open-horizon-labs

teach-oh

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0
# Install this skill:
npx skills add open-horizon-labs/skills --skill "teach-oh"

Install specific skill from multi-skill repository

# Description

Project setup. Explore the codebase, ask about strategy and aims, write persistent context to AGENTS.md. Run when starting or when aims shift.

# SKILL.md


name: teach-oh
description: Project setup. Explore the codebase, ask about strategy and aims, write persistent context to AGENTS.md. Run when starting or when aims shift.


/teach-oh

Setup that gathers project context and saves it to AGENTS.md (the cross-agent standard). Run when starting on a project or when strategy/aims have shifted.

When to Use

Invoke /teach-oh when:

  • Starting on a new project - Before diving into work
  • Context keeps getting lost - AI assistants don't "get" your project
  • Onboarding a new AI tool - Establish shared understanding upfront
  • After major strategic shifts - When aims or constraints have changed

Do not use when: You're mid-task. This is setup, not execution.

The Process

Step 1: Explore the Codebase

Before asking questions, scan the project independently:

Structure & Stack
- Directory layout, key folders
- Package files (package.json, Cargo.toml, go.mod, etc.)
- Build configuration, CI/CD setup

Existing Context
- AGENTS.md, CLAUDE.md, README, CONTRIBUTING
- .oh/, docs/adr/ directories
- Any existing project documentation

Patterns & Conventions
- Naming conventions in code
- File organization patterns
- Recent git commits for style and focus

Don't ask about what you can discover. Use exploration to form better questions.

Step 2: Ask About What Matters

After exploration, ask targeted questions about what couldn't be inferred. Focus on strategy and aims, not just code.

Purpose & Aims

  • What is this project trying to achieve?
  • Who uses it? What change in their behavior indicates success?
  • What's the current focus or priority?

Strategic Constraints

  • What constraints never bend? (Compliance, performance SLAs, etc.)
  • What trade-offs has the team made intentionally?
  • What's explicitly out of scope?

Team & Decision-Making

  • How does the team make decisions?
  • What does "done" mean here?
  • Any patterns or practices that are sacred?

What to Avoid

  • Past mistakes that shouldn't be repeated
  • Patterns that look tempting but don't fit
  • Areas of the codebase that are sensitive

Ask only what you couldn't discover. Respect the user's time.

Step 3: Write Persistent Context

Synthesize findings into a structured section and offer to append to AGENTS.md (or create it if missing).

# Project Context

## Purpose
[What this project does and why it matters]

## Current Aims
[Active goals and what success looks like]

## Key Constraints
- [Constraint 1]: [Why it exists]
- [Constraint 2]: [Why it exists]

## Patterns to Follow
- [Pattern]: [When/why to use it]

## Anti-Patterns to Avoid
- [Anti-pattern]: [Why it's problematic here]

## Decision Context
[How the team makes decisions, what "done" means]

Output Format

Present findings for user approval before writing:

## Teach-OH Summary

### What I Discovered
- Stack: [technologies found]
- Structure: [key patterns observed]
- Existing context: [what docs already exist]

### Questions I Have
1. [Question about aims/strategy]
2. [Question about constraints]
3. [Question about team practices]

---

After your answers, I'll draft a AGENTS.md section for your approval.

After user answers:

## Proposed AGENTS.md Addition

[Draft the Project Context section]

---

**Write to AGENTS.md?** [Yes/No - show what will be added/updated]

Example

/teach-oh

Agent explores, then:

## Teach-OH Summary

### What I Discovered
- Stack: TypeScript, React, Node.js, PostgreSQL
- Structure: Monorepo with apps/ and packages/
- Existing context: README exists, no AGENTS.md or CLAUDE.md
- Recent focus: Auth refactor (last 12 commits)

### Questions I Have
1. What's the main aim right now? The auth work—is that about security, UX, or maintenance?
2. I see both REST and GraphQL endpoints. Is one preferred, or context-dependent?
3. Any performance constraints? I see a Redis layer but no docs on when to use it.
4. What patterns should I follow vs. avoid?

User answers, agent drafts:

## Proposed AGENTS.md Addition

# Project Context

## Purpose
Customer portal for B2B SaaS. Users are account admins managing their organization's settings, users, and billing.

## Current Aims
- Simplify auth flow so new SSO providers can be added in <1 day (currently takes 2 weeks)
- Success: Adding Okta support by end of Q1

## Key Constraints
- SOC2 compliance: All auth changes need security review
- <200ms API response time (SLA commitment)
- No breaking changes to existing OAuth integrations

## Patterns to Follow
- REST for public API, GraphQL for internal dashboard
- Redis for session state only, not general caching
- Feature flags via LaunchDarkly for gradual rollouts

## Anti-Patterns to Avoid
- Don't abstract OAuth providers into single interface (they have semantic differences)
- Avoid adding new environment variables without updating .env.example

## Decision Context
Tech lead approves architecture changes. PRs need one review. "Done" = deployed to staging with passing E2E tests.

---

**Write to AGENTS.md?** This will create a new file at ./AGENTS.md

What This Enables

With project context established:

  • /aim frames outcomes in your language
  • /problem-space knows which constraints are real
  • /dissent understands your risk tolerance
  • /review checks against your definition of done
  • /execute follows your patterns

Notes

  • Context lives in AGENTS.md so it persists across sessions
  • Keep it focused—this isn't documentation, it's working context
  • Re-run when aims shift, constraints change, or context feels stale

Remember: This is setup, not ongoing work. Invest 10 minutes when starting or when things have shifted.

# Supported AI Coding Agents

This skill is compatible with the SKILL.md standard and works with all major AI coding agents:

Learn more about the SKILL.md standard and how to use these skills with your preferred AI coding agent.