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.
npx skills add bestdeejay-design/agent-skills --skill repo-social-previewgit clone --depth 1 https://github.com/bestdeejay-design/agent-skillsWrote 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.
[](https://agentmods.dev/skills/bestdeejay-design/agent-skills/repo-social-preview)<a href="https://agentmods.dev/skills/bestdeejay-design/agent-skills/repo-social-preview"><img src="https://agentmods.dev/badge/skills/bestdeejay-design/agent-skills/repo-social-preview/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.
<a href="https://agentmods.dev/skills/bestdeejay-design/agent-skills/repo-social-preview"><img src="https://agentmods.dev/badge/skills/bestdeejay-design/agent-skills/repo-social-preview.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00105 | $0.01181 |
| Opus 5 | $0.00053 | $0.00590 |
| Sonnet 5 | $0.00021 | $0.00236 |
| Haiku 4.5 | $0.00011 | $0.00118 |
Grade A, and why
repo-social-preview 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 today.
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.
How it starts
The opening of the file, as written. The whole thing — 87 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Repo Social Preview — og:image as a typeset hero section
The social preview is a piece of web design, not a generated raster. Lay out a clean hero section in HTML/CSS, render it with headless Chrome, upload the PNG.
Hard rules
- Typeset HTML — never compose pixels. No Pillow drawing, no self-made SVG
shapes, no site screenshots. The deliverable is a standalone
hero.html. Existing assets from the site (logo URL, brand fonts) may be referenced; do not draw new ones. - Hero only. No header, nav, footer, badges, cookie bars. One full-bleed 1280×640 hero section: headline, optional subline/CTA, brand background.
- 40pt padding on all sides (
padding: 40pt). This is the GitHub crop-safe hint: essential text and logos must stay inside it — share previews crop edges. - Site is the source of truth. If the repo has a website with a hero section, re-typeset that hero: its headline, subline, palette, typeface. Do not invent a different design; do not embed an iframe or screenshot of it.
- Front-end quality bar. Build the markup per the
frontendskill: real typography scale, brand tokens as CSS variables, no layout hacks.
Workflow
- Collect source material
- Website exists → fetch it, extract the hero: headline, subline, CTA text, colors, fonts, logo URL.
- No website → build from README/description: repo name + one-line value prop.
- Lay out
hero.html(standalone file, fixed 1280×640 viewport):
Solid background preferred over transparency; hard color stops are fine, keep text contrast ≥ WCAG AA.<!doctype html> <html><head><meta charset="utf-8"> <link rel="stylesheet" href="https://fonts.googleapis.com/css2?family=Inter:wght@400;600;800"> <style> :root { --brand-a:#F64A8A; --brand-b:#0ABAB5; --ink:#1A1A1A; } * { margin:0 } body { width:1280px; height:640px; overflow:hidden } .hero { width:100%; height:100%; box-sizing:border-box; padding:40pt; display:flex; flex-direction:column; justify-content:center; gap:16pt; background:linear-gradient(90deg, var(--brand-a) 50%, var(--brand-b) 50%); } </style></head> <body><section class="hero">…</section></body></html> - Render to PNG:
Headless Chrome renders real web layout pixel-perfectly (webfonts included).python3 scripts/render_social_preview.py hero.html --out og.png - Verify: exactly 1280×640, < 1 MB, nothing clipped at the edges, text readable at thumbnail size. Re-render if any check fails.
- Upload manually: Settings → Social preview → Edit → Upload (no API).
What ships with it
4 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.
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.
- today Changed · +8 lines 4ee6cdfc446b
- 12d ago First seen · 79 lines · 105 tokens per session scan A 489328a889b0
repo-social-preview is a skill published in the GitHub repository bestdeejay-design/agent-skills (6 stars, last pushed yesterday), licensed MIT. It adds 105 tokens to every session and 1,181 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 2026-08-31.
Other skills, from other repositories
workflow
Use when a task is too large for turn-by-turn orchestration and should run through the big-task workflow lane: system-wide changes, large migrations, repo-wide audits, high-confidence verification, or tasks explicitly asking to run a workflow. Claude Code uses native dynamic workflows; Codex, OpenCode, and Grok use…
skill-compiler
Automatic solved-to-skill compiler — detects novel task completions and autonomously drafts new SKILL.md files. Stolen from Hermes Agent's learning loop (NousResearch, 2026-05-11).
context-compactor
9-section context compression with analysis scratchpad. Adapted from Claude Code's /compact system (2026-03-31).
daemon-loop
Autonomous recurring agent tasks — converts workflows into persistent background daemons that run on intervals. Stolen from Boris Cherny's Claude Code /loop pattern (2026-03-31).
trade-journal-analyzer
Unified post-trade analytics: journal pattern extraction + drawdown classification. Absorbs: drawdown-classifier.
Deep Research Loop
Multi-step web research, compilation, and synthesis workflow. Scrapes multiple sources, cross-references claims, and produces a structured research brief.