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Mb Red Verify

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mrvladd-d
mb-red-verify

Adversarial semantic verification for one TASK-* so teams can catch solutions that pass process checks but are still wrong in substance.

Overview

Publishermrvladd-d
Repositorymemobank
Skill namemb-red-verify
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 Red Verify 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-red-verify
  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-red-verify .claude/skills/mb-red-verify
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Mb Red Verify 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 Red Verify 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 Red Verify 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-red-verify — Adversarial semantic verification

  • What it does: pressure-tests one completed TASK-* for semantic correctness, hidden failure modes, and systemic harm.
  • Use it when: a task may be locally correct yet still wrong in product/domain/architectural/operational reality.
  • Input: TASK_ID, task intent, actual change surface, tests/evidence, and only then relevant spec reconciliation.
  • Output: red-verification.md, a concise semantic-risk report, and follow-up bugs/tasks when concerns are serious.

Goal

Catch changes that are "disciplined but wrong":

  • pass acceptance criteria but solve the wrong problem
  • work locally but damage system integrity
  • overfit to the task record and ignore neighboring constraints
  • introduce architectural drift, state inconsistency, or hidden maintenance cost
  • create false confidence because the evidence surface is too narrow

This is intentionally different from existing commands

  • mb-verify checks acceptance criteria and evidence-backed task completion.
  • mb-review reviews Memory Bank quality, planning, and discipline in fresh context.
  • mb-red-verify asks: "Is this solution actually right in substance?"

Status Transition Modes

Status transitions have two modes.

Scheduler mode:

  • /autopilot and /autonomous own task status transitions.
  • Scheduler decides closure/failure/blocking eligibility.
  • /execute returns scoped implementation handoff; it does not close tasks.
  • /verify gives functional verdict/evidence; in scheduler mode it does not close/fail/block/promote.
  • /red-verify gives semantic verdict for T2/T3; in scheduler mode it does not close/fail/block/promote.
  • Scheduler must write the closure/failure/blocking decision, final task status, and evidence links to the authoritative indexed .memory-bank/tasks/TASK-*.task.json record before /mb-sync.
  • /mb-sync records/reconciles already-written task state. It does not decide closure/failure/blocking/promotion and must not sync a decision that exists only in scheduler context.
  • T0/T1 scheduler closure may use compact evidence / functional PASS according to tier policy.
  • T2/T3 scheduler closure requires VERDICT: PASS plus SEMANTIC_VERDICT: semantic-pass before scheduler marks done.
  • T3 scheduler closure also requires exact markers HUMAN_CHECKPOINT: done and ROLLBACK_RECOVERY_NOTE: present.

Manual mode:

  • Expected T0/T1 simple flow: /execute -> /verify for one TASK.
  • Manual closure is allowed only when an explicit closure owner exists.
  • explicit standalone owner means either the user directly asked the current top-level agent to close the task, or the top-level agent/orchestrator explicitly runs a manual workflow for one TASK and records that it owns closure. Subagents/worker prompts do not silently become closure owners.
  • /verify PASS may mark T0 / T1 status: done only when explicit closure ownership is present and completed evidence has been written to the task record verify field and the compact/full protocol required by tier.
  • If explicit closure owner is absent, /verify records VERDICT: PASS, evidence, and a closure recommendation, leaves status unchanged, and tells the scheduler/owner to close.
  • T2 / T3 manual closure requires /verify PASS plus /red-verify / mb-red-verify SEMANTIC_VERDICT: semantic-pass before status: done or /mb-sync; if semantic-pass is absent, leave closure pending or blocked, not done.
  • semantic-concern in manual mode means do not trust the existing done state without human review / follow-up.
  • Do not mix scheduler mode and manual mode inside one task run.
  • No persisted mode field is used.

Preconditions

  • Implementation exists.
  • Quality gates were already run (or failures were recorded).
  • For non-trivial tasks, mb-verify should usually run first.
  • The indexed task record contains tier. Authoritative red-verification routing is only task.tier; the old risk / risk.level model is invalid.
  • For T2 / T3, linked SDD specs are present in task richer fields, feature spec_design_links, or spec-index.md; if absent, stop and route back to /spec-improve or /spec-auto.
  • In scheduler mode, T2 / T3 require this pass before scheduler marks done.
  • In manual mode, this pass is required for T2 / T3 after mb-verify PASS and before final closure//mb-sync; T0 / T1 usually skip it unless their real scope grew beyond the recorded tier.
  • T0 / T1 usually skip it unless scope has grown and the tier is updated first.

