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project-scaffold

Interactive wizard that guides you through creating a comprehensive agentic development scaffold. Use when starting a new project or adding AI capabilities to an existing codebase. Asks about project goals, tech stack, team size, and workflows, then generates custom instructions, skills, agents, and MCP configurations.

Install with Tessl CLI

npx tessl i github:0xrabbidfly/eric-cartman --skill project-scaffold
What are skills?

82

3.17x

Quality

77%

Does it follow best practices?

Impact

89%

3.17x

Average score across 3 eval scenarios

Optimize this skill with Tessl

npx tessl skill review --optimize ./.github/skills/project-scaffold/SKILL.md
SKILL.md
Review
Evals

Project Scaffold Generator

Purpose

Generate a complete, customized agentic development environment tailored to your specific project needs through guided questions.


Size Limits (Enforced)

All generated artifacts MUST respect these limits:

ArtifactMinMaxAction if Exceeded
Root instructions50300Split to .github/instructions/
Skills (SKILL.md)100500Extract to supporting files
Agents100400Split into sub-agents
Instructions50200Split by concern

Progressive Disclosure Pattern

Structure ALL generated content using Anthropic's 3-tier pattern:

Tier 1: METADATA (~100 tokens)
├── name + description in frontmatter
├── Loaded at startup for ALL installed skills
└── Must clearly indicate WHEN to trigger

Tier 2: INSTRUCTIONS (1-5K tokens)  
├── Full SKILL.md body
├── Loaded only when skill is triggered
└── Core workflow — NOT reference material

Tier 3: RESOURCES (on-demand)
├── Bundled files in skill directory
├── Loaded only when explicitly referenced
└── Templates, checklists, examples, patterns

Generation Rules:

  • Never embed examples >20 lines in SKILL.md — use See: examples.md
  • Never embed templates in SKILL.md — use See: templates/[name].template.[ext]
  • Never embed reference material — use See: reference.md
  • Always create templates/ subdirectory for reusable code

When to Use

  • Starting a new development project
  • Adding AI coding assistant capabilities to existing project
  • Standardizing team's AI tooling approach
  • Migrating from basic to advanced agentic features

Workflow

Phase 1: Project Discovery

Ask the user these questions (use multiple-choice when possible):

  1. What type of project is this?

    • New greenfield project
    • Existing codebase enhancement
    • Migration or refactor
    • Prototype/POC
    • Other (specify)
  2. What is your primary technology stack?

    • Frontend: [React/Vue/Angular/Svelte/Other]
    • Backend: [Node.js/Python/Java/Go/Ruby/.NET/Other]
    • Database: [PostgreSQL/MySQL/MongoDB/Redis/Other]
    • Infrastructure: [AWS/Azure/GCP/On-prem/Hybrid]
  3. What languages will be used? (multi-select)

    • JavaScript/TypeScript
    • Python
    • Java/Kotlin
    • C#/.NET
    • Go
    • Rust
    • Other (specify)
  4. Team size and structure?

    • Solo developer
    • Small team (2-5 people)
    • Medium team (6-15 people)
    • Large team (16+ people)
    • Open source project
  5. What are your primary development workflows? (multi-select)

    • Unit/integration testing
    • End-to-end testing
    • Code review process
    • CI/CD pipelines
    • Local development
    • Pair programming
    • Documentation generation
    • Performance monitoring
    • Security scanning
  6. Desired AI integration level?

    • Basic (code completion, simple suggestions)
    • Intermediate (workflow automation, guided tasks)
    • Advanced (autonomous agents, complex orchestration)
  7. What external tools need integration? (multi-select)

    • Git/GitHub/GitLab
    • Jira/Linear/Asana
    • Slack/Discord
    • Cloud provider CLI
    • Database tools
    • API testing tools
    • Monitoring/logging services
    • Other (specify)

Phase 2: Generate Core Configuration

Based on answers, create:

  1. .github/copilot-instructions.md

    • Project overview from answers
    • Technology stack guidelines
    • Team conventions (based on team size)
  2. Language-Specific Instructions For each selected language, create .github/instructions/[language].instructions.md:

    • Language-specific best practices
    • Framework patterns
    • Common gotchas
  3. Framework Instructions (if applicable) For major frameworks (React, Django, etc.):

    • Framework-specific patterns
    • Project structure conventions
    • Common patterns for this stack

Phase 3: Generate Recommended Skills

Based on workflows selected, generate skills in .github/skills/.

Required Skill Directory Structure:

.github/skills/[skill-name]/
├── SKILL.md              # Core workflow (MAX 500 lines)
├── checklist.md          # Validation checklist (optional)
├── patterns.md           # Code patterns if >50 lines
├── examples.md           # Extended examples if >20 lines
└── templates/            # Reusable templates
    └── [name].template.[ext]

Testing Workflow → Create testing/SKILL.md:

---
name: testing
description: Comprehensive testing workflow including unit, integration, and e2e tests. Use when adding tests or debugging test failures.
---

# Testing Skill {#testing-skill }

## Purpose {#purpose }
Streamline test creation and execution for [PROJECT_STACK]

## When to Use {#when-to-use }
- Writing new tests for features
- Debugging failing tests
- Running test suites
- Updating test coverage

## Instructions {#instructions }

1. **Identify test type needed**:
   - Unit: Single function/component isolation
   - Integration: Multiple components working together
   - E2E: Full user workflow

2. **Generate test scaffold**:
   [Language-specific test template]

3. **Run tests**:
   - Command: [project-specific command]
   - Watch mode: [if applicable]

4. **Validate coverage**:
   - Check coverage report
   - Identify gaps
   - Add missing tests

## Examples {#examples }
[Stack-specific examples]
Repository
0xrabbidfly/eric-cartman
Last updated
Created

Is this your skill?

If you maintain this skill, you can claim it as your own. Once claimed, you can manage eval scenarios, bundle related skills, attach documentation or rules, and ensure cross-agent compatibility.