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 asfbay-bit/opchain-skills --skill oc-rag-forgegit clone --depth 1 https://github.com/asfbay-bit/opchain-skillsWrote 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/asfbay-bit/opchain-skills/oc-rag-forge)<a href="https://agentmods.dev/skills/asfbay-bit/opchain-skills/oc-rag-forge"><img src="https://agentmods.dev/badge/skills/asfbay-bit/opchain-skills/oc-rag-forge/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/asfbay-bit/opchain-skills/oc-rag-forge"><img src="https://agentmods.dev/badge/skills/asfbay-bit/opchain-skills/oc-rag-forge.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 | $0.00104 | $0.06293 |
| Opus 5 | $0.00052 | $0.03146 |
| Sonnet 5 | $0.00021 | $0.01259 |
| Haiku 4.5 | $0.00010 | $0.00629 |
Grade A, and why
oc-rag-forge 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 6d 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 — 513 lines — stays where its author put it; the contents beside it link to each section on GitHub.
RAG Forge
On first invocation, read references/orchestrator.md and follow its welcome protocol (if present; otherwise fall back to the shared skills/orchestrator.md).
Tri-agent retrieval harness: the Designer picks the retrieval architecture (vector DB, embedding model, chunking strategy, search mode) → the Builder materialises the ingestion + retrieval pipeline and indexes a corpus → the Evaluator scores retrieval quality against a labelled set with isolated context and gates the system on recall/MRR/nDCG/faithfulness thresholds.
RAG is not "embed some docs and hope." Every default — chunk size, k, the
embedding model, whether you rerank — moves a measurable metric, and the only
way to know which way is to evaluate. This skill exists to make retrieval an
evaluated artifact, not a vibe.
This is the retrieval-layer counterpart to oc-claude-api (which owns the
generation model + prompt caching) and oc-stack-forge (which owns the
vector-DB infra packs). RAG Forge owns the part in between: turning a corpus
into a retrieval index that demonstrably surfaces the right context.
/oc-rag — Command Reference
RAG FORGE COMMANDS
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
TRI-AGENT HARNESS
/oc-rag Design a RAG system end-to-end (Designer → Builder → Evaluator)
/oc-rag design Pick vector DB, embedding model, chunking, search mode (Designer)
/oc-rag build Materialise ingest + retrieval pipeline, index corpus (Builder)
/oc-rag eval Score retrieval against a labelled set (Evaluator)
RETRIEVAL DESIGN
/oc-rag chunk Choose / tune a chunking strategy for a corpus
/oc-rag embed Choose / swap the embedding model
/oc-rag hybrid Add BM25 + dense fusion and a reranker
EVALUATION
/oc-rag goldset Build or extend the labelled query→relevant-doc set
/oc-rag bench Benchmark vector-DB / embedding / chunking choices head-to-head
/oc-rag regress Re-run the goldset and gate on metric regression
UTILITIES
/oc-rag inspect Dump retrieved chunks for a query (debug retrieval)
/checkpoint Show checkpoint status
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Type any command to begin. /oc-rag to see this again.
What ships with it
6 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.
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.
- 6d ago Changed 24aa058b7295
- 10d ago First seen · 513 lines · 104 tokens per session scan A 2253f86077ec
oc-rag-forge is a skill published in the GitHub repository asfbay-bit/opchain-skills (0 stars, last pushed 5d ago), licensed Apache-2.0. It adds 104 tokens to every session and 6,293 once invoked, about $0.0005 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.
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azure-search-documents-dotnet
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Select and optimize embedding models for semantic search and RAG applications. Use when choosing embedding models, implementing chunking strategies, or optimizing embedding quality for specific domains.