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 agentmods add skills/lovstudio/skills/find-logonpx skills add lovstudio/skills --skill find-logogit clone --depth 1 https://github.com/lovstudio/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/lovstudio/skills/find-logo)<a href="https://agentmods.dev/skills/lovstudio/skills/find-logo"><img src="https://agentmods.dev/badge/skills/lovstudio/skills/find-logo.svg" alt="Measured on agentmods" 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 | $0.00099 | $0.01676 |
| Opus 5 | $0.00049 | $0.00838 |
| Sonnet 5 | $0.00020 | $0.00335 |
| Haiku 4.5 | $0.00010 | $0.00168 |
Grade A, and why
lov-find-logo scanned grade A with 1 finding 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 5d ago.
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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
Stdlib only (urllib, html.parser, argparse). No `pip install` required. How it starts
The opening of the file, as written. The whole thing — 149 lines — stays where its author put it; the contents beside it link to each section on GitHub.
find-logo — collect brand logos, prefer wide + transparent
Takes a brand name or URL, probes Clearbit + the site's own og:image /
<link rel=icon> / favicon, scores each candidate, and archives the best
one plus a couple of alternates into the configured collection directory.
When to Use
- User asks to collect one or more brand logos for a slide/poster/site lineup
- User names companies to drop into a partners/press strip
- User gives a URL and wants its logo pulled down cleanly
Workflow (MANDATORY)
Step 1: Identify each brand
Accept any mix of names and URLs. If the user gave only a name with no obvious
domain, ask — don't silently guess .com (script will guess, but for non-US or
ambiguous brands that fails).
Use AskUserQuestion when:
- Brand name is ambiguous (e.g. "Apple" = fruit vs. Inc.)
- No URL and the domain isn't guessable (
xAI→x.ai, notxai.com) - User gave a list without URLs
Step 2: Fetch — one brand per invocation
python3 scripts/find_logo.py --name "Anthropic" --url https://anthropic.com --json
For a batch, loop; the script is idempotent per <slug>/ (re-runs overwrite).
Step 3: Inspect score; fall back to WebSearch if needed
- Exit code
0→ logo archived. The printedscoreis your quality signal:≥ 60— solid: SVG or transparent PNG with wide/square aspect20–60— usable: probably a favicon or small PNG< 20— weak: only ICO or tiny stub found
- Exit code
2/status: "no-candidates"→ script found nothing. Do NOT give up. UseWebSearchfor"<brand> logo svg site:*.com"or the brand's press-kit page, then re-invoke with--url <direct-image-url>is not supported — if you have a direct image URL, save it into the configured collection directory under<slug>/logo.<ext>and hand-writemeta.jsonusing the existing layout as a template.
Step 4: Report
Report back with the archive path and the primary's aspect + format. If the score is weak, tell the user and offer to retry with a specific press-kit URL or Wikipedia SVG.
What ships with it
5 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.
- 5d ago First seen · 149 lines · 99 tokens per session scan A 8b828c934d66
lov-find-logo is a skill published in the GitHub repository lovstudio/skills (64 stars, last pushed yesterday), licensed MIT. It adds 99 tokens to every session and 1,676 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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