agentleFS
marlonvidalmarlonvidal/tech-manager-os/.agents/skills/bmad-generate-project-context/SKILL.md

bmad-generate-project-context

Skill17 starsChanged 4 months agoMITView on GitHub

Create project-context.md with AI rules. Use when the user says "generate project context" or "create project context"

We split this skill into the rules it gives an agent

8 rules. 0 are common, 1 is distinctive, and 7 are very specific to this project.

  • 0

    Common

    Most of the 10 edited versions we read keep these.

  • 1

    Distinctive

    This author's own choices. Worth a look.

  • 7

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Very specific:

    Goal: Create a concise, optimized project-context.md file containing critical rules, patterns, and guidelines that AI agents must follow …

    line 8
  2. Distinctive:

    Document state tracked in frontmatter

    line 25
  3. Very specific:

    Treat every entry in {workflow.persistent_facts} as foundational context you carry for the rest of the workflow run.

    line 49
  4. Very specific:

    Use {user_name} for greeting

    line 54
  5. Very specific:

    Use {communication_language} for all communications

    line 55
  6. Very specific:

    Use {document_output_language} for output documents

    line 56
  7. Very specific:

    Use {planning_artifacts} for output location and artifact scanning

    line 57
  8. Very specific:

    Use {project_knowledge} for additional context scanning

    line 58

“Common” is measured: kept by at least 6 in 10 of the 10 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

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.

Download now (ZIP)

Does it work?

Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.

No reviews yet

People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.

  • It worked
  • Partly
  • It didn't
What did you use it for? e.g. Reviewing pull requests in a Rails app

Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.

Your trail

Files you open will collect here, so you can find them again.

Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.

Now do this for your own.

Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.