ask

Overview of ask skill

What the ask skill does

The ask skill is a lightweight /ask workflow for fast answers inside OrbitOS. Its job is simple: answer a question directly, optionally check existing knowledge in the vault first, and avoid turning a quick lookup into a full research session. If you want low-friction help without creating plans, notes, or extra agent orchestration, ask is the right fit.

Who should install ask skill

This ask skill is best for users already working with an OrbitOS-style knowledge base, especially if they keep reusable material in folders like 30_Research/ and 40_Wiki/. It is useful for:

• quick factual or procedural questions
• brief coding help with small examples
• answering from existing internal notes when available
• deciding whether something deserves a permanent wiki note

Why ask is different from a generic prompt

A normal prompt can answer questions, but ask for Knowledge Bases adds a specific operating rule: check existing knowledge first when relevant, then answer concisely, and only save to the vault if the result is truly reusable. That makes it better for teams or individuals who want quick answers without cluttering their knowledge system.

Main tradeoffs before you adopt

ask is intentionally narrow. It is not for deep research, multi-step planning, or tasks that need sub-agents and long document creation. Its value comes from speed and restraint. If your workflow depends on automatic note capture for every answer, this ask guide should help you see that the skill is designed to do the opposite unless the insight is worth preserving.

How to Use ask skill

Install context and where to read first

The repository evidence shows only one real source file: EN/.agents/skills/ask/SKILL.md. Read that first because it contains the full workflow and boundaries. There is no separate README.md, metadata.json, or helper scripts to explain missing behavior. This matters for ask install decisions: what you see in SKILL.md is effectively the whole contract.

What input the ask skill needs

To use ask skill well, give it:

• the actual question
• relevant project or vault context
• whether you want a fast answer or a reusable note
• any constraints like language, format, or code stack

Weak input:

• “Explain embeddings.”

Stronger input:

• “Using our OrbitOS notes style, explain embeddings in 5 sentences for a beginner. If a relevant wiki note already exists, reference it. Include one Python example only if it helps.”

That stronger prompt matches the ask usage pattern: direct answer first, optional vault reference, minimal overhead.

A practical ask usage workflow

A reliable ask workflow looks like this:

1. Trigger /ask for a short question.
2. Let the skill check 30_Research/ or 40_Wiki/ if existing knowledge would likely help.
3. Return a concise answer in chat.
4. Include a code snippet only when it materially improves understanding.
5. Offer to save a note only if the answer is reusable beyond this one conversation.

This keeps the skill fast. If you ask broad questions like “research all options” or “design a complete system,” you are outside its intended scope and will get weaker results than with a more structured skill.

Prompt patterns that improve output quality

The best ask guide advice is to turn vague questions into bounded requests. Include:

• audience: beginner, teammate, decision-maker
• scope: one concept, one comparison, one bug
• expected output: bullets, short answer, example
• vault behavior: “check notes first” or “no note needed”

Example:

• “/ask Compare vector databases vs Postgres pgvector for a small internal KB. Keep it to 6 bullets, mention tradeoffs, and link any existing note if we already covered this.”

This works better than a generic prompt because it aligns with the skill’s direct-answer format and prevents overproduction.

ask skill FAQ

Is ask skill good for beginners?

Yes, especially if you want concise answers without learning a heavy workflow first. The main thing beginners should know is that ask skill is not a teaching framework by itself; it is a fast-answer tool. If you need step-by-step tutoring or a full study note every time, you may need a different skill or a more explicit prompt.

When should I use ask instead of a normal chat prompt?

Use ask when you want quick retrieval-plus-answer behavior inside a knowledge-base workflow. The differentiator is not raw model intelligence; it is the discipline: optionally check the vault, answer directly, avoid unnecessary note creation, and keep the response lean. That makes ask for Knowledge Bases a better fit than ordinary prompting when note clutter is a real problem.

When is ask the wrong skill?

Do not use ask for:

• large research tasks
• project planning
• multi-file implementation work
• workflows needing sub-agents
• mandatory documentation capture for every answer

The skill explicitly avoids over-engineering. If your task needs deep synthesis, ask is likely too small.

Does ask automatically save everything to the vault?

No. The ask skill only suggests saving when the output contains genuinely reusable knowledge. That is a feature, not a gap. It protects your wiki from filling up with one-off Q&A that will never be reused.

How to Improve ask skill

Give ask skill better retrieval hints

The biggest quality gain comes from telling ask where knowledge might already exist. Mention note names, categories, or likely folders. For example:

• “Check 40_Wiki/AI/ first.”
• “We may already have a note on [[RAG Basics]].”
• “Use existing research if available, otherwise answer from first principles.”

This reduces guesswork and increases the chance that ask uses your own knowledge base instead of generating a detached answer.

Prevent the most common failure modes

Weak results usually come from one of three issues:

• the question is too broad
• the expected output format is unclear
• the user really needs a different skill

If ask keeps answering too generally, narrow the task: one concept, one comparison, one troubleshooting target. If it writes too much, explicitly request “short answer only.” If it skips reusable knowledge, ask it to “offer note-saving only if broadly useful.”

Provide stronger inputs for code and technical answers

When asking technical questions, include stack, version, and failure point. Example:

• “/ask In Python 3.11, how do I parse ISO timestamps with timezone offsets? Give one minimal example and mention pitfalls.”

That is much better than:

• “How do timestamps work in Python?”

The ask skill can include code examples, but only when the request is concrete enough for a useful snippet.

Iterate after the first answer

A good ask usage pattern is two-pass refinement:

1. get the direct answer
2. ask for one improvement only

Useful follow-ups:

• “Make this clearer for a beginner.”
• “Turn this into 4 bullets.”
• “Now check whether we already have a related wiki note.”
• “This seems reusable; draft a wiki-note version.”

This keeps ask fast while still letting you promote valuable answers into your knowledge base when they deserve it.