# Thinking Out Loud

Hold action on a long dictated ramble until an echo brief is approved.

- Repository: https://github.com/Shubhamsaboo/awesome-llm-apps (143670 stars)
- Category: AI and agents
- License: Apache-2.0
- Last updated: 2026-10-22
- Runs scripts: no, instructions only

## Install

Claude Code: `npx skills add Shubhamsaboo/awesome-llm-apps --skill thinking-out-loud -g -a claude-code -y`
Codex: `npx skills add Shubhamsaboo/awesome-llm-apps --skill thinking-out-loud -g -a codex -y`
Cursor: `npx skills add Shubhamsaboo/awesome-llm-apps --skill thinking-out-loud -g -a cursor -y`
Gemini CLI: `npx skills add Shubhamsaboo/awesome-llm-apps --skill thinking-out-loud -g -a gemini-cli -y`

## Description

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.

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

# 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

The rest: https://github.com/Shubhamsaboo/awesome-llm-apps/blob/HEAD/agent_skills/thinking-out-loud/SKILL.md