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lmiranda 7c8a20c804 refactor: extract skills from commands across 8 plugins

Refactored commands to extract reusable skills following the
Commands → Skills separation pattern. Each command is now <50 lines
and references skill files for detailed knowledge.

Plugins refactored:
- claude-config-maintainer: 5 commands → 7 skills
- code-sentinel: 3 commands → 2 skills
- contract-validator: 5 commands → 6 skills
- data-platform: 10 commands → 6 skills
- doc-guardian: 5 commands → 6 skills (replaced nested dir)
- git-flow: 8 commands → 7 skills

Skills contain: workflows, validation rules, conventions,
reference data, tool documentation

Commands now contain: YAML frontmatter, agent assignment,
skills list, brief workflow steps, parameters

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>

2026-01-30 17:32:24 -05:00

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Setup Workflow

Important Context

This workflow uses Bash, Read, Write, AskUserQuestion tools - NOT MCP tools
MCP tools won't work until after setup + session restart
PostgreSQL and dbt are optional - pandas tools work without them

Phase 1: Environment Validation

Check Python Version

python3 --version

Requires Python 3.10+. If below, stop and inform user.

Phase 2: MCP Server Setup

Locate MCP Server

Check both paths:

# Installed marketplace
ls -la ~/.claude/plugins/marketplaces/leo-claude-mktplace/mcp-servers/data-platform/

# Source
ls -la ~/claude-plugins-work/mcp-servers/data-platform/

Check/Create Virtual Environment

# Check
ls -la /path/to/mcp-servers/data-platform/.venv/bin/python

# Create if missing
cd /path/to/mcp-servers/data-platform
python3 -m venv .venv
source .venv/bin/activate
pip install --upgrade pip
pip install -r requirements.txt
deactivate

Phase 3: PostgreSQL Configuration (Optional)

Config Location

~/.config/claude/postgres.env

Config Format

# PostgreSQL Configuration
POSTGRES_URL=postgresql://user:pass@host:5432/db

Set permissions: chmod 600 ~/.config/claude/postgres.env

Test Connection

source ~/.config/claude/postgres.env && python3 -c "
import asyncio, asyncpg
async def test():
    conn = await asyncpg.connect('$POSTGRES_URL', timeout=5)
    ver = await conn.fetchval('SELECT version()')
    await conn.close()
    print(f'SUCCESS: {ver.split(\",\")[0]}')
asyncio.run(test())
"

Phase 4: dbt Configuration (Optional)

dbt is project-level (auto-detected via dbt_project.yml).

For subdirectory projects, set in .env:

DBT_PROJECT_DIR=./transform
DBT_PROFILES_DIR=~/.dbt

Check dbt Installation

dbt --version

Phase 5: Validation

Verify MCP Server

cd /path/to/mcp-servers/data-platform
.venv/bin/python -c "from mcp_server.server import DataPlatformMCPServer; print('OK')"

Memory Limits

Default: 100,000 rows per DataFrame

Override in project .env:

DATA_PLATFORM_MAX_ROWS=500000

For larger datasets:

Use chunked processing (chunk_size parameter)
Filter data before loading
Store to Parquet for efficient re-loading

Session Restart

After setup, restart Claude Code session for MCP tools to become available.

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