skills/codex/context-driven-development/SKILL.md
<!-- AUTO-GENERATED by export-skills.py — DO NOT EDIT --> --- name: context-driven-development description: Use this skill when working with Conductor's context-driven --- # Context-Driven Development Guide for implementing and maintaining context as a managed artifact alongside code, enabling consistent AI interactions and team alignment through structured project documentation. ## Do not use this skill when - The task is unrelated to context-driven development - You need a different domain
npx skillsauth add frank-luongt/faos-skills-marketplace skills/codex/context-driven-developmentInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
3 of 9 scanners reported clean
Some scanners were skipped, did not run, or reported a non-clean status. Review each row below.
Guide for implementing and maintaining context as a managed artifact alongside code, enabling consistent AI interactions and team alignment through structured project documentation.
Context-Driven Development treats project context as a first-class artifact managed alongside code. Instead of relying on ad-hoc prompts or scattered documentation, establish a persistent, structured foundation that informs all AI interactions.
Key principles:
Follow the Context → Spec & Plan → Implement workflow:
Purpose: Captures product vision, goals, target users, and business context.
Contents:
Update when:
Purpose: Establishes brand voice, messaging standards, and communication patterns.
Contents:
Update when:
Purpose: Documents technology choices, dependencies, and architectural decisions.
Contents:
Update when:
Purpose: Establishes development practices, quality gates, and team workflows.
Contents:
Update when:
Purpose: Registry of all work units with status and metadata.
Contents:
Update when:
Ensure changes in one artifact reflect in related documents:
Before adding any new dependency:
After completing a feature track:
Before starting any track:
For new projects:
/conductor:setup to create all artifacts interactivelyCharacteristics:
For existing codebases:
/conductor:setup with existing codebase detectionCharacteristics:
conductor/
├── index.md # Navigation hub linking all artifacts
├── product.md # Product vision and goals
├── product-guidelines.md # Communication standards
├── tech-stack.md # Technology preferences
├── workflow.md # Development practices
├── tracks.md # Work unit registry
├── setup_state.json # Resumable setup state
├── code_styleguides/ # Language-specific conventions
│ ├── python.md
│ ├── typescript.md
│ └── ...
└── tracks/
└── <track-id>/
├── spec.md
├── plan.md
├── metadata.json
└── index.md
/conductor:setupBefore starting implementation on any track, validate context:
Avoid these context management mistakes:
Problem: Context documents become outdated and misleading. Solution: Update context as part of each track's completion process.
Problem: Information scattered across multiple locations. Solution: Use the defined artifact structure; resist creating new document types.
Problem: Relying on knowledge not captured in artifacts. Solution: If you reference something repeatedly, add it to the appropriate artifact.
Problem: One person maintains context without team input. Solution: Review context artifacts in pull requests; make updates collaborative.
Problem: Context becomes so detailed it's impossible to maintain. Solution: Keep artifacts focused on decisions that affect AI behavior and team alignment.
Configure your IDE to display context files prominently:
Consider pre-commit hooks that:
Include context validation in pipelines:
Conductor supports multi-session development through context persistence:
If interrupted mid-task:
[~] with note about stopping pointdevelopment
<!-- AUTO-GENERATED by export-skills.py — DO NOT EDIT --> --- name: databricks-mlflow-evaluation --- # MLflow 3 GenAI Evaluation ## Before Writing Any Code 1. **Read GOTCHAS.md** - 15+ common mistakes that cause failures 2. **Read CRITICAL-interfaces.md** - Exact API signatures and data schemas ## End-to-End Workflows Follow these workflows based on your goal. Each step indicates which reference files to read. ### Workflow 1: First-Time Evaluation Setup For users new to MLflow GenAI evalu
development
<!-- AUTO-GENERATED by export-skills.py — DO NOT EDIT --> --- name: databricks-lakebase-provisioned --- # Lakebase Provisioned Patterns and best practices for using Lakebase Provisioned (Databricks managed PostgreSQL) for OLTP workloads. ## When to Use Use this skill when: - Building applications that need a PostgreSQL database for transactional workloads - Adding persistent state to Databricks Apps - Implementing reverse ETL from Delta Lake to an operational database - Storing chat/agent m
tools
<!-- AUTO-GENERATED by export-skills.py — DO NOT EDIT --> --- name: databricks-jobs --- # Databricks Lakeflow Jobs ## Overview Databricks Jobs orchestrate data workflows with multi-task DAGs, flexible triggers, and comprehensive monitoring. Jobs support diverse task types and can be managed via Python SDK, CLI, or Asset Bundles. ## Reference Files | Use Case | Reference File | | ----------------------
development
<!-- AUTO-GENERATED by export-skills.py — DO NOT EDIT --> --- name: databricks-genie --- # Databricks Genie Create and query Databricks Genie Spaces - natural language interfaces for SQL-based data exploration. ## Overview Genie Spaces allow users to ask natural language questions about structured data in Unity Catalog. The system translates questions into SQL queries, executes them on a SQL warehouse, and presents results conversationally. ## When to Use This Skill Use this skill when: -