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 elitongadotti/cockpit --skill pycegerb-yodagit clone --depth 1 https://github.com/elitongadotti/cockpitWrote 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/elitongadotti/cockpit/pycegerb-yoda)<a href="https://agentmods.dev/skills/elitongadotti/cockpit/pycegerb-yoda"><img src="https://agentmods.dev/badge/skills/elitongadotti/cockpit/pycegerb-yoda/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/elitongadotti/cockpit/pycegerb-yoda"><img src="https://agentmods.dev/badge/skills/elitongadotti/cockpit/pycegerb-yoda.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 | $0.00035 | $0.02841 |
| Opus 5 | $0.00017 | $0.01421 |
| Sonnet 5 | $0.00007 | $0.00568 |
| Haiku 4.5 | $0.00003 | $0.00284 |
Grade A, and why
pycegerb-yoda 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 11d 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.
- 11d ago First seen · 222 lines · 35 tokens per session scan A 9d4c261b9fa0
pycegerb-yoda is a skill published in the GitHub repository elitongadotti/cockpit (2 stars, last pushed 2d ago), with no licence file. It adds 35 tokens to every session and 2,841 once invoked, about $0.0002 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
pgvector-semantic-search
Use this skill for setting up vector similarity search with pgvector for AI/ML embeddings, RAG applications, or semantic search. Trigger when user asks to: Store or search vector embeddings in PostgreSQL Set up semantic search, similarity search, or nearest neighbor search Create HNSW or IVFFlat indexes for vectors…
postgres-hybrid-text-search
Use this skill to implement hybrid search combining BM25 keyword search with semantic vector search using Reciprocal Rank Fusion (RRF). Trigger when user asks to: Combine keyword and semantic search Implement hybrid search or multi-modal retrieval Use BM25/pgtextsearch with pgvector together Implement RRF (Reciprocal…
prompt-sensei
Stage-aware prompt coaching, prompt improvement, lookback analysis, prompting habit feedback, and local reports about prompt quality for AI coding agents such as Claude Code or Codex.
llama-cpp
Guide for llama.cpp, the C/C++ LLM inference framework by ggml-org. Covers the C API (llama.h), GGUF format, quantization (Q4KM, Q80, IQ4XS), CMake builds, GPU backends (CUDA, Vulkan, Metal, ROCm), HTTP server with OpenAI-compatible API, embeddings, grammar constraints, function calling, LoRA, speculative decoding…
meta-muse-video-analysis
Analyze local video files with the fixed Meta Model API model Muse Spark 1.2 Contributor and a user-defined prompt. Use when Codex or Claude Code is asked to inspect, summarize, transcribe, timestamp, inventory, review, or extract information from a video and the METAMUSEKEY Windows environment variable is available.
data-integrity-post-normalize
Apply a composable pipeline of normalization operations to a string. Trim whitespace, fix encoding, normalize unicode, strip HTML, and more — in a single call.