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Wwud

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Jamailar
wwud

What Would User Do for RedConvert. Infer how the current app user would decide, approve, reject, scope, phrase, prioritize, or route work inside RedClaw, automation approvals, creative workflows, manuscript/media decisions, advisor/member discussions, and product operations. Use when the model reaches a decision point that normally needs user judgment, and use after explicit user corrections so the app can learn the user role logic.

Overview

PublisherJamailar
RepositoryBeav
Skill namewwud
Stars
1.7K
Forks
225
Bundled files
2
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 Jamailar on GitHub. Read the source before you install it.

Installation

Install the Wwud 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.
  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/Jamailar/Beav.git /tmp/Beav
mkdir -p .claude/skills
cp -r /tmp/Beav/desktop/builtin-skills/wwud .claude/skills/wwud
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Wwud 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 Wwud 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 Wwud 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.

WWUD for RedConvert

WWUD means "What Would User Do". Use it to make RedConvert's AI decisions closer to the current user's real judgment, role logic, taste, and operating habits.

WWUD has two jobs:

  • Decide: infer the user's likely choice at a decision point.
  • Learn: turn user confirmations, corrections, rejections, and approvals into future decision rules.

App-Specific Context Sources

Use the most specific available source first:

  1. Current user message and task metadata.
  2. Current RedClaw / Wander / chat session context.
  3. profiles://user and profiles://creator_profile.
  4. Operate(resource="redclaw.profile", operation="bundle", input={}).
  5. Active advisor/member skill when the task asks how a role would judge.
  6. Knowledge files, manuscript state, current media/project files, and approval queue details.
  7. Session transcripts, session bundles, automation history, and prior decision logs when available.

Do not use generic personality guessing when app-local evidence exists.

Decision Classes

  • Routine: wording, ordering, compact UI choice, reversible draft direction, low-risk internal routing.
  • Material: architecture, user-visible product behavior, creative direction, publishing preparation, automation approval, non-trivial time/cost tradeoff.
  • Restricted: irreversible deletion, external publish/send, production deployment, money, credentials, account permissions, privacy exposure, legal/compliance, or any hard-to-undo action.

Decide routine choices when confidence is medium or high. Decide material choices only when confidence is high and reversible. Escalate restricted choices.

RedConvert Preference Model

Prefer these defaults unless fresher user evidence says otherwise:

  • Finish the actual job once execution starts; avoid stopping at abstract advice after the user says continue, fix, execute, or complete.
  • Make repo-, file-, page-, artifact-, or workflow-specific decisions instead of generic recommendations.
  • Inspect real evidence before diagnosing: code, logs, state stores, transcripts, bundles, generated files, app data, and UI behavior.
  • Keep UI additions small, intuitive, and low-text. Prefer existing surfaces over new pages.
  • Respect strong scope boundaries. If the user corrects the boundary, treat the correction as high-priority evidence.
  • Use existing app primitives: RedClaw orchestration, advisor/member skills, profiles, manuscripts, media tools, knowledge retrieval, automation queue, and skills.invoke.
  • Avoid keyword-forced routing. Prefer typed task metadata, active skills, explicit user choice, and runtime contracts.
  • Keep changes atomic. Do not bundle unrelated fixes or generated side effects into one decision.

Decide Workflow

  1. State the decision point in one sentence.
  2. Classify it as routine, material, or restricted.
  3. Read the narrowest relevant app evidence.
  4. Compare options against the user model.
  5. Return:
text
decision: ...
confidence: high|medium|low
evidence:
- ...
risk: ...
fallback: ...
learn: optional observation if this outcome should update the model

For UI-facing or chat-facing answers, keep the wording natural and concise, but preserve the same fields when the decision affects execution.

Learn Workflow

Record learning when the user:

  • approves or rejects an automation item
  • changes a RedClaw plan or route
  • corrects a product/architecture boundary
  • chooses one creative direction over another
  • says a UI is too much, too verbose, too hidden, too abstract, or off-brand
  • asks for a workflow to behave differently next time
  • tells a member/advisor how it should think or speak

The learning event must include:

  • source: where the observation came from
  • decision: what was being decided
  • chosen: what the user preferred
  • rejected: what the user moved away from
  • principle: the reusable rule
  • confidence: high, medium, or low
  • expires: optional, when the rule is likely temporary

Do not turn a one-off exception into a global rule unless the user frames it as a rule.

Role Logic

When asked to decide as a role, separate three layers:

  • User logic: what the app owner/operator/user tends to choose.
  • Creator profile logic: what the current content account or project should choose.
  • Member/advisor logic: what this specific simulated role would argue.

If these conflict, surface the conflict instead of blending them into a vague compromise.

Safety Boundary

WWUD is not authorization. It may recommend, rank, or prepare an action. It may not silently execute restricted actions.

When the right answer is to ask, ask one precise question and include the default you would choose if the user delegates 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 Wwud AI skill do?

What Would User Do for RedConvert. Infer how the current app user would decide, approve, reject, scope, phrase, prioritize, or route work inside RedClaw, automation approvals, creative workflows, manuscript/media decisions, advisor/member discussions, and product operations. Use when the model reaches a decision point that normally needs user judgment, and use after explicit user corrections so the app can learn the user role logic.

Why use Wwud on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Jamailar/Beav/tree/main/desktop/builtin-skills/wwud. 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 Wwud?

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 Wwud?

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

Is the Wwud AI skill free?

It is published on GitHub by Jamailar. Check the repository for licensing terms. 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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