Checkpoint Review Workflow

SkillProductivity

LLM-assisted human-in-the-loop review. Make sense of a change, focus attention where it matters, test. Use when the user says "checkpoint", "human review", or "walk me through this change".

Available today. Use it from your connected AI after setup.

Connect ahel once, and every AI you use reads what you have installed.

Then ask your AI: use the Checkpoint Review Workflow skill

What this skill tells your AI

The instructions your AI receives, as published by delorenj/mcp-server-trello in .agents/skills/bmad-checkpoint-preview/SKILL.md and read by ahel’s review.

Goal: Guide a human through reviewing a change — from purpose and context into details.

Your Role: You are assisting the user in reviewing a change.

Conventions

  • Bare paths (e.g. step-01-orientation.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.

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:

  • implementation_artifacts
  • planning_artifacts
  • communication_language
  • document_output_language

Step 5: Greet the User

Greet the user, speaking in {communication_language}.

Step 6: Execute Append Steps

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

Activation is complete. Begin the workflow below.

Global Step Rules (apply to every step)

  • Path:line format — Every code reference must use CWD-relative path:line format (no leading /) so it is clickable in IDE-embedded terminals (e.g., src/auth/middleware.ts:42).
  • Front-load then shut up — Present the entire output for the current step in a single coherent message. Do not ask questions mid-step, do not drip-feed, do not pause between sections.
  • Language — Speak in {communication_language}. Write any file output in {document_output_language}.

FIRST STEP

Read fully and follow ./step-01-orientation.md to begin.

Signals

GitHub stars
437
Forks
144
Last commit
Aug 2026
Advanced
Catalog kind
skill
Gateway key
bmad-checkpoint-preview
Source
github.com/delorenj/mcp-server-trello