Moai Jev Skill Suggestion logo

Moai Jev Skill Suggestion

OrganizationPopular
modu-ai
moai-jev-skill-suggestion

Guidance for a display-only skill-suggestion capability: how a caller should suggest a skill to the operator, how the emitted answer is read, and what must never happen (selection, auto-loading, judgment). The capability gate ships off; this skill carries mechanism and contract ONLY — never the call path; where and how to reach the capability is the catalogue's concern.

Overview

Publishermodu-ai
Repositorymoai-adk
Skill namemoai-jev-skill-suggestion
Stars
1.2K
Forks
226
Bundled files
Instructions only
LicenseApache-2.0
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.

  • Self-contained

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

  • Open source

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

Installation

Install the Moai Jev Skill Suggestion 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/modu-ai/moai-adk/tree/main/.claude/skills/moai-jev-skill-suggestion
  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/modu-ai/moai-adk.git /tmp/moai-adk
mkdir -p .claude/skills
cp -r /tmp/moai-adk/.claude/skills/moai-jev-skill-suggestion .claude/skills/moai-jev-skill-suggestion
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Moai Jev Skill Suggestion 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 Moai Jev Skill Suggestion 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 Moai Jev Skill Suggestion 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.

Skill Suggestion, Display-Only

The gate comes first

The capability is gated behind workflow.jev.enabled, and the shipped default is off. Confirm the gate before anything else. With the gate off, a caller constructs no request, makes no network call, and proceeds with no signal: an absent signal is NO SIGNAL — the same as "we did not ask" — never a negative answer, and never a reason to change course.

Three readiness surfaces exist for the on state: the gate key itself, the credentials file referenced by convention (~/.moai/.env.typesafe, read by the credential loader — never inlined, never copied into settings), and the doctor readiness check (a TCP-only probe that reports gate, credential, and reachability in three lines without sending any judgment request). A caller that cannot confirm all three proceeds as if the gate were off.

The reachable surface itself lives in the product catalogue, not here. This skill owns mechanism and contract only; the catalogue owns where the call lives.

What a suggestion is

A suggestion is a signal a person reads: a labelled model answer that names a skill that might be worth loading and carries a confidence, and it does nothing else. Four things it is not:

  • Not a selection. The existing selector keeps its selection authority; a suggestion never flips it.
  • Not an auto-load. No skill content is loaded, attached, or injected because a suggestion was emitted.
  • Not a judgment. Not a completion decision, not a merge approval, not a queue mutation — anything hard to undo stays with people.
  • Not a code-path input. Nothing branches on the answer; a human reads it, and the reading is the only consumer.

The loading mechanisms rejected here

Two mechanisms were evaluated and rejected; recording them keeps them from returning quietly:

  • Hiding the listing and injecting a chosen SKILL.md body directly. This breaks the display-only chain: a skill that never appeared in context has no reading human, and a direct content injection is precisely the auto-load the contract forbids.
  • An ambient function-hook wiring that evaluates a suggestion on every prompt. It commercializes a model call per prompt on an unmeasured behavior, fragments the template distribution, and rides an early-experiment surface that can shift without notice. Not adopted. Revisit only with measured evidence that a constant-answer baseline has been beaten — the measurement bar belongs to a separate track, not to this skill.

Operational checklist (for when a caller exists)

Every item below is guidance conditioned on a caller existing — it ships no behavior:

  1. Log each decision, and announce once per session that suggestion logging is active — one visible line per session, not a chorus per call.
  2. Filter non-task origins. A prompt typed by the operator is not a task event; only genuine task origins may produce a suggestion.
  3. Filter invocation-disabled skills. A skill marked disable-model-invocation stays invisible to suggestions; the filter runs before the call, never after the answer.
  4. Cache the roster and skill bodies per session; re-reading listings and bodies on every prompt is the waste a session cache exists to prevent.
  5. Guard against duplicate injection: a skill already present in context is never suggested to appear again. Check what is loaded before showing a name.
  6. Canonicalize names: a display name and a directory id are two spellings of one skill; map both through one canonical form before deduplicating, logging, or displaying.

Question design is a separate discipline

The suggestion call is a judgment over a typed question, and typed questions carry their own rules — compute the question in code, always admit a no-match answer, keep the supplied state small, phrase both polarities. Those rules live in the question-design reference skill; this skill does not restate them. The measurement regime above the surface is likewise owned elsewhere: this skill records the contract, never baseline numbers.

What this skill is not

This skill is not a capability. It ships no caller, registers no command, adds no hook, and changes no setting. It is not a verdict: suggestion answers never close cards, approve merges, or mutate queues. And it is not an availability promise: the gate ships off, and nothing in this skill makes a suggestion surface exist — the checklist above governs only if a caller is ever built elsewhere, and until then an absent suggestion is just NO SIGNAL.

Frequently asked questions

What does the Moai Jev Skill Suggestion AI skill do?

Guidance for a display-only skill-suggestion capability: how a caller should suggest a skill to the operator, how the emitted answer is read, and what must never happen (selection, auto-loading, judgment). The capability gate ships off; this skill carries mechanism and contract ONLY — never the call path; where and how to reach the capability is the catalogue's concern.

Why use Moai Jev Skill Suggestion on TypingMind?

Because you install it once and use it with any model. Moai Jev Skill Suggestion 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 Moai Jev Skill Suggestion in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/modu-ai/moai-adk/tree/main/.claude/skills/moai-jev-skill-suggestion. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Moai Jev Skill Suggestion?

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 Moai Jev Skill Suggestion?

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

Is the Moai Jev Skill Suggestion AI skill free?

Yes. It is published on GitHub by modu-ai under the Apache-2.0 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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