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 Lu1sDV/skillsmd --skill jevgit clone --depth 1 https://github.com/Lu1sDV/skillsmdWrote 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/lu1sdv/skillsmd/jev)<a href="https://agentmods.dev/skills/lu1sdv/skillsmd/jev"><img src="https://agentmods.dev/badge/skills/lu1sdv/skillsmd/jev/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/lu1sdv/skillsmd/jev"><img src="https://agentmods.dev/badge/skills/lu1sdv/skillsmd/jev.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 | $0.00098 | $0.05665 |
| Opus 5.5 | $0.00039 | $0.02266 |
| Sonnet 5.5 | $0.00020 | $0.01133 |
| Haiku 4.5 | $0.00010 | $0.00566 |
Grade C, and why
jev scanned grade C with 2 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 24d 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.
Downloads and executes remote codehighSupply chain
curl | sh runs whatever the server returns today, which is not necessarily what it returned when this was reviewed.
curl -s ... | python3 routing.py Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
curl -s https://api.typesafe.ai/v1/systemone \ The source is not reproduced here
No licence file
A repository with no LICENSE is all rights reserved by default, so the body is not copied here. The metadata, the measurements and the link are.
What ships with it
1 file 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.
- 24d ago First seen · 512 lines · 98 tokens per session scan C 9f4252179541
jev is a skill published in the GitHub repository Lu1sDV/skillsmd (2 stars, last pushed 4d ago), with no licence file. It adds 98 tokens to every session and 5,665 once invoked, about $0.0004 per session on Opus 5.5. A static security scan graded it C with 2 findings (downloads and executes remote code, makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-23.
Other skills, from other repositories
baoyu-imagine
AI image generation with OpenAI GPT Image 2, Azure OpenAI, Google, OpenRouter, DashScope, Z.AI GLM-Image, MiniMax, Jimeng, Seedream and Replicate APIs. Supports text-to-image, reference images, aspect ratios, and batch generation from saved prompt files. Sequential by default; use batch parallel generation when the…
model-routing-patterns
Capability-aware model routing for Codex, Copilot, Claude and provider APIs. Triggers: model routing, model selection, reasoning effort, approved fallback, cost, escalation.
rag-patterns
RAG: embeddings, chunking, hybrid search (BM25+vector), reranking, CRAG, multi-hop. Triggers: RAG, embedding, pgvector, Qdrant, Pinecone, Weaviate, reranker, semantic search.
evaluate
Evaluates RAG retrieval and LLM-as-judge metrics (faithfulness, relevancy, context precision). Triggers: measure RAG quality, knowledge gap, RAG eval, golden dataset.
json-mode-patterns
Structured JSON output from Claude: native JSON schemas, strict tools, local validation, refusal and truncation handling. Triggers: JSON mode, structured output, schema validation, JSON parsing.
ai-product
Every product will be AI-powered. The question is whether you'll build it right or ship a demo that falls apart in production.