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Grill Me

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nateherkai
grill-me

Interview the user relentlessly about a plan, design, or topic, checkpointing every answer to a brainstorm file so nothing is lost. Use when the user wants to stress-test a plan, get grilled on a design, run a brainstorm or discovery session, extract what's in their head into a doc, build up their AI OS context over time, or says "grill me".

Overview

Publishernateherkai
RepositoryAIS-OS
Skill namegrill-me
Stars
1.6K
Forks
458
Bundled files
1
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.

  • 1 bundled files

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

  • Open source

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

Installation

Install the Grill Me 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/nateherkai/AIS-OS/tree/main/.agents/skills/grill-me
  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/nateherkai/AIS-OS.git /tmp/AIS-OS
mkdir -p .claude/skills
cp -r /tmp/AIS-OS/.agents/skills/grill-me .claude/skills/grill-me
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Grill Me 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 Grill Me 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 Grill Me 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.

Grill Me

Relentlessly interview the user about every aspect of the topic until you reach shared understanding. Walk down each branch of the decision tree, resolving dependencies one by one. The real goal is to extract what's in their head into a durable, organized markdown file so nothing is lost as context fills up.

The capture file is the whole point

Long interviews fill up context. If you hold answers only in your head, you will eventually misremember, conflate, or drop something. So you checkpoint to disk after every single answer. The file, not your context, is the source of truth. Never make the user ask you to save progress.

Existing context and scope

Read the applicable AGENTS.md and CLAUDE.md, then only the context pages and prior captures relevant to the topic. Starter-kit context lives in context/, but follow an established project's declared routes instead of creating a duplicate knowledge store. Reuse facts already supplied; ask about gaps, changes, decisions, and tradeoffs. Installing the skill does not start an interview.

Setup (do this BEFORE the first question)

  1. Create the capture file at brainstorms/{YYYY-MM-DD}-{topic-slug}.md (create the brainstorms/ folder if it doesn't exist). Every brainstorm capture lives here. One predictable home, regardless of topic. Do NOT scatter captures into project folders. If a session later produces a polished deliverable (a plan, a map, a spec), that artifact can move into the relevant projects/ folder, but the raw capture always stays in brainstorms/.
    • Get today's actual date using the available clock or shell (Get-Date -Format yyyy-MM-dd in PowerShell, date +%F in Bash).
    • Never overwrite an existing capture. For a new session with the same date/topic, add a time or unique suffix and create exclusively. Resume an existing capture only when the user requests or clearly refers to that session; read it first and continue its question numbering.
    • If no topic is supplied or inferable, create an untitled-discovery capture with goal pending, then ask what to explore. Record the chosen topic and goal in that same file.
  2. Create the file immediately with a header: title, date, the goal of the session, and an empty "Open flags" section.
  3. Tell the user where you're saving, in one line. Then ask Q1.

The checkpoint rule (non-negotiable)

After EVERY user answer, BEFORE you ask the next question:

  • Append a structured entry to the capture file: the question topic, the key facts and decisions from their answer (in their words where the wording matters), and any flags (things they couldn't answer plus who should).
  • Update the running summary when a later answer changes it. Preserve the original Q&A entry, mark it superseded, and link to the correcting answer so the reasoning remains traceable.
  • Read back the saved entry before continuing. If saving fails, explain the failure and keep the answer visible in chat; do not claim it was saved or continue collecting answers without a working checkpoint.
  • Only then ask the next question.

Never batch multiple answers into one write. Checkpoint one answer at a time. The point is that if context is lost at any moment, the file already holds everything said so far.

Interview method

  • Ask one question at a time. For decisions, provide a suggested answer grounded in context and label it as a suggestion. For personal or business facts, ask neutrally; do not lead the user with invented facts or save your inference as their answer.
  • Keep user-confirmed facts, tentative ideas, assistant suggestions, and unresolved questions distinguishable in the capture.
  • Resolve dependencies in order: settle the upstream decision before the ones that depend on it.
  • If a question can be answered by exploring the codebase or reading a file/doc, do that instead of asking. If the user hands you a doc (e.g. a Google Doc), read it and only surface what's net-new.
  • When the user can't answer something, capture it as a flag with the right owner and move on. Don't stall.
  • Keep going until the user says you're done, needs a pause, or you've covered the useful branches. Respect an explicit stop immediately; do not add a new question. Offer a completeness backstop near the end ("anything we haven't touched?").

Capture file structure

# {Topic}: Brainstorm / Discovery Notes
Date: {date} · Goal: {one line}
Status: in progress / paused / complete
Context sources: {relevant existing pages, if any}

## Summary / key decisions
(running synthesis, updated as you go)

## Q&A log
### Q1 - {topic}
- Asked: {question}
- Captured: {user-confirmed facts, decisions, in their words where it matters}
- Tentative / suggested: {unconfirmed ideas, clearly labeled}
- Flags: {open item -> owner}
...

## Open flags (pending input)
- {item} -> {who can answer}

At the end or on pause

  • Read the capture for contradictions or gaps. Mark unresolved conflicts explicitly; do not choose a business fact on the user's behalf. Update the session status and resume point.
  • For sessions explicitly about building or updating AI OS context, merge user-confirmed durable facts and preferences into the appropriate existing context page, with a dated link back to the capture. Preserve unrelated content. Record confirmed meaningful decisions in the existing decision log without duplicating entries. Unknowns, brainstorm ideas, and assistant suggestions stay labeled in the capture until confirmed.
  • For a plan/design interview without a request to update canonical context, keep the raw capture as the output. Suggest /link for routing a useful result, or /level-up for turning a selected opportunity into one improvement. Do not run either automatically.
  • Ensure the operating manual or an existing index routes to brainstorms/ when capture routing is absent, using the project's conventions and preserving shared manual parity. Individual captures do not each need a root-manual entry. Raw captures are dated interview evidence, not automatically current business truth.
  • Give a short recap with a clickable capture path, context pages actually updated, remaining flags, and the next step or resume point.

Boundaries and verification

  • Local interview capture and requested context updates are the scope. No global-memory edits, publication, messages, or external-system changes. Do not request or save credentials; use references or redactions for secrets.
  • The starter kit ignores brainstorms/ in Git by default. This does not remove captures already tracked in an existing repository.
  • Validate a fresh session, same-day name collision, resume, a correcting answer, a stop request, and a failed write. Verify that only confirmed facts reach canonical context and that earlier answers remain traceable.

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 Grill Me AI skill do?

Interview the user relentlessly about a plan, design, or topic, checkpointing every answer to a brainstorm file so nothing is lost. Use when the user wants to stress-test a plan, get grilled on a design, run a brainstorm or discovery session, extract what's in their head into a doc, build up their AI OS context over time, or says "grill me".

Why use Grill Me on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/nateherkai/AIS-OS/tree/main/.agents/skills/grill-me. 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 Grill Me?

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 Grill Me?

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

Is the Grill Me AI skill free?

It is published on GitHub by nateherkai. 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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