setup

name	setup
description	Initialize a new customer engagement. Creates the engagement directory structure, gathers core engagement metadata through a structured interview, and writes the initial CONTEXT.md that all subsequent skills will build upon. Run this first — it's the foundation everything else reads from.

/setup — Initialize Engagement

This skill bootstraps a new customer engagement by gathering essential metadata and creating the engagement workspace. It produces the CONTEXT.md file that every downstream skill depends on.

When This Skill Triggers

• Architect runs /setup in Claude Code or OpenCode
• Architect says "start a new engagement", "initialize engagement", or "set up for "
• The engagements/ directory has no customer subdirectories yet

Workflow

Step 1: Gather Engagement Metadata

Conduct a structured interview with the architect. Ask these questions conversationally — don't dump them all at once. Group related questions and adapt based on answers.

Required Fields (must have before proceeding):

Field	Question	Example
customer_name	"What's the customer name?"	Acme Federal
customer_slug	Auto-generate from name (kebab-case)	acme-federal
engagement_type	"What type of engagement is this?"	See engagement types below
architect_name	"Who's the lead architect?"	Todd Wardzinski
engagement_dates	"What are the start and target end dates?"	2026-04-01 → 2026-06-30
executive_sponsor	"Who's the customer executive sponsor?"	Jane Smith, CTO

Engagement Types (offer these as options):

1. Application Modernization — Assess and migrate legacy applications to cloud-native platforms
2. AI/ML Enablement — Evaluate and deploy AI/ML capabilities on Red Hat infrastructure
3. Platform Assessment — Evaluate readiness for OpenShift, RHEL, or hybrid cloud adoption
4. Security & Compliance — Assess security posture and compliance alignment
5. Infrastructure Modernization — Datacenter transformation, edge computing, hybrid cloud
6. Custom — Architect defines the engagement scope manually

Optional Fields (ask but don't block on):

Field	Question
clearance_level	"Any clearance requirements? (Unclassified, CUI, Secret, TS/SCI)"
environment_type	"Connected, limited connectivity, or air-gapped?"
team_members	"Who else is on the engagement team?"
customer_industry	"What sector? (Federal Civilian, DoD, IC, SLED, Critical Infrastructure)"
existing_rh_footprint	"Any existing Red Hat products in the environment?"
key_constraints	"Any known constraints? (budget, timeline, compliance, legacy dependencies)"
success_criteria	"What does success look like for this engagement?"

Step 2: Create Engagement Directory

Once you have the required fields, create the directory structure:

CUSTOMER_SLUG="<customer_slug>"
mkdir -p "engagements/${CUSTOMER_SLUG}"/{discovery,assessments,deliverables}

Step 3: Configure Git Hooks

Set up the repo's shared Git hooks to prevent accidental pushes of customer-sensitive data:

git config core.hooksPath .claude/skills/setup/hooks

This activates the pre-push hook that blocks all pushes to remote. The hook is bundled with this skill under .claude/skills/setup/hooks/.

Step 4: Write CONTEXT.md

Generate the initial CONTEXT.md at engagements/<slug>/CONTEXT.md using this template:

# Engagement Context: <Customer Name>

> This file is the living memory for this engagement. Every skill reads from and
> appends to this file. Do not manually edit unless correcting an error.

## Engagement Metadata
- **Customer:** <customer_name>
- **Type:** <engagement_type>
- **Lead Architect:** <architect_name>
- **Dates:** <start_date> → <end_date>
- **Executive Sponsor:** <executive_sponsor>
- **Clearance:** <clearance_level or "Unclassified">
- **Environment:** <environment_type or "Connected">
- **Industry:** <customer_industry or "Not specified">
- **Existing RH Footprint:** <existing_rh_footprint or "Unknown — to be discovered">

## Team
- <architect_name> (Lead Architect)
<additional team members if provided>

## Constraints
<key_constraints or "None identified yet">

## Success Criteria
<success_criteria or "To be defined during discovery">

## Engagement Log
### <today's date> — Engagement Initialized
- Engagement workspace created by <architect_name>
- Type: <engagement_type>
- Status: **Discovery phase**

---
<!-- Skills append their findings below this line -->

Step 5: Write .gitignore

Create or update the repo-level .gitignore to protect sensitive engagement data:

# Engagement artifacts that should never be pushed to shared remotes
engagements/*/discovery/*.sensitive
engagements/*/discovery/*.classified

# OS artifacts
.DS_Store
Thumbs.db

# Editor artifacts
*.swp
*.swo
*~

Step 6: Confirm and Recommend Next Steps

After creating the workspace, confirm what was created and recommend the logical next skill:

✅ Engagement initialized: <customer_name>
📁 Workspace: engagements/<slug>/
📄 Context: engagements/<slug>/CONTEXT.md

Recommended next steps based on engagement type:

Next-step recommendations by engagement type:

Engagement Type	Recommended Next Skill
Application Modernization	/discover-infrastructure then /assess-app-portfolio
AI/ML Enablement	/discover-infrastructure then /assess-app-portfolio
Platform Assessment	/discover-infrastructure then /assess-app-portfolio
Security & Compliance	/discover-infrastructure with security focus
Infrastructure Modernization	/discover-infrastructure
Custom	/discover-infrastructure (always a good starting point)

Error Handling

• If an engagement directory already exists for the given slug, ask: "An engagement for <slug> already exists. Do you want to resume it, or create a new one with a different name?"
• If the architect provides minimal information, create the workspace with what you have and note gaps in CONTEXT.md under a ## Known Gaps section.
• If running in an air-gapped environment, confirm that all knowledge base files are present locally.

Example Session

Architect: /setup

Agent: Let's get this engagement started. What's the customer name?

Architect: Acme Federal Services

Agent: Got it — Acme Federal Services. What type of engagement is this?
  1. Application Modernization
  2. AI/ML Enablement
  3. Platform Assessment
  4. Security & Compliance
  5. Infrastructure Modernization
  6. Custom

Architect: 2 - AI/ML Enablement

Agent: Who's the lead architect for this engagement?

Architect: Me — Todd Wardzinski

Agent: What are the start and target end dates?

Architect: Starting next week, targeting end of Q2

Agent: And who's the customer executive sponsor?

Architect: Deputy Director of IT, Mark Johnson

Agent: A few more optional details that'll help downstream —
Any clearance requirements?

Architect: TS/SCI

Agent: Environment type — connected, limited connectivity, or air-gapped?

Architect: Air-gapped

Agent: Any existing Red Hat products in the environment?

Architect: RHEL 8 across the board, some Ansible Tower

Agent: ✅ Engagement initialized: Acme Federal Services
📁 Workspace: engagements/acme-federal-services/
📄 Context: engagements/acme-federal-services/CONTEXT.md

Since this is an AI/ML Enablement engagement in an air-gapped TS/SCI environment,
I'd recommend starting with `/discover-infrastructure` to map the compute and
data landscape, with special attention to GPU resources and data classification levels.