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Mb Map Codebase

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mrvladd-d
mb-map-codebase

Map an existing codebase into an as-is Memory Bank without inventing roadmap items.

Overview

Publishermrvladd-d
Repositorymemobank
Skill namemb-map-codebase
Stars
56
Forks
11
Bundled files
1
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 1 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by mrvladd-d on GitHub. Read the source before you install it.

Installation

Install the Mb Map Codebase AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/mrvladd-d/memobank/tree/main/skills/mb-map-codebase
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/mrvladd-d/memobank.git /tmp/memobank
mkdir -p .claude/skills
cp -r /tmp/memobank/skills/mb-map-codebase .claude/skills/mb-map-codebase
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Mb Map Codebase in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Mb Map Codebase on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Mb Map Codebase is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

mb-map-codebase — Brownfield: Repo → Memory Bank

  • What it does: scans an existing repository, merges evidence from scoped workers, and writes as-is Memory Bank docs.
  • Use it when: code already exists and you need a reliable baseline before planning changes.
  • Input: repository root plus an initialized .memory-bank/.
  • Output: product, architecture, testing, runbook, and contract docs that describe the current system without speculative task planning.

Preconditions

  • You are in the repo root.
  • .memory-bank/ skeleton exists (if not, run mb-init).

Process

1) Start a protocol + task folder

Create:

  • .protocols/MAP-CODEBASE/plan.md
  • .protocols/MAP-CODEBASE/progress.md
  • .protocols/MAP-CODEBASE/verification.md (mapping completeness + evidence)
  • .protocols/MAP-CODEBASE/handoff.md
  • .protocols/MAP-CODEBASE/decision-log.md (optional)

Create:

  • .tasks/TASK-MB-MAP/

Plan MUST include MB-SYNC (link .memory-bank/workflows/mb-sync.md).

2) Spawn scanning subagents (parallel)

Use ./agents/shared-repo-scanner.md as the base worker prompt.

Rules:

  • Max 5–7 parallel subagents.
  • Non-overlapping scopes (to avoid conflicts).
  • Smart calling: each worker validates that its globs match real files.

Suggested scopes:

  • S-01 tooling/CI
  • S-02 backend/services
  • S-03 frontend/UI
  • S-04 data layer
  • S-05 tests/quality

Each subagent writes:

  • .tasks/TASK-MB-MAP/TASK-MB-MAP-S-0X-final-report-<code|docs>-01.md

3) Fan-in (mandatory)

Before writing final docs:

  • read all worker reports
  • merge into a single outline
  • resolve contradictions (or record them as “needs verification”)
  • write a short fan-in note into .protocols/MAP-CODEBASE/progress.md with:
    • which reports were used
    • what conflicted and how resolved
    • open questions

4) Synthesize Memory Bank (as-is)

Using the fan-in view, create/update:

  • product brief (.memory-bank/product.md)
  • architecture overview (.memory-bank/architecture/)
  • normative routing docs when supported by evidence (.memory-bank/spec-index.md, .memory-bank/glossary.md, .memory-bank/invariants.md)
  • runbooks (.memory-bank/runbooks/)
  • contracts (.memory-bank/contracts/)
  • states (.memory-bank/states/) when lifecycle/state rules are evident from code, workflows, or tests
  • testing strategy (.memory-bank/testing/index.md)
  • index (.memory-bank/index.md)

Use references/synthesis-checklist.md.

PRD-less rule (non-negotiable): if there is no prd.md, you MUST NOT create or populate:

  • .memory-bank/epics/*
  • .memory-bank/features/*
  • .memory-bank/tasks/*.task.json with real roadmap tasks

Empty skeleton files/folders are OK if they were created by bootstrap.

Mapping is documentation of the current system, not planning.

In overview docs, explicitly separate:

  • Facts (evidence: paths/commands/logs/tests)
  • Inferences (hypotheses, explicitly marked)

5) Ask for PRD delta

Once baseline MB exists:

  • ask the user for a prd.md describing what to change/add.
  • then run mb-from-prd to plan the delta on top of baseline.

6) Review gate

Run mb-review in fresh context.

7) MB-SYNC

Follow .memory-bank/workflows/mb-sync.md and append a changelog entry.

Definition of done

  • .tasks/TASK-MB-MAP/ contains scoped reports.
  • .memory-bank/ describes the system “as-is” with evidence.
  • Contradictions and open questions are recorded.
  • User has been asked for PRD delta.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Mb Map Codebase AI skill do?

Map an existing codebase into an as-is Memory Bank without inventing roadmap items.

Why use Mb Map Codebase on TypingMind?

Because you install it once and use it with any model. Mb Map Codebase is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Mb Map Codebase in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/mrvladd-d/memobank/tree/main/skills/mb-map-codebase. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Mb Map Codebase?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Mb Map Codebase?

As many as you like. As long as a model supports skills, you can use Mb Map Codebase with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Mb Map Codebase AI skill free?

Yes. It is published on GitHub by mrvladd-d under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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