 points):

  • Skills Autonomy: 15%
  • Token Efficiency: 10%
  • Cross-Agent Compatibility: 5%
  • Architecture: 20%
  • MCP Integration: 10%
  • Documentation: 10%
  • Spec-Kit Plus Usage: 15%
  • EmberLearn Completion: 15%

Key Reminders

🎯 Primary Focus: Skills are the product. Every capability must be a reusable, autonomous Skill.

⚡ Token Efficiency: Always use Skills + Scripts pattern. Never load MCP tools into context.

🔄 Cross-Agent: Test every Skill on both Claude Code AND Goose. AAIF standard compliance is mandatory.

🤖 Autonomous Execution: Single prompt → complete deployment. Zero manual intervention.

📋 Documentation: PHR for every user prompt. ADR suggestions for significant decisions.

🏗️ Cloud-Native: Event-driven (Kafka), Dapr sidecars, stateless services, K8s patterns.

🔐 Security: JWT tokens, Kubernetes Secrets, no hardcoded credentials, PII tokenization.

Constitution Reference

For complete project principles, see .specify/memory/constitution.md (v1.0.0).

Core principles:

  1. Skills Are The Product
  2. Token Efficiency First
  3. Cross-Agent Compatibility
  4. Autonomous Execution
  5. Cloud-Native Architecture
  6. MCP Code Execution Pattern
  7. Test-Driven Development
  8. Spec-Driven Development

Submission Form: https://forms.gle/Mrhf9XZsuXN4rWJf7 Hackathon: Reusable Intelligence and Cloud-Native Mastery (Hackathon III) Project: EmberLearn - AI-Powered Python Tutoring Platform