1nfinix-editorial-card-screenshot

1nfinix-editorial-card-screenshot is a skill for Codex from endearqb/endearqb-skills. It costs 105 tokens per session (2,698 once invoked), scanned A, original, MIT.

A skill for turning source text into editorial-style information cards and saving them as PNG images. It supports several common image shapes, such as square, portrait, landscape, and widescreen.

In plain words
What is it for?
Use it to create research cards, briefing cards, cover cards, information cards, or social-media graphics from supplied text.
Why use it?
It helps present research or briefing content in a designed, readable layout instead of leaving it as plain text. It also addresses problems such as clipped text and unused space.

Skill for Codex

Written for Codex: agents/openai.yaml present.

Good fit Use it to create research cards, briefing cards, cover cards, information cards, or social-media graphics from supplied text.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/endearqb/endearqb-skills/1nfinix-editorial-card-screenshot
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add endearqb/endearqb-skills --skill 1nfinix-editorial-card-screenshot
Clone the repo
git clone --depth 1 https://github.com/endearqb/endearqb-skills

Made for: Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for 1nfinix-editorial-card-screenshot

README.md
[![agentmods](https://agentmods.dev/badge/skills/endearqb/endearqb-skills/1nfinix-editorial-card-screenshot/github.svg)](https://agentmods.dev/skills/endearqb/endearqb-skills/1nfinix-editorial-card-screenshot)
Your own site
<a href="https://agentmods.dev/skills/endearqb/endearqb-skills/1nfinix-editorial-card-screenshot"><img src="https://agentmods.dev/badge/skills/endearqb/endearqb-skills/1nfinix-editorial-card-screenshot/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for 1nfinix-editorial-card-screenshot

Your own site · 80×15
<a href="https://agentmods.dev/skills/endearqb/endearqb-skills/1nfinix-editorial-card-screenshot"><img src="https://agentmods.dev/badge/skills/endearqb/endearqb-skills/1nfinix-editorial-card-screenshot.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 105 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,698 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00105 $0.02698
Opus 5 $0.00053 $0.01349
Sonnet 5 $0.00021 $0.00540
Haiku 4.5 $0.00011 $0.00270

Measured 12d ago against content hash 817433ff0a36, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

1nfinix-editorial-card-screenshot scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 12d ago.

The scan reads SKILL.md. This mod also ships 1 executable file (scripts/capture_card.sh), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

skills/1nfinix-editorial-card-screenshot/SKILL.md · 219 lines

How it starts

The opening of the file, as written. The whole thing — 219 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Editorial Card Screenshot

Overview

Turn source text into an editorial-style HTML information card, then capture it as a PNG in a supported ratio. The priority is editorial hierarchy plus stable rendering: no clipped Chinese text, no accidental cropping, and no large dead zones in the lower half.

Default deliverables unless the user asks otherwise:

  1. One complete HTML file with embedded CSS.
  2. One PNG screenshot rendered from that HTML.

Workflow

1. Analyze Content Density

Choose layout strategy from the content itself:

  • Use "big-character" composition when content is sparse and a single phrase, number, or hook can carry the page.
  • Use a two-column or three-column editorial grid when content is dense and needs stronger hierarchy.
  • Use oversized numbers, heavy rules, tinted blocks, and pull-quote scale to avoid dead space.
  • Do not force dense content into evenly weighted tiles. Build primary blocks, secondary blocks, and lighter supporting blocks.
  • Match structure to content type:
    • Ranking / recommendation content: allow asymmetric hero + structured list.
    • Tutorial / analysis / interpretation content: group into overview, core judgment, interpretation, boundary, and conclusion.

Before compressing content, first change the layout skeleton.

  • Ratio changes should primarily change reading path, hierarchy, and module arrangement.
  • Do not treat ratio changes as a reason to delete content by default.
  • Only compress, group, or summarize when the current ratio cannot hold the content clearly after layout has already been restructured.