Required outputs

Create or update:

  • .protocols/<TASK_ID>/red-verification.md

Store a concise report in:

  • .tasks/<TASK_ID>/<TASK_ID>-S-RED-VERIFY-final-report-docs-01.md

If concerns are material:

  • .memory-bank/bugs/BUG-<short>.md
  • follow-up .task.json records indexed in .memory-bank/tasks/index.json
  • recommended task/dependent status for the active scheduler or explicit standalone owner

Use:

  • ./references/shared-protocols-red-verification-template.md
  • ./agents/red-verifier.md

Input priority

Do not start by over-trusting the same full spec context the implementer used.

Prime in this order:

  1. task intent and expected real-world outcome
  2. actual code changes / diff / touched runtime behavior
  3. tests, logs, screenshots, traces, and other evidence
  4. linked SDD specs and neighboring constraints (contracts/*, states/*, domains/*, runbooks/*, invariants)
  5. broader spec reconciliation

This keeps the verifier from merely confirming the workflow surface.

When to use it

Use mb-red-verify when:

  • task.tier is T2 or T3
  • contracts, states, migrations, or data behavior changed
  • the task crosses boundaries between modules/features
  • runtime or API behavior changed
  • business rules or domain semantics matter heavily
  • acceptance criteria can be satisfied in a narrow but misleading way
  • the change is risky, architectural, or likely to create hidden future cost

When not to use it

Usually skip it for:

  • typo-only edits
  • formatting-only changes
  • isolated mechanical refactors with no behavioral impact

Process

1) Start from task intent and reality, not paperwork

Read only what you need:

  • .protocols/<TASK_ID>/plan.md
  • .protocols/<TASK_ID>/progress.md
  • .protocols/<TASK_ID>/verification.md if it exists
  • linked task record, feature, and requirement docs for intent
  • the real diff / changed files / tests / runtime artifacts

2) Build a hostile hypothesis list

Challenge the solution from multiple angles:

  • wrong problem solved
  • local optimization with systemic harm
  • hidden assumptions
  • cross-boundary regression risk
  • architectural drift
  • state/data inconsistency
  • operational weakness
  • future maintenance burden

3) Reconcile with specs only after forming independent concerns

Then inspect the smallest sufficient spec subset:

  • .memory-bank/spec-index.md and linked SDD specs for T2 / T3
  • relevant contracts/*
  • domains/*
  • states/*
  • runbooks/*
  • requirements.md
  • invariants.md
  • related feature/epic docs

If code and specs disagree, record the drift explicitly rather than silently choosing one side.

4) Produce a hard-to-game report

The output must be concise and high-signal. Include:

  • semantic verdict
  • top substance risks
  • hidden assumptions
  • cross-boundary impact
  • architectural concerns
  • state/data consistency concerns
  • operational concerns
  • future maintenance cost
  • how the change could still be wrong
  • counterproposal or escalation path

For T3, also cover critical/security/runtime/recovery concerns and confirm exact marker lines HUMAN_CHECKPOINT: done and ROLLBACK_RECOVERY_NOTE: present are present before closure.

5) Take action from the verdict

  • semantic-pass: no substantive concerns found; scheduler closure-eligible when mb-verify also has PASS; manual T2 / T3 closure is eligible when mb-verify also has PASS
  • semantic-concern: not proven wrong, but blocked or human-review-required; in manual mode, do not trust existing done without human review / follow-up
  • semantic-fail: substantively wrong, systemically harmful, or too risky to accept; recommend or apply task status: failed according to active workflow ownership and explicit closure ownership

When invoked by /autopilot or /autonomous, mb-red-verify must not independently close the task, write done, write failed, block dependents, or promote dependents. It writes the semantic verdict and returns the recommended status/dependent action to the scheduler.

For semantic-concern, recommend blocking task/dependents, reopening from done, or leaving the task pending human review. If human review accepts the concern, record owner/reason and repeat mb-red-verify; scheduler normal done requires semantic-pass. For semantic-fail, file or recommend a bug, recommend follow-up tasks, recommend or apply status: failed according to active workflow ownership and explicit closure ownership, and stop downstream progression through the scheduler/explicit standalone owner.

Definition of done

  • red-verification.md exists and is substance-focused.
  • The report is concise, skeptical, and not just a rephrased /verify.
  • Serious concerns result in explicit bugs/tasks/escalation.

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 Red Verify AI skill do?

Adversarial semantic verification for one TASK-* so teams can catch solutions that pass process checks but are still wrong in substance.

Why use Mb Red Verify on TypingMind?

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

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

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 Red Verify?

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

Is the Mb Red Verify 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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