本文へ移動
cccskills
無料GitHub で公開

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.

インストール方法を見る

含まれるファイル(3)

  • SKILL.md7.3 KB
  • README.md2.8 KB
  • references/echo-format.md2.4 KB

SKILL.md(原文)

インストールする前に、エージェントに与えられる指示の中身を確認できます。

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.

レビュー

まだレビューはありません。使ってみた感想をお寄せください。

同じリポジトリのスキル

概要と使いどころ

Use when a task is too large for one model pass, needs parallel research or generation across many subtasks (like researching a dozen competitors at once), or the user asks to orchestrate multiple models, split work across a model team, run an advisor-worker loop, have a stronger model review the plan while cheap workers execute, or says "too big for one model" or "fan this out". Not for single-file edits or tasks one model handles in one pass.

日本語の概要は準備中です。原文の説明を表示しています。

Shubhamsaboo/awesome-llm-apps14.1万2026年10月1日 更新

DEPRECATED: This skill has been replaced by `mcp-app-builder`. Check if `mcp-app-builder` is available in the skills folder. If not, install it: `npx skills install mcp-use/mcp-use --skill mcp-app-builder` Use `mcp-app-builder` instead of this skill. Build ChatGPT apps with interactive widgets using mcp-use and OpenAI Apps SDK. Use when creating ChatGPT apps, building MCP servers with widgets, defining React widgets, working with Apps SDK, or when user mentions ChatGPT widgets, mcp-use widgets, or Apps SDK development.

日本語の概要は準備中です。原文の説明を表示しています。

Shubhamsaboo/awesome-llm-apps14.1万2026年10月1日 更新

Reconstructs why code exists from local git history, including the introducing commit, later changes, current authors, repeated companion files, and likely intent. Use when the user asks "why does this code exist", "who wrote this function and why", or to "explain the history of this function" before a rewrite, refactor, or risky edit. Runs entirely locally.

日本語の概要は準備中です。原文の説明を表示しています。

Shubhamsaboo/awesome-llm-apps14.1万2026年10月1日 更新

Checks requirements.txt, pyproject.toml, and package.json dependency manifests for surface-level direct-dependency footguns: standard-library shadowing pins, abandoned backports, unpinned dependencies, and obvious intra-manifest conflicts, plus opt-in PyPI yanked releases. Use when the user asks to check a manifest for dependency problems, asks why dependencies won't install or whether anything is wrong with their dependencies, wants a dependency autopsy, or suspects dependency manifest rot. Runs offline by default as a local tool for the user's own project, not repository CI.

日本語の概要は準備中です。原文の説明を表示しています。

Shubhamsaboo/awesome-llm-apps14.1万2026年10月1日 更新

Beta readers for any draft, run by simulating how a real reader experiences it, moment by moment. A skim gate, a no-lookahead timed read producing an attention transcript with quit points, a recall test of what a reader remembers the next day, and a trust ledger of the implied author. Use when the user asks for a beta read, beta readers, test readers, human review, reader review, to read something like a human, to be a first reader, whether a piece holds attention or will actually get read, for a quick read of whether a busy skimmer would even open a post, what readers will remember or take away tomorrow, where readers stop reading or bounce, or why a draft still feels off or hollow after anti-slop or humanizer edits. Final gate before publishing; reports where the reading broke; never rewrites.

日本語の概要は準備中です。原文の説明を表示しています。

Shubhamsaboo/awesome-llm-apps14.1万2026年10月1日 更新

**MANDATORY for ALL MCP server work** - mcp-use framework best practices and patterns. **READ THIS FIRST** before any MCP server work, including: - Creating new MCP servers - Modifying existing MCP servers (adding/updating tools, resources, prompts, widgets) - Debugging MCP server issues or errors - Reviewing MCP server code for quality, security, or performance - Answering questions about MCP development or mcp-use patterns - Making ANY changes to server.tool(), server.resource(), server.prompt(), or widgets This skill contains critical architecture decisions, security patterns, and common pitfalls. Always consult the relevant reference files BEFORE implementing MCP features.

日本語の概要は準備中です。原文の説明を表示しています。

Shubhamsaboo/awesome-llm-apps14.1万2026年10月1日 更新

Shubhamsaboo のスキルをすべて見る

このスキルの問題を報告する