2. Apply the Editorial Defaults

Use these defaults unless the user overrides them:

  • Import Google Fonts:
    <link href="https://fonts.googleapis.com/css2?family=Noto+Serif+SC:wght@700;900&family=Noto+Sans+SC:wght@400;500;700&family=Oswald:wght@500;700&family=Inter:wght@400;600;700&display=swap" rel="stylesheet">
    
  • For Chinese text, always declare local CJK fallbacks before generic serif/sans-serif fallbacks. Prefer:
    font-family: "Noto Sans SC", "Noto Sans CJK SC", "Source Han Sans SC", "Microsoft YaHei", "PingFang SC", sans-serif;
    font-family: "Noto Serif SC", "Noto Serif CJK SC", "Source Han Serif SC", "Songti SC", "STSong", serif;
    
  • Keep body text around 18px to 20px on a 900px-wide composition.
  • Keep meta/tag text at 13px minimum.
  • Use compact spacing: outer padding 40px to 50px, module gaps 30px to 40px, line-height 1.5 to 1.6.
  • Add visual anchors with 4px to 6px accent rules, subtle gray planes, and a light noise overlay.
  • Favor warm-paper backgrounds such as #f5f3ed unless the user specifies another palette.
  • For Chinese-heavy cards, shorten dense column line lengths and always add overflow-wrap / word-break guards.
  • If the user provides a title, use it as the main headline by default. Expand the layout before rewriting or shortening it.
  • Avoid equal-weight boxes on dense cards. At least one module should feel visually heavier than the others.
  • Do not hard-code signatures, creator handles, or personal branding unless requested.

Read the full file on GitHub · 219 lines

Files

What ships with it

6 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 12d ago First seen · 219 lines · 105 tokens per session scan A 817433ff0a36

Subscribe to this mod's changes

1nfinix-editorial-card-screenshot is a skill published in the GitHub repository endearqb/endearqb-skills (19 stars, last pushed 9d ago), licensed MIT. It adds 105 tokens to every session and 2,698 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

Related

Other skills, from other repositories

taiyi-evolve

A workflow skill that compares the implemented code with the frozen design after development and testing. It records architecture changes and proposes updates to DESIGN.md, the document describing the intended system structure.

Dong90/oh-my-taiyiforge · 37 tokens

reply-to-pr-threads

Draft, confirm, and post replies to GitHub PR review threads. Handles per-category reply formatting, re-fetches thread resolution state so auto-resolved threads are skipped, and posts via GraphQL. Use when the user asks to "reply to PR threads", "post PR thread replies", or "draft PR reply messages".

tobihagemann/turbo · 71 tokens

answer-reviewer-questions

For each reviewer question on a PR, recall implementation reasoning and compose a raw answer. Use when the user asks to "answer reviewer questions", "draft answers to PR questions", or "explain reviewer questions".

tobihagemann/turbo · 48 tokens

evaluate-findings

Critically assess external feedback (code reviews, AI reviewers, PR comments) and decide which suggestions to apply using adversarial verification. Use when the user asks to "evaluate findings", "assess review comments", "triage review feedback", "evaluate review output", or "filter false positives".

tobihagemann/turbo · 63 tokens

create-project-skills

Scans an existing codebase and generates project-specific skills that capture inferred conventions such as naming, file organization, framework usage, data access, error handling, and testing style. Writes into the project's chosen skill directory (e.g., .claude/skills/, .agents/skills/, or a custom path). Use when…

tobihagemann/turbo · 111 tokens

retrospective-audit

Stage B of /prflow:retrospective-weekly: given a most-recent-first subset of one recurring pattern's occurrence-PR context bundles (bounded by auditbundlecap), re-derive the root cause and return one JSON object carrying a ranked findings array (one to three sub-patterns) — no edits, no worktree. Invoked as a subagent…

The01Geek/prflow · 91 tokens