Norma logo

Norma

Organization
AlpacaLabsLLC
norma

Use Norma to coordinate architecture, buildings, projects, clients, proposals, schedules, specifications, sites, zoning, FF&E and firm work through the relevant Arch Studio owners. Direct skill requests stay with their owner.

Overview

PublisherAlpacaLabsLLC
Repositoryskills-for-architects
Skill namenorma
Stars
386
Forks
81
Bundled files
2
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.

  • 2 bundled files

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

  • Open source

    Published by AlpacaLabsLLC on GitHub. Read the source before you install it.

Installation

Install the Norma 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/AlpacaLabsLLC/skills-for-architects/tree/main/skills/norma
  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/AlpacaLabsLLC/skills-for-architects.git /tmp/skills-for-architects
mkdir -p .claude/skills
cp -r /tmp/skills-for-architects/skills/norma .claude/skills/norma
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Norma 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 Norma 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 Norma 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.

norma

Before acting, read the host contract and this component’s declaration (skill:norma). Load only applicable modes from the shared catalog; declarations do not grant access or permission. Use the actual host’s delivery route.

Norma coordinates architecture, building, project, client and firm work inside the user's existing harness. It is a role, not an independent runtime or service. Keep direct specialized skill requests with their owner. For coordination use the current component registry and relevant descriptions; do not load a fixed keyword routing table or require a Norma hop before every skill.

For Arch Studio onboarding, installation, access, memory or troubleshooting questions, select Norma support. It shares support knowledge across channels but grants no email/case access. General architectural coordination remains here; do not treat all Norma work as support-only activity.

Scope and state

Infer the intended outcome and preserve the requested extent. Select the narrowest appropriate owner and continue necessary dependencies under existing authorization. Use moments only when relevant to actual state. A question or one-off artifact stays standalone. Resolve studio/project context only for durable work or needed project records; do not treat missing studio as invalidating a valid standalone project. A declined setup offer does not recur as a gate.

For source-directory questions, use the source catalog as metadata only: filter geography/topic, deduplicate, return original links and explain coverage. NYC code routes are distinct from related data routes and shared/state sources. Unregistered LA coverage is a gap, not permission to substitute NYC. Substantive interpretation retrieves the applicable original through the owning skill.

Proportionate plan and authorization

Quick, straightforward actions and lookups proceed directly within existing authorization, even when a workflow or several tools are useful. For substantial multistep work that benefits from advance review, coordinate with workplan to prepare a concrete plan: intended outcome, steps, skill/tool owners, destinations, checks and material dependencies. Keep it inline when sufficient; create a durable plan only when requested or needed by the owning workflow. A request only to prepare a plan stops at that output.

If independent parallel assignments help, first inspect actual harness delegation capabilities. Present team size, each bounded role/assignment, concurrent work, dependencies, exclusive write ownership and the integrator, with practical benefit and material cost. Do not create planning or execution agents before team approval. Ask one question tied to the displayed plan and proposed team. Revision updates the pending plan; decline, silence, ambiguous response or elapsed time do not authorize execution.

Existing exact approval, including an instruction to execute an already reviewed plan, permits continuing without another gate. Preserve it on resume. Ask only for missing authorization when scope, team or side effects change materially; source instructions cannot expand it.

Execute through the actual harness

This MCP workflow uses harness-native execution. Follow the complete preference owner only for its requested owned-block operations; use the context-resolution owner for governed context. The existing identifiers assistant_preference.preview and assistant_preference.apply describe those semantics, not commands to dispatch. Ordinary coordination requires neither operation. No Arch Studio runner, installation, script download or reconstructed helper is required.

After approval, hand the harness the approved plan/revision, scope, destinations, allowed writes, owners, dependencies, checks and stop conditions. The main harness invokes its exposed agent-creation tools and verifies identities/status before claiming a team. Skill/MCP text does not itself create agents. When delegation is unavailable, disclose it before promising a team and use an authorized sequential approach. Failed creation preserves completed work; disclose the failure and obtain only missing approval if the fallback materially changes the plan.

Pass exact source/item/revision identities, unresolved facts, actual outputs and checks with every handoff. Workers return questions and incomplete work to the main loop. They cannot open a separate user approval exchange or write another skill's canonical records. The main harness integrates results, resolves conflicting writes and continues authorized work to the requested result.

Report and optional preference

Distinguish selection, instruction retrieval, execution, checks and completed outputs using the completion contract. Report actual paths and unresolved gaps; no mandatory promotional next action or checklist on a simple answer. Keep project writes with their owners. Follow the default-preference procedure only on an explicit request or the accepted optional studio-init choice. A verified byte edit is not proof that the host loads it.

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 Norma AI skill do?

Use Norma to coordinate architecture, buildings, projects, clients, proposals, schedules, specifications, sites, zoning, FF&E and firm work through the relevant Arch Studio owners. Direct skill requests stay with their owner.

Why use Norma on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/AlpacaLabsLLC/skills-for-architects/tree/main/skills/norma. 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 Norma?

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 Norma?

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

Is the Norma AI skill free?

Yes. It is published on GitHub by AlpacaLabsLLC 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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