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 yonatangross/orchestkit --skill rag-retrievalgit clone --depth 1 https://github.com/yonatangross/orchestkitWrote 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/yonatangross/orchestkit/rag-retrieval)<a href="https://agentmods.dev/skills/yonatangross/orchestkit/rag-retrieval"><img src="https://agentmods.dev/badge/skills/yonatangross/orchestkit/rag-retrieval/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/yonatangross/orchestkit/rag-retrieval"><img src="https://agentmods.dev/badge/skills/yonatangross/orchestkit/rag-retrieval.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk warn
- NVIDIA SkillSpector pass
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 | $0.00058 | $0.04397 |
| Opus 5.5 | $0.00023 | $0.01759 |
| Sonnet 5.5 | $0.00012 | $0.00879 |
| Haiku 4.5 | $0.00006 | $0.00440 |
Grade A, and why
rag-retrieval 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 13d 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.
How it starts
The opening of the file, as written. The whole thing — 352 lines — stays where its author put it; the contents beside it link to each section on GitHub.
RAG Retrieval
Comprehensive patterns for building production RAG systems. Each category has individual rule files in rules/ loaded on-demand.
House thresholds, fusion ordering, and the latency and quality budgets we assert live in House delta below. Vendor documentation is linked, not restated (see Upstream coverage).
Quick Reference
| Category | Rules | Impact | When to Use |
|---|---|---|---|
| Core RAG | 4 | CRITICAL | Basic RAG, citations, hybrid search, context management |
| Embeddings | 3 | HIGH | Model selection, chunking, batch/cache optimization |
| Contextual Retrieval | 3 | HIGH | Context-prepending, hybrid BM25+vector, pipeline |
| HyDE | 3 | HIGH | Vocabulary mismatch, hypothetical document generation |
| Agentic RAG | 4 | HIGH | Self-RAG, CRAG, knowledge graphs, adaptive routing |
| Multimodal RAG | 3 | MEDIUM | Image+text retrieval, PDF chunking, cross-modal search |
| Query Decomposition | 3 | MEDIUM | Multi-concept queries, parallel retrieval, RRF fusion |
| Reranking | 3 | MEDIUM | Cross-encoder, LLM scoring, combined signals |
| PGVector | 4 | HIGH | PostgreSQL hybrid search, HNSW indexes, schema design |
Total: 30 rules across 9 categories
Core RAG
Fundamental patterns for retrieval, generation, and pipeline composition.
| Rule | File | Key Pattern |
|---|---|---|
| Basic RAG | rules/core-basic-rag.md |
Retrieve + context + generate with citations |
| Hybrid Search | rules/core-hybrid-search.md |
RRF fusion (k=60) for semantic + keyword |
| Context Management | rules/core-context-management.md |
Token budgeting + sufficiency check |
| Pipeline Composition | rules/core-pipeline-composition.md |
Composable Decompose → HyDE → Retrieve → Rerank |
Embeddings
Embedding models, chunking strategies, and production optimization.
What ships with it
42 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.
- checklists/rag-quality.md 1.9 KB
- checklists/search-implementation-checklist.md 18 KB
- examples/chatbot-with-rag-example.ts 1.8 KB runs code
- metadata.json 570 B
- rules/_sections.md 3.2 KB
- rules/_template.md 339 B
- rules/agentic-adaptive-retrieval.md 2.9 KB
- rules/agentic-corrective-rag.md 2.6 KB
- rules/agentic-knowledge-graph.md 1.9 KB
- rules/agentic-self-rag.md 2.8 KB
- rules/contextual-hybrid.md 3.0 KB
- rules/contextual-pipeline.md 3.7 KB
- rules/contextual-prepend.md 3.6 KB
- rules/core-basic-rag.md 3.0 KB
- rules/core-context-management.md 3.3 KB
- rules/core-hybrid-search.md 2.7 KB
- rules/core-pipeline-composition.md 3.4 KB
- rules/embeddings-advanced.md 3.6 KB
- rules/embeddings-chunking.md 3.1 KB
- rules/embeddings-models.md 3.5 KB
- rules/hyde-fallback.md 2.1 KB
- rules/hyde-generation.md 3.4 KB
- rules/hyde-per-concept.md 2.1 KB
- rules/multimodal-chunking.md 3.3 KB
- rules/multimodal-embeddings.md 3.2 KB
- rules/multimodal-pipeline.md 3.9 KB
- rules/pgvector-hybrid-search.md 4.0 KB
- rules/pgvector-indexing.md 3.5 KB
- rules/pgvector-metadata.md 2.2 KB
- rules/pgvector-schema.md 2.4 KB
- rules/query-decompose.md 3.1 KB
- rules/query-detection.md 2.4 KB
- rules/query-hyde-combo.md 2.7 KB
- rules/reranking-combined.md 3.3 KB
- rules/reranking-cross-encoder.md 2.3 KB
- rules/reranking-llm.md 3.9 KB
- scripts/chunk-repository.py 10 KB runs code
- scripts/rag-pipeline-template.ts 8.8 KB runs code
- scripts/scripts/crag-workflow.py 18 KB runs code
- scripts/scripts/self-rag-graph.py 16 KB runs code
- scripts/search-service.py 5.5 KB runs code
- test-cases.json 15 KB
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.
- 13d ago Changed · -1 lines f462879df6d4
- 21d ago Changed · -5 lines 7d2f20d5aa9a
- 22d ago Changed · -1 lines ef83631f74d7
- 1mo ago Changed 22f7e7342610
- 1mo ago First seen · 359 lines · 58 tokens per session scan A a858fb8a4bc5
rag-retrieval is a skill published in the GitHub repository yonatangross/orchestkit (298 stars, last pushed today), licensed MIT. It adds 58 tokens to every session and 4,397 once invoked, about $0.0002 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 2026-09-05.
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