Mb Harness

Community

mrvladd-d

mb-harness

Set up deterministic commands, worktrees, and quality gates so agents can run safely in this repository.

Overview

Publisher	mrvladd-d
Repository	`memobank`
Skill name	`mb-harness`
Stars	56
Forks	11
Bundled files	1
License	MIT
Links	Skill source Repository

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 Harness AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

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.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
https://github.com/mrvladd-d/memobank/tree/main/skills/mb-harness
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.

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-harness .claude/skills/mb-harness

Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Mb Harness 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 Harness 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 Harness 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-harness — Harness engineering setup

What it does: defines the execution harness around the repo, including commands, gates, and parallel-safe workflow.
Use it when: the repository needs stronger agent guardrails before autonomous or multi-session work.
Input: repository root and the project’s canonical build, test, and lint commands.
Output: documented quality gates, optional Codex config, and a safer harness for agent execution.

Goal

Turn the repo into a reliable “harness” for agents:

clear entry points (AGENTS.md)
reproducible commands (build/test/lint)
mechanical checks (CI + MB lint)
parallel-safe workflow (worktrees)

Process

1) Codex project configuration (optional but recommended)

If you use Codex:

Create .codex/ folder.
Create .codex/config.toml from assets/codex-config.toml.

Usage examples:

default profile (coding): codex
deep review: codex --profile deep-review

2) Document quality gates

In AGENTS.md (keep it short), list the canonical commands (examples):

install deps
lint / typecheck
unit tests
e2e tests

If the repo has UI or browser flows, explicitly document:

Playwright command(s)
agent-browser / browser MCP path (if available)
where screenshots/videos/traces are stored
which flows are considered release-critical

If the repo lacks them, add minimal scripts/Make targets.

3) Worktree workflow (parallel agents)

If multiple agents work in parallel:

create worktrees per agent to avoid file conflicts
merge only after passing gates

Example:

bash
git worktree add ../wt-agent-1 -b agent-1

4) Add deterministic Memory Bank gates

If not already present, install the packaged Memory Bank gate assets:

scripts/mb-lint.mjs from mb-garden/assets/mb-lint.mjs
scripts/mb-doctor.mjs from mb-garden/assets/mb-doctor.mjs
CI workflow coverage for the default health gates

The mb-garden/assets path is the current packaged asset location, not conceptual ownership of the doctor role. mb-lint covers structural/mechanical hygiene. mb-doctor covers workflow/autonomous readiness over mb-lint. Run default mb-doctor for ordinary pre-queue health checks. Run strict mode only after the JSON task queue exists: after /prd-to-tasks, before scheduler execution inside /autonomous, or before /autopilot when the queue is already prepared:

bash
node scripts/mb-lint.mjs
node scripts/mb-doctor.mjs --strict

The harness should treat missing task.tier as a blocking policy error. Task state is JSON-only through .memory-bank/tasks/index.json and indexed TASK-*.task.json records; backlog.md, markdown task cards, and old risk / risk.level routing are unsupported.

4.1) Browser verification for UI projects

If the product has a UI:

prefer Playwright / agent-browser / CDP-driven checks over “manual looks OK”
persist artifacts (screenshots, videos, traces) into .tasks/TASK-XXX/
document canonical browser verification commands in .memory-bank/testing/index.md

5) Optional: skill eval harness

If you iterate on skills heavily:

use codex exec --json runs + deterministic graders (see OpenAI evals guidance)

Definition of done

.codex/config.toml exists (if using Codex) with coding + review profiles.
AGENTS.md lists quality-gate commands.
repo has a documented path for worktrees.
Memory Bank lint exists and passes.
UI repos have a documented browser-driven verification path.

Bundled files

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

assets/codex-config.toml1 KB

Frequently asked questions

What does the Mb Harness AI skill do?

Set up deterministic commands, worktrees, and quality gates so agents can run safely in this repository.

Why use Mb Harness on TypingMind?

Because you install it once and use it with any model. Mb Harness 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 Harness in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/mrvladd-d/memobank/tree/main/skills/mb-harness. 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 Harness?

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 Harness?

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

Is the Mb Harness 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.

View all

Diagnosing Bugs

mattpocock

CommunityPopular

Diagnosis loop for hard bugs and performance regressions. Use when the user says "diagnose"/"debug this", or reports something broken/throwing/failing/slow.

E2e Testing

Patrones de pruebas E2E con Playwright, Page Object Model, configuración, integración CI/CD, gestión de artefactos y estrategias para pruebas inestables.

Eval Harness

Framework formal de evaluación para sesiones de Claude Code que implementa principios de desarrollo orientado a evals (EDD)

Tdd Workflow

Usar este skill al escribir nuevas funcionalidades, corregir bugs o refactorizar código. Aplica el desarrollo guiado por pruebas con 80%+ de cobertura incluyendo pruebas unitarias, de integración y…

Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

mb-harness — Harness engineering setup

Goal

Process

1) Codex project configuration (optional but recommended)

2) Document quality gates

3) Worktree workflow (parallel agents)

4) Add deterministic Memory Bank gates

4.1) Browser verification for UI projects

5) Optional: skill eval harness

Definition of done

Bundled files

Frequently asked questions

Related AI Skills

Set up your own AI workspace now