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Thinking Out Loud

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Shubhamsaboo
thinking-out-loud

A contract for what the agent does when a long, messy, stream-of-consciousness ramble arrives (usually voice dictation): act on nothing until the echo brief is approved. The echo audits the entire transfer, mission, locked decisions and constraints, open questions, flips and parked tangents, with the model's inferences and guesses quarantined away from the user's own phrasing, so the user verifies what the model believes, not just what it doubts. Use when the user says "let me think out loud" or wants to ramble a bit before building anything, when a message opens with a speech-to-text preamble like "switching to voice, sorry for typos", when input is a long weakly punctuated stream with restarts and mid-message reversals ("actually no, wait, scrap that idea entirely"), or when the user asks to be interviewed about a fuzzy half-formed idea. Includes an optional capture mode for rambles spread across several messages and an optional targeted interview.

Overview

PublisherShubhamsaboo
Repositoryawesome-llm-apps
Skill namethinking-out-loud
Stars
138.7K
Forks
20.4K
Bundled files
1
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.

  • 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 Shubhamsaboo on GitHub. Read the source before you install it.

Installation

Install the Thinking Out Loud 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/Shubhamsaboo/awesome-llm-apps.git /tmp/awesome-llm-apps
mkdir -p .claude/skills
cp -r /tmp/awesome-llm-apps/agent_skills/thinking-out-loud .claude/skills/thinking-out-loud
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Thinking Out Loud 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 Thinking Out Loud 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 Thinking Out Loud 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.

Thinking Out Loud

A ten minute voice ramble transfers more context than any prompt a person would type, and models reconstruct rambles well. The failure is downstream and invisible: the model fills every gap in the ramble confidently. "The usual model" silently becomes a specific model. "The standard size" becomes a specific viewport. A position the user reversed mid-ramble survives as fact. None of this registers as uncertainty from the inside, so none of it ever becomes a clarifying question. The model then acts on a misreading it fully believes, and the user discovers it an hour of generated work later.

This skill is the fix: before acting on any ramble, produce an echo, a short structured audit of everything absorbed, with the model's own additions quarantined from the user's words. The user corrects three lines instead of debugging a built artifact.

Why an echo instead of follow-up questions

Asking clarifying questions is good, and the interview below does it. But questions alone cannot secure a ramble, for two structural reasons:

  • Questions verify what the model doubts. The echo verifies what the model believes. A clarifying question requires felt uncertainty, and confident misreadings feel like knowledge. The echo forces every inference and gap-fill into the open whether or not it felt uncertain.
  • Questions sample; the echo audits. A long ramble carries dozens of facts and half-decisions. Even good questions probe three or four; the rest of the model's understanding goes unverified into action. The echo inventories the entire transfer, and it works by recognition, not recall: the user reads and spots what is wrong, which is far cheaper than producing answers, and ramblers often do not know their answer until they see the wrong guess written down.

The contract

  1. Act on nothing. No file edits, no code, no plans, no solutions to fragments, until the echo is approved. Reconstruct first.
  2. Label every addition. Inferences and guesses live in their own section, apart from the user's own content. Never present a guess in the user's voice.
  3. Surface every reversal. Adopt the later position, but flag the flip. Never silently average or pick.
  4. Lose nothing. Tangents get parked, not dropped.
  5. Never remark on dictation artifacts. Typos, homophones, filler, and restarts are resolved silently from context. Keep the user's own vocabulary and project names.
  6. Ask before persisting. The approved brief is offered a home, never saved unprompted.

When to use

  • A message is a long, weakly punctuated stream of consciousness with restarts, filler, and mid-message reversals ("actually no, scrap that")
  • A message opens with a voice preamble ("switching to speech recognition, sorry for any typos", "dictating this")
  • The user says they want to ramble or think out loud
  • The user asks to be interviewed to untangle a fuzzy idea

When not to use

  • Short requests that are already clear
  • The user wants a verbatim transcript, minutes, or cleanup of dictation while keeping their exact words
  • Long but already structured text, such as a pasted spec or document
  • The user asked a direct question and wants a direct answer

The echo

One structured reply. Dense, scannable, and short: the user should find and fix an error in seconds. Full template with a worked example in references/echo-format.md.

  1. Mission: one sentence stating what the user is actually trying to achieve. Often this differs from what they said first; that is fine.
  2. Locked: the user's decisions and constraints, merged into one list. Mark anything they called a top priority.
  3. Open: questions the ramble raised but did not answer.
  4. Ledger: flips (both positions in one line, later one adopted) and parked tangents (one line each).
  5. My additions: the only interpretation callouts. "Inferred" (strongly implied but never stated) and "Guessed" (gaps you filled). Tell the user to correct these first.

Compression rules, non-negotiable:

  • Nothing appears twice. Every fact lives in exactly one section.
  • No "you said" recap. Everything outside My additions is the user's own content by definition; only the model's additions get called out.
  • One line per bullet. If a bullet needs two lines, it is two bullets or it is bloat.
  • Vague quantifiers are never silently resolved. "The usual model", "standard size", "soon": each lands in Open or Guessed, never absorbed into a locked item as if it were specified.

Close by inviting corrections and offering the interview.

The interview (optional)

Follow-up questions have their place: after the audit, not instead of it. Only if the user accepts the offer, or asked to be interviewed up front.

  • Ask only about items flagged in Open or Guessed
  • One question per message, highest information gain first
  • Each question states in one clause why it matters
  • Cap at five questions; stop early once answers stop changing the brief
  • After the interview, restate only the sections of the echo that changed

Capture mode (multi-message rambles)

Not needed for dictation tools, where the whole ramble arrives as one message. Use it when the user invokes the skill before rambling and then adds thoughts across several messages, possibly over a long stretch.

  • Acknowledge once, in one short line ("Go ahead, I'm listening. Say 'done' when you want the echo.")
  • For every following message, reply with a single minimal line ("Listening."). Vary it slightly so it does not feel robotic.
  • Do NOT solve, praise, summarize, analyze, or ask questions mid-stream.
  • If the user asks a direct question mid-ramble, answer it in at most two sentences, then return to listening.
  • Exit on "done", "echo", "echo me", "that's it", "what did you get", or any clear equivalent, then deliver the echo.

Persistence

After the user approves the echo, offer exactly three options:

  1. Append the brief to CLAUDE.md so future sessions inherit it
  2. Save it to docs/rambles/YYYY-MM-DD-<topic>.md
  3. Keep it in-conversation only

The approved brief then governs the rest of the session: honor its decisions and constraints without re-asking.

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 Thinking Out Loud AI skill do?

A contract for what the agent does when a long, messy, stream-of-consciousness ramble arrives (usually voice dictation): act on nothing until the echo brief is approved. The echo audits the entire transfer, mission, locked decisions and constraints, open questions, flips and parked tangents, with the model's inferences and guesses quarantined away from the user's own phrasing, so the user verifies what the model believes, not just what it doubts. Use when the user says "let me think out loud" or wants to ramble a bit before building anything, when a message opens with a speech-to-text pream...

Why use Thinking Out Loud on TypingMind?

Because you install it once and use it with any model. Thinking Out Loud 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 Thinking Out Loud in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Shubhamsaboo/awesome-llm-apps/tree/main/agent_skills/thinking-out-loud. 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 Thinking Out Loud?

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 Thinking Out Loud?

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

Is the Thinking Out Loud AI skill free?

Yes. It is published on GitHub by Shubhamsaboo 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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