/setup-dev-context — Developer Context Setup

Standalone skill for teams integrating AI Analyst into development workflows. Most users (PMs, execs, DS) never need this — only teams doing codebase integration.

Trigger

Invoked as

/setup-dev-context

Purpose

Collects codebase-specific context to help AI Analyst understand your development environment. This enables more accurate SQL generation, schema awareness, and integration with your existing data infrastructure.

Prerequisites

• /setup

  interview (Phases 1-2) must be completed first
• Read

  .knowledge/setup-state.yaml

  to verify

  phase_2.status: complete

• If setup incomplete, inform user: "Run

  /setup

  first to configure your profile and data connection."

Interview Flow

Step 1: Codebase Structure

Ask the user:

I'll ask a few questions about your development environment to provide better support.

1. **Repository type:** What kind of codebase is this?
   - [ ] Analytics/data warehouse (dbt, SQL files, ETL)
   - [ ] Application backend (API, services)
   - [ ] Full-stack application
   - [ ] Data science / ML project
   - [ ] Other: ___

Record response in

.knowledge/user/dev-context.yaml

under

codebase.type

.

Step 2: Data Layer

Ask the user:

2. **Data layer:** How is your data organized?
   - Database type: (Postgres, BigQuery, Snowflake, DuckDB, other)
   - Schema naming convention: (e.g., `analytics.`, `public.`, `dbt_prod.`)
   - Key tables location: (path to schema definitions, dbt models, etc.)

Record under

codebase.data_layer

.

Step 3: SQL Conventions

Ask the user:

3. **SQL conventions:** Does your team follow specific patterns?
   - Naming: snake_case / camelCase / other
   - Date handling: timezone-aware? Default timezone?
   - NULL handling: COALESCE patterns? Default values?
   - Any team-specific SQL style guide? (path or URL)

Record under

codebase.sql_conventions

.

Step 4: Integration Points

Ask the user:

4. **Integration points:** Where does AI Analyst fit in your workflow?
   - [ ] Ad-hoc analysis only (no integration needed)
   - [ ] Reads from dbt models
   - [ ] Connects to production replica
   - [ ] Uses exported CSV/Parquet files
   - [ ] Accesses data warehouse directly
   - Other: ___

Record under

codebase.integration

.

Step 5: File Conventions

Ask the user:

5. **File conventions:** (optional)
   - Where do analysis outputs go? (default: `outputs/`)
   - Any naming conventions for SQL files?
   - Git branch strategy for analysis work?

Record under

codebase.file_conventions

.

Output

Save collected context to

.knowledge/user/dev-context.yaml

:

schema_version: 1
created: "{{DATE}}"
last_updated: "{{DATE}}"

codebase:
  type: null           # analytics | backend | fullstack | data-science | other
  data_layer:
    database: null     # postgres | bigquery | snowflake | duckdb | other
    schema_prefix: null
    models_path: null  # path to dbt models or schema definitions
  sql_conventions:
    naming: snake_case
    timezone_aware: false
    default_timezone: UTC
    null_handling: null
    style_guide: null
  integration:
    mode: null         # adhoc | dbt | replica | exported | direct
    details: null
  file_conventions:
    output_dir: outputs/
    sql_naming: null
    git_strategy: null

Update

.knowledge/setup-state.yaml

:

dev_context:
  status: complete
  completed_at: "{{DATE}}"

Completion Message

Developer context saved. AI Analyst will now:
- Use your schema prefix ({{schema_prefix}}) in SQL queries
- Follow your team's SQL conventions
- Output files to {{output_dir}}

You can update this anytime with `/setup-dev-context`.

Reset

/setup-dev-context reset

— Clears dev-context.yaml and resets to defaults.