Prompt Hotbar

Prompt Hotbar began with nine number-key shortcuts for repeated operational instructions. Continued use turned it into a broader prompt-reuse workspace with 58 built-in public-safe prompts, 10 preset and Workflow Sets, browser-local personalization, safe sharing, reviewed Discovery, LAB experiments, and a private local MCP pilot in Claude Code.

Overview

Prompt Hotbar is a keyboard-first workspace for discovering, organizing, and reusing operational prompts across recurring AI workflows.

It began with a very small frustration: I kept typing the same short instructions.

“Looks good, proceed.”
“Stay within scope.”
“Verify the work when you’re done.”

Longer instructions increasingly moved to voice input, while the keyboard was still carrying these short approval, scope-control, verification, and handoff messages. I wanted those instructions close at hand without turning them into another document I had to search.

That became Prompt Hotbar.

국문 요약: 반복해서 쓰는 AI 업무 지시를 1–9번 퀵슬롯에 넣고, 검색·프리셋·Workflow Set을 통해 필요한 순간에 바로 찾아 복사하는 키보드 중심의 프롬프트 워크스페이스입니다. 최근에는 검토된 공개-safe 카탈로그를 Claude Code 안에서 직접 검색·추천·조회하는 로컬 read-only MCP 개인 파일럿까지 확장했습니다.

Live product: prompt-hotbar.vercel.app
Reviewed Discovery: Prompt, Workflow, and LAB assets
Evidence-first Guide: Inspect, verify, and audit before asking AI to change anything
Local MCP pilot: Testing Prompt Hotbar inside Claude Code
Source status: private development repository
Published LinkedIn post: From repeated instructions to Prompt Hotbar

What it does

The core interaction remains deliberately simple:

Find a prompt in the library or choose a reusable set.
Place it into one of nine Hotbar slots.
Click the slot or press its number key.
Paste the copied instruction into the AI conversation already in progress.

Prompt Hotbar does not perform the task for the user. It does not execute prompts or automatically send them to an AI service. It only makes an instruction that already exists easier to retrieve at the right moment.

The workflow shown in the launch demo

The published demo connects Prompt Hotbar to an actual planning context rather than showing copy behavior in isolation.

A feature is planned with ChatGPT.
A reusable verification prompt is found in Prompt Hotbar.
The prompt is placed into slot 4 and copied with the number key.
It is sent back into the same conversation.
The existing implementation plan is converted into a concrete verification checklist.

This is the product boundary in practice: Prompt Hotbar sits between planning and execution as a retrieval layer, while the AI conversation keeps its own context and performs the reasoning.

The LinkedIn-native demo is embedded on this page. The original LinkedIn post remains the canonical source for its bilingual launch copy, reactions, comments, and publication context. If LinkedIn does not allow the player to load in a particular browser, the original-post link remains available directly below it.

Local MCP pilot

A private local pilot tested whether Prompt Hotbar could provide the same reviewed retrieval layer directly inside an AI coding client.

Claude Code connected to a local stdio MCP server and exposed five read-only tools for prompt search, prompt retrieval, contextual recommendation, workflow search, and workflow retrieval. The runtime used a generated public-safe snapshot containing 23 reviewed prompts and three workflows.

The pilot successfully handled natural-language requests to:

recommend a prompt for resuming interrupted work
suggest verification-stage prompts and explain why they fit
retrieve the nine-step coding-agent workflow
return the canonical body of a requested prompt

The MCP did not read browser-local personal prompts, clipboard data, Hotbar assignments, search history, accounts, or cloud state. It introduced no write tool and did not execute the retrieved prompt.

This creates a complementary product boundary:

Web product: visual discovery, organization, personal browser-local prompts, nine-slot placement, and copy
Local MCP pilot: read-only search, recommendation, and retrieval inside a compatible AI client
AI client: task context, reasoning, and any actual execution

The package is prepared as a standalone artifact, but public npm publication remains intentionally withheld while personal-use value is evaluated. See the local MCP pilot proof note for the exact evidence and limitations.

Current product experience

What started as nine shortcuts now includes:

Nine configurable Hotbar slots with click and number-key copy
58 built-in public-safe prompts for approval, verification, scope control, handoff, reporting, safety, writing, release, and token-saving work
10 built-in preset and Workflow Sets for switching between recurring work modes
Search and discovery controls across Board and List views
Two-way placement from a prompt to a slot or from an empty slot to a prompt
Browser-local personal prompts for adding and managing the user’s own reusable instructions
Guided onboarding that demonstrates search, placement, and keyboard copy
Korean and English UI with a separate AI response-language preference
Safe sharing based on stable IDs and slot numbers rather than prompt bodies
Reviewed Discovery pages for selected Prompt, Workflow, and LAB assets
LAB experiments for evaluating prompt and model capability patterns
A private local MCP pilot for read-only retrieval of the reviewed public-safe catalog inside Claude Code
Structured feedback channels for product feedback, bug reports, and prompt suggestions

Reviewed Discovery proof set

The v0.5.0 Discovery Foundation publishes a small reviewed proof set rather than generating public pages for every built-in asset.

Prompts

Workflows

Guide

Before Asking AI to Change Anything: An Evidence-first Inspect, Verify, and Audit Workflow

LAB

Pelican on a Bicycle — SVG capability check

Prompt Hotbar owns the canonical asset pages and their copy or open-in-product actions. This site keeps the project story, methodology, and proof-of-work context without duplicating full prompt bodies.

Privacy and product boundary

Personal prompt content, clipboard contents, search text, Hotbar assignments, and local settings stay on the device.

Shared links do not contain prompt bodies or personal configuration. Production measurement is limited to a small allowlisted set of non-personal events; automatic pageviews, session recording, autocapture, and person profiles are disabled. Public detail-page view labels use cached, nonlinear aggregate buckets rather than raw counts or unique-person claims.

The public web product intentionally does not include:

AI prompt execution
Accounts or cloud synchronization
A server-side prompt database
Browser-wide global shortcuts
In-product payments, teams, or a prompt marketplace

The local MCP pilot does not change those boundaries. It reads only the reviewed public-safe snapshot and does not access browser-local personal state.

Prompt Hotbar does not process payments inside the product. Future support links, if activated, remain an external Support Layer and do not change access to the Core Utility.

What changed through use

The first version treated the problem as prompt storage. Using it repeatedly showed that retrieval was the harder part.

That changed the product direction. Similar prompts needed to be grouped. The library needed search. Sets needed to support different modes of work. New users needed to understand the placement and copy model quickly. Feedback needed its own path. Reviewed public asset pages also became necessary so that selected Prompt, Workflow, and LAB artifacts could be understood before opening the product.

The local MCP pilot extended the same observation: when the user is already inside an AI client, the useful retrieval surface may be a natural-language search or recommendation rather than another browser round trip. The web interface and MCP are therefore complementary surfaces around the same reviewed catalog, not competing products.

The useful lesson was not that every repeated sentence should become a product. It was that a small, persistent friction can reveal a clearer product boundary once it is observed in real work, shipped narrowly, and revised through use.

Launch demo — planning to verification