bmad-generate-project-context

What this skill does

# Generate Project Context Workflow

**Goal:** Create a concise, optimized `project-context.md` file containing critical rules, patterns, and guidelines that AI agents must follow when implementing code. This file focuses on unobvious details that LLMs need to be reminded of.

**Your Role:** You are a technical facilitator working with a peer to capture the essential implementation rules that will ensure consistent, high-quality code generation across all AI agents working on the project.

## Conventions

- Bare paths (e.g. `steps/step-01-discover.md`) resolve from the skill root.
- `{skill-root}` resolves to this skill's installed directory (where `customize.toml` lives).
- `{project-root}`-prefixed paths resolve from the project working directory.
- `{skill-name}` resolves to the skill directory's basename.

## WORKFLOW ARCHITECTURE

This uses **micro-file architecture** for disciplined execution:

- Each step is a self-contained file with embedded rules
- Sequential progression with user control at each step
- Document state tracked in frontmatter
- Focus on lean, LLM-optimized content generation
- You NEVER proceed to a step file if the current step file indicates the user must approve and indicate continuation.

## On Activation

### Step 1: Resolve the Workflow Block

Run: `python3 {project-root}/_bmad/scripts/resolve_customization.py --skill {skill-root} --key workflow`

**If the script fails**, resolve the `workflow` block yourself by reading these three files in base → team → user order and applying the same structural merge rules as the resolver:

1. `{skill-root}/customize.toml` — defaults
2. `{project-root}/_bmad/custom/{skill-name}.toml` — team overrides
3. `{project-root}/_bmad/custom/{skill-name}.user.toml` — personal overrides

Any missing file is skipped. Scalars override, tables deep-merge, arrays of tables keyed by `code` or `id` replace matching entries and append new entries, and all other arrays append.

### Step 2: Execute Prepend Steps

Execute each entry in `{workflow.activation_steps_prepend}` in order before proceeding.

### Step 3: Load Persistent Facts

Treat every entry in `{workflow.persistent_facts}` as foundational context you carry for the rest of the workflow run. Entries prefixed `file:` are paths or globs under `{project-root}` — load the referenced contents as facts. All other entries are facts verbatim.

### Step 4: Load Config

Load config from `{project-root}/_bmad/bmm/config.yaml` and resolve:
- Use `{user_name}` for greeting
- Use `{communication_language}` for all communications
- Use `{document_output_language}` for output documents
- Use `{planning_artifacts}` for output location and artifact scanning
- Use `{project_knowledge}` for additional context scanning

### Step 5: Greet the User

Greet `{user_name}`, speaking in `{communication_language}`.

### Step 6: Execute Append Steps

Execute each entry in `{workflow.activation_steps_append}` in order.

Activation is complete. If `activation_steps_prepend` or `activation_steps_append` were non-empty, confirm every entry was executed in order before proceeding. Do not begin the main workflow until all activation steps have been completed.

## Paths

- `output_file` = `{output_folder}/project-context.md`

## Execution

- ✅ YOU MUST ALWAYS SPEAK OUTPUT In your Agent communication style with the config `{communication_language}`
- ✅ YOU MUST ALWAYS WRITE all artifact and document content in `{document_output_language}`

Load and execute `./steps/step-01-discover.md` to begin the workflow.

**Note:** Input document discovery and initialization protocols are handled in step-01-discover.md.