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 eralpozcan/vision-squeezer --skill vision-statsgit clone --depth 1 https://github.com/eralpozcan/vision-squeezerWrote 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/eralpozcan/vision-squeezer/vision-stats)<a href="https://agentmods.dev/skills/eralpozcan/vision-squeezer/vision-stats"><img src="https://agentmods.dev/badge/skills/eralpozcan/vision-squeezer/vision-stats/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/eralpozcan/vision-squeezer/vision-stats"><img src="https://agentmods.dev/badge/skills/eralpozcan/vision-squeezer/vision-stats.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 | $0.00070 | $0.00485 |
| Opus 5.5 | $0.00028 | $0.00194 |
| Sonnet 5.5 | $0.00014 | $0.00097 |
| Haiku 4.5 | $0.00007 | $0.00049 |
Grade A, and why
vision-stats 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 26d 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.
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.
The source is not reproduced here
A licence we could not identify
The repository carries a LICENSE file, but it is custom or dual enough that GitHub cannot name it and neither can this catalogue. Unknown terms are not permission, so the body is not copied here. Read the licence at the source and decide for yourself.
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.
- 26d ago Changed · +1 lines 0b6ba395f096
- 1mo ago First seen · 41 lines · 70 tokens per session scan A 944fde9b4d0e
vision-stats is a skill published in the GitHub repository eralpozcan/vision-squeezer (2 stars, last pushed 5d ago), with no licence file. It adds 70 tokens to every session and 485 once invoked, about $0.0003 per session on Opus 5.5. 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
free-llm-apis
Guide users through obtaining and configuring free API keys for LLM providers. Use when the user wants to set up a free LLM API, get a free API key, connect to a free model provider, configure an OpenAI-compatible endpoint at no cost, or asks about free tiers for AI models. Triggers on "free API key", "free LLM", "set…
ocx
Operate opencodex (ocx): discover CLI tasks offline, inspect local configuration, and manage a running proxy’s account pool, providers, models, routing, usage report, and management API. Use for proxy operation rather than editing the opencodex codebase.
rubyllm
Build and maintain Ruby or Rails applications with the RubyLLM AI framework. Use for chats, agents, tools, MCP servers, structured output, judgments, evaluations, media generation, transcription, OCR, moderation, embeddings, reranking, tracing, Rails integration, and RubyLLM upgrades; not for contributing to the…
dividend-analysis
Comprehensive capital allocation analysis: dividends, buybacks, M&A, debt management, and FCF deployment.
financial-report-analyst
Analyze company financial reports (10-K, 10-Q, annual reports) and extract actionable investment insights — statement quality, red flags, and a signal. For a structured section-by-section summary document of a 10-K rather than an investment call, use 10k-digest.
report-generator
Generate professional HTML/PDF investment reports with interactive visualizations.