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/podbay-cloud/podbay/rag-qualitynpx skills add Podbay-Cloud/podbay --skill rag-qualitygit clone --depth 1 https://github.com/Podbay-Cloud/podbayWrote 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/podbay-cloud/podbay/rag-quality)<a href="https://agentmods.dev/skills/podbay-cloud/podbay/rag-quality"><img src="https://agentmods.dev/badge/skills/podbay-cloud/podbay/rag-quality.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 | $0.00084 | $0.00551 |
| Opus 5 | $0.00042 | $0.00275 |
| Sonnet 5 | $0.00017 | $0.00110 |
| Haiku 4.5 | $0.00008 | $0.00055 |
Grade A, and why
rag-quality 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 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.
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 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.
- 5d ago First seen · 47 lines · 84 tokens per session scan A 7850fa4d3035
rag-quality is a skill published in the GitHub repository Podbay-Cloud/podbay (6 stars, last pushed 4d ago), with no licence file. It adds 84 tokens to every session and 551 once invoked, about $0.0004 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
jetson-inference-mem-tune
Pick the serving stack and per-runtime memory flags (vLLM, SGLang, llama.cpp, TensorRT Edge-LLM) for an LLM/VLM workload on any NVIDIA Jetson.
kg-builder
Designs and builds knowledge graphs from documents — ontology modeling with domain/range constraints, entity/relation/event extraction, entity resolution, provenance and supersession, and GraphRAG serving. Use when asked to "build a knowledge graph", "design an ontology", "extract entities and relations", "deduplicate…
rag-auditor
Evaluates RAG pipeline quality across retrieval (precision, recall, MRR) and generation (groundedness, hallucination rate). Triggers on: "audit RAG pipeline", "RAG quality", "hallucination detection", "why is RAG failing", "grounding check". NOT for general architecture audits, use architecture-reviewer.
rag-implementation
Comprehensive guide to implementing RAG systems including vector database selection, chunking strategies, embedding models, and retrieval optimization. Use when building RAG systems, implementing semantic search, optimizing retrieval quality, or debugging RAG performance issues.
internal-rag
Mandatory persistent project memory for substantial coding tasks. Use at task start, recovery, milestones, failures, before risky operations, before compaction, and before finishing. v1.0.1 adds type filtering, type-priority scoring, query expansion, grouped context output, and promote workflow.
dify-rag-pm
Analyze, plan, design, and review Dify Dataset, Knowledge, and RAG product work. Use for Dataset lifecycle, Workflow Knowledge Retrieval nodes, field contracts, user stories, and KnowledgeFS-to-Dify product translation.