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Nw Command Optimization Workflow

Organization
nWave-ai
nw-command-optimization-workflow

Step-by-step workflow for converting bloated command files to lean declarative definitions

Overview

PublishernWave-ai
RepositorynWave
Skill namenw-command-optimization-workflow
Stars
616
Forks
65
Bundled files
Instructions only
LicenseMIT
Links
Skill sourceRepositoryHomepage
  • 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.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by nWave-ai on GitHub. Read the source before you install it.

Installation

Install the Nw Command Optimization Workflow 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/nWave-ai/nWave/tree/main/nWave/skills/nw-command-optimization-workflow
  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/nWave-ai/nWave.git /tmp/nWave
mkdir -p .claude/skills
cp -r /tmp/nWave/nWave/skills/nw-command-optimization-workflow .claude/skills/nw-command-optimization-workflow
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Nw Command Optimization Workflow 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 Nw Command Optimization Workflow 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 Nw Command Optimization Workflow 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.

Command Optimization Workflow

Overview

Convert oversized commands (500-2400 lines) to lean declarative definitions (40-300 lines) by removing duplication, extracting domain knowledge to agents, and adopting the forge.md pattern.

Phase 1: Analyze

  1. Classify: dispatcher, orchestrator, or simple
  2. Measure: wc -l the file
  3. Identify content categories (approximate %): parameter parsing/validation | Workflow/orchestration logic | Agent prompt templates | Examples/documentation | Error handling | Boilerplate (orchestrator briefing, agent registry)
  4. Flag reducible content: Duplication from other commands (orchestrator briefing, agent registry, parameter parsing) | Domain knowledge belonging in agents (TDD phases, review criteria, methodology) | Dead code (deprecated formats, aspirational features, old signatures) | Verbose examples (JSON blocks exceeding 3 examples) | Aggressive language (count CRITICAL/MANDATORY/MUST)

Phase 2: Extract

2a: Remove shared boilerplate

These blocks appear in 5-12 commands, extract to shared orchestrator preamble skill:

  • "Sub-agents have NO ACCESS to Skill tool" briefing (~20-30 lines/file)
  • Agent registry with capabilities (~15-20 lines/file)
  • Parameter parsing rules (~10-15 lines/file)
  • Pre-invocation validation checklist (~10-15 lines/file)
  • "What NOT to include" blocks (~8-12 lines/file)

After extraction, command references preamble.

2b: Move domain knowledge to agents

If command contains HOW the agent works, move to agent definition or skill:

  • TDD phase details -> nw-software-crafter | DIVIO templates -> nw-documentarist | Refactoring hierarchies -> nw-software-crafter | BDD/Gherkin -> nw-acceptance-designer | Review criteria -> reviewer agent | Mutation testing config -> nw-software-crafter

Command retains WHAT and success criteria. Agent owns HOW.

2c: Delete dead content

Remove: deprecated format references | Aspirational unimplemented features | Verbose JSON contradicting current format | BUILD:INJECT placeholders | Mixed-language comments

2d: Reduce aggressive language

  • "CRITICAL: You MUST extract..." -> "Extract..."
  • "MANDATORY: Use the Task tool" -> "Use the Task tool"
  • "NON-NEGOTIABLE requirement" -> Remove adjective entirely

2e: Condense examples

Keep 2-3 canonical examples max. Each demonstrates distinct pattern (correct usage, edge case, common mistake).

Phase 3: Restructure

Apply declarative command template (load command-design-patterns skill):

  1. Header: wave, agent, overview
  2. Context files required
  3. Agent invocation with configuration
  4. Success criteria
  5. Next wave handoff
  6. Expected outputs

For orchestrators, add Phases section between overview and agent invocation.

Phase 4: Validate

  • Under size target (dispatchers: 40-150, orchestrators: 100-300)
  • No duplicated boilerplate
  • No domain knowledge belonging in agents
  • No aggressive language (CRITICAL/MANDATORY/MUST count = 0)
  • 2-3 examples max
  • No dead code or deprecated references
  • Declarative structure (WHAT not HOW)
  • Measurable success criteria
  • Clear agent invocation pattern

Phase 5: Measure and Report

Report before/after: Line count (target 60-80% reduction for large files) | Content categories removed/moved | Aggressive language removed | Dependencies created (shared preamble, agent skill additions)

Special Case: develop.md (Mega-Orchestrator)

develop.md at 2,394 lines requires special handling:

  1. Embeds 6+ sub-command workflows inline
  2. Should reference sub-commands, not embed them
  3. Orchestration logic (phase sequencing, resume) is legitimate -- keep it
  4. Agent prompt templates for sub-commands -> replace with references
  5. Target: 200-300 lines of pure orchestration

Approach: extract orchestration phases as lean sequence (which agent, which command, what context, what gate), remove all embedded sub-command content.

Shared Orchestrator Preamble

Extract common orchestrator knowledge to single shared skill:

  • Architectural constraint about sub-agent tool access
  • Valid agent list with brief capabilities
  • Standard parameter parsing rules
  • Pre-invocation validation pattern
  • Correct/wrong invocation examples (2 each)

Estimated: ~60-80 lines. Replaces ~620 lines duplicated across commands.

Frequently asked questions

What does the Nw Command Optimization Workflow AI skill do?

Step-by-step workflow for converting bloated command files to lean declarative definitions

Why use Nw Command Optimization Workflow on TypingMind?

Because you install it once and use it with any model. Nw Command Optimization Workflow 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 Nw Command Optimization Workflow in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/nWave-ai/nWave/tree/main/nWave/skills/nw-command-optimization-workflow. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Nw Command Optimization Workflow?

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 Nw Command Optimization Workflow?

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

Is the Nw Command Optimization Workflow AI skill free?

Yes. It is published on GitHub by nWave-ai 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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