本文へ移動
cccskills
無料GitHub で公開

github-readme-visuals

Create or refresh original product hero images, interface screenshots, and image-led README sections for GitHub repositories. Use when a user asks to make a GitHub README show the product visually, add screenshots, design a README hero image, or prepare repository-bound visual documentation.

インストール方法を見る

含まれるファイル(3)

  • SKILL.md8.8 KB
  • agents/openai.yaml273 B
  • references/visual-checklist.md4.5 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。

GitHub README Visuals

Create a fast, trustworthy visual explanation of the product without replacing its factual README content.

Workflow

  1. Inspect the repository before designing.

    • Read the README, product notes, local instructions, existing assets, and available screenshots.
    • For application repositories, prioritize real product screenshots as proof. Do not present generated UI as a real screen.
    • Identify the user journey a first-time visitor should understand in one glance.
    • Build a visual-claim ledger before writing hero copy: every product, pricing, privacy, credential, provider, and deployment claim must be supported by the README, source, or user-confirmed facts. Omit ambiguous claims instead of guessing.
  2. Establish visual direction.

    • When Creative Production is available, use its style intake before generating a new direction. Use moodboard-explorer when the user asks for multiple visual territories or concept options.
    • If the user does not choose a direction, use a restrained default: one original hero, product-led composition, readable hierarchy, and a palette derived from the repository's existing UI or brand assets.
    • Directly inspect every supplied reference before production. Record its canvas ratio, major spatial split, dominant visual, screenshot count, overlap, rotation, off-canvas clipping, shadows, typography blocks, and decorative motifs.
    • When the user explicitly requests a layout or style match, preserve the reference's composition grammar and visual hierarchy while replacing its UI, logo, copy, branding, and assets with material from the target repository.
    • Measure repeated screenshot frames instead of estimating them independently. If the reference uses equal-size frames, define one canonical width and aspect ratio for every screenshot; create hierarchy through position, overlap, off-canvas clipping, rotation, contrast, and z-order rather than arbitrary scaling.
    • When the brief says one-to-one, exact, or fully replicated, create a pixel geometry table before editing. Record canvas size; each screenshot's four corners, frame width and height, rotation, off-canvas clipping, and z-order; distances between adjacent frames; and each decorative shape's orientation and anchor point.
    • Derive CSS positions from measured corners and rotation. Do not substitute a visually similar arrangement or independently eyeball each frame.
    • Do not start final production from a moodboard or verbal summary alone when an exact reference image is available.
  3. Produce assets.

    • Use deterministic composition for a software hero whose reference is built from real interface screenshots. Image generation may add a non-product polish layer, but it must not invent, redraw, or replace the product UI.
    • Use real captured screenshots for implementation, settings, privacy, or workflow proof. Review each one for secrets, personal data, and legibility.
    • Make the repository's real screenshots the dominant visual whenever the reference does. A screenshot collage may use four to eight captures when that count is part of the requested composition.
    • Use the application's first/default screen as the dominant screenshot unless the user names another primary workflow. Arrange secondary screenshots by user importance, not filename order.
    • Do not repeat the same page, near-identical state, or duplicated crop inside one collage. Each screenshot must prove a distinct interface or capability.
    • Keep decorative shapes outside screenshot bounds and outside the logo clear space. Decorations must never look like product UI, status indicators, annotations, or screenshot content.
    • Use the original transparent application logo directly on the composition background unless an existing brand system explicitly defines a container. Do not add a dark tile, badge, or frame by default.
    • When the repository has multiple README languages, generate one localized hero per README from a shared layout source. Keep geometry, screenshot order, logo treatment, and visual hierarchy identical; localize only verified copy and typography adjustments.
    • Match the language inside product screenshots to the destination README whenever the application supports that locale. A localized headline over screenshots from another language does not count as a localized hero.
    • Use stable language-qualified filenames such as product-hero-zh.png and product-hero-en.png, and point each README only to its matching language asset.
    • Preserve the complete product window. Never use object-fit: cover, clipping, masks, or manual crops merely to force a screenshot into the reference frame ratio.
    • When source and reference aspect ratios differ, recapture the real application at the target ratio. If recapture is impossible, use proportional contain scaling and disclose the resulting empty space; do not silently remove interface content.
    • Reproduce decorative orientation explicitly. For triangles, arrows, slashes, and asymmetric shapes, record the pointing direction or vertex coordinates; matching only color and approximate location is insufficient.
    • Save project-bound final assets in a tracked repository directory, normally docs/images/. Do not leave README assets only in a generated-images or temporary directory.
  4. Update README files additively.

    • Keep every existing line unless the user explicitly authorizes removal or rewriting.
    • Place the hero directly after the primary installation or download call to action.
    • Place an interface-preview section after the first workflow explanation, using only the smallest set of screenshots needed to explain the product.
    • Keep language variants factually aligned while writing naturally in each language.
    • Keep localized hero claims semantically equivalent. Do not introduce a capability, provider, pricing, credential, or privacy claim in only one language.
    • Use descriptive alt text and repository-relative image paths.
  5. Verify before delivery.

    • Read the visual checklist and complete every applicable item.
    • Inspect the final hero and screenshots at display size. Confirm that the hero conveys the product category and the screenshots remain readable.
    • Compare the final hero against the reference on five structural checks: left/right allocation, dominant screenshot position, screenshot count, overlap/rotation pattern, and text-block hierarchy.
    • Run a duplicate-and-intrusion review: confirm every screenshot is unique, the main screenshot is the intended first screen, and no decoration overlaps a screenshot or logo.
    • Re-check every visible factual claim against the visual-claim ledger. Remove any unsupported API-key, pricing, privacy, offline, provider, or product-capability statement.
    • Inspect every exported language variant independently. A successful render of one locale does not validate another; check for missing images, black render blocks, overflow, unexpected wrapping, and layout drift.
    • Verify both localization layers for every variant: composition copy and in-product screenshot UI. Navigation, headings, tabs, buttons, and settings labels should use the destination README language.
    • For equal-frame references, verify every screenshot has the same rendered width and aspect ratio in the composition source.
    • Confirm every screenshot preserves the complete captured window: title bar, navigation, primary content bounds, and visible window edges must not be removed by the composition.
    • For one-to-one work, render at the reference's exact pixel dimensions and compare reference and output with a 50% opacity overlay or edge overlay. Verify screenshot corner positions, inter-frame gaps, rotation, z-order, canvas-edge clipping, and asymmetric decoration direction before approval.
    • Use a default geometry tolerance of 8 px per measured corner and 0.5 degrees per rotation unless the user specifies a stricter tolerance. Any intentional deviation must be documented and user-driven.
    • Report which images are original generated artwork and which are real product captures.

Decision Rules

  • Use a single hero image when the README needs immediate visual identity.
  • Use two or three screenshots by default, but follow the reference when a screenshot collage is an explicit part of the requested design.
  • Use an abstract generated hero when exact UI would be risky or inaccurate; pair it with real screenshots below.
  • Stop and ask for direction only if an unverified visual claim, brand rule, or missing source asset would materially change the result.

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

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

Create a changeset file in .changeset/ that describes a user-facing change for the release notes

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

Add tree-sitter language support to codegraph end-to-end — wire the grammar + extractor, write tests, then benchmark extraction quality and retrieval value on 3 popular real-world repos. Use when the user runs /add-lang <language> or asks to add/support a new language (e.g. Lua, Elixir, Zig, OCaml) in codegraph.

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

EthanYoQ のスキルをすべて見る

このスキルの問題を報告する