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prototype

Build a throwaway prototype to answer a design question. Use when the user wants to sanity-check whether a state model or logic feels right, or explore what a UI should look like.

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  • SKILL.md2.9 KB
  • agents/openai.yaml143 B
  • LOGIC.md6.0 KB
  • UI.md6.8 KB

SKILL.md(原文)

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Prototype

A prototype is throwaway code that answers a question. The question decides the shape.

Pick a branch

Identify which question is being answered — from the user's prompt, the surrounding code, or by asking if the user is around:

  • "Does this logic / state model feel right?" → LOGIC.md. Build a single shareable HTML file — free-play buttons plus tabbed guided walkthroughs — that pushes the state machine through cases that are hard to reason about on paper, and that a non-developer can drive.
  • "What should this look like?" → UI.md. Generate several radically different UI variations on a single route, switchable via a URL search param and a floating bottom bar.

The two branches produce very different artifacts — getting this wrong wastes the whole prototype. If the question is genuinely ambiguous and the user isn't reachable, default to whichever branch better matches the surrounding code (a backend module → logic; a page or component → UI) and state the assumption at the top of the prototype.

Rules that apply to both

  1. Throwaway from day one, and clearly marked as such. Locate the prototype code close to where it will actually be used (next to the module or page it's prototyping for) so context is obvious — but name it so a casual reader can see it's a prototype, not production. For throwaway UI routes, obey whatever routing convention the project already uses; don't invent a new top-level structure.
  2. Trivial to run. A UI prototype starts from one command in the project's task runner — pnpm <name>, python <path>, bun <path>, etc. A logic demo is a single HTML file the user double-clicks. Either way, no thinking required to start it.
  3. No persistence by default. State lives in memory. Persistence is the thing the prototype is checking, not something it should depend on. If the question explicitly involves a database, hit a scratch DB or a local file with a clear "PROTOTYPE — wipe me" name.
  4. Skip the polish. No tests, no error handling beyond what makes the prototype runnable, no abstractions. The point is to learn something fast.
  5. Surface the state. After every action (logic) or on every variant switch (UI), print or render the full relevant state so the user can see what changed.
  6. Capture it when done. Fold any validated decision into the real code, then capture the prototype itself as a primary source: commit it to a throwaway branch, out of main, and leave a context pointer to that branch on the implementation issue. Capture the answer too — the verdict and the question it settled — in the issue or a commit. The main branch keeps only the validated decision.

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。

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概要と使いどころ

使用 AACT (Aggregate Analysis of ClinicalTrials.gov) PostgreSQL 数据仓库进行批量、历史、聚合性临床试验数据挖掘。Use this skill when the user requests bulk SQL analysis over the full clinical trials data warehouse — historical trial trends, disease landscapes, similar-design matching, or multi-year aggregations across hundreds of thousands of NCT records. 触发场景包括:AACT 查询、临床试验批量分析、PostgreSQL 试验数据、全量 NCT 检索、试验数据挖掘、历史试验分析、clinical trials data warehouse、SQL trials、bulk trial analysis、disease landscape、试验设计相似性匹配、跨年度聚合、sponsor/phase/country 多维统计。**与 clinical-trials-v2 差异**:本 skill 走批量 SQL · 离线大数据(PostgreSQL);v2 走实时 API · 单查询。两者互补:单条 NCT 实时状态用 v2,百万级历史挖掘用本 skill。支持云端公共 PostgreSQL(aact-db.ctti-clinicaltrials.org · 零部署)和每日 dump 本地还原(高性能 · 离线)两种连接方式,自动检测优先用本地。跨平台(macOS/Linux/Windows)参数化 SQL 防注入,read-only 强制保护。

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。

EthanYoQ/Skill-hub112026年10月5日 更新

使用 AACT (Aggregate Analysis of ClinicalTrials.gov) PostgreSQL 数据库进行大批量临床试验历史分析与数据挖掘。触发场景包括:AACT 查询、临床试验批量分析、PostgreSQL 试验数据、全量 NCT 检索、试验数据挖掘、clinical trials data warehouse、疾病领域全景分析、设计相似试验匹配、跨年度试验趋势聚合。本 skill 通过 SQL 接口处理百万级试验记录,支持云端公共 PostgreSQL 服务(aact-db.ctti-clinicaltrials.org)和每日 dump 本地还原两种连接方式,自动检测并优先使用本地高性能模式。

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。

EthanYoQ/Skill-hub112026年10月5日 更新

Use when work produces a deliverable, factual claim, dataset, code change, decision, publication, deployment, automation, or irreversible action whose failure would matter; when the user asks for a quality gate, acceptance criteria, QA, completeness, validation, audit, evidence, preflight, release readiness, or a definition of done; or before claiming completion on medium- or high-risk work. Automatically decide whether a formal gate is warranted, derive task-specific pass/fail criteria, gather evidence, and block unsupported completion. Make sure to use this skill even when the user does not say "quality gate" if consequential work needs acceptance criteria or completion evidence. Skip formal gating for trivial, reversible, low-impact requests.

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。

EthanYoQ/Skill-hub112026年10月5日 更新

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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。

EthanYoQ/Skill-hub112026年10月5日 更新

Add a new competitor to the AI Visibility Tool Directory — researches the tool, generates data, takes a screenshot, and inserts into the codebase

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。

EthanYoQ/Skill-hub112026年10月5日 更新

add-lang

無料

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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。

EthanYoQ/Skill-hub112026年10月5日 更新

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