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 jpoutrin/product-forge --skill rag-cag-securitygit clone --depth 1 https://github.com/jpoutrin/product-forgeWrote 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/jpoutrin/product-forge/rag-cag-security)<a href="https://agentmods.dev/skills/jpoutrin/product-forge/rag-cag-security"><img src="https://agentmods.dev/badge/skills/jpoutrin/product-forge/rag-cag-security/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/jpoutrin/product-forge/rag-cag-security"><img src="https://agentmods.dev/badge/skills/jpoutrin/product-forge/rag-cag-security.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 | $0.00047 | $0.00473 |
| Opus 5.5 | $0.00019 | $0.00189 |
| Sonnet 5.5 | $0.00009 | $0.00095 |
| Haiku 4.5 | $0.00005 | $0.00047 |
Grade A, and why
rag-cag-security 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 1mo 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.
What it actually says
RAG/CAG Security Skill
This skill provides security patterns for RAG and CAG systems.
Multi-Tenant Architecture
Tenant Isolation Strategies
- Namespace Isolation - Separate vector namespaces per tenant
- Metadata Filtering - Filter by tenant_id at query time
- Separate Collections - Isolated collections per tenant
# Metadata filtering approach
results = vector_store.similarity_search(
query,
filter={"tenant_id": current_user.tenant_id}
)
Access Control
Document-Level Permissions
@dataclass
class Document:
id: str
content: str
tenant_id: str
access_groups: list[str]
classification: str # public, internal, confidential
def can_access(user: User, doc: Document) -> bool:
return (
user.tenant_id == doc.tenant_id
and any(g in doc.access_groups for g in user.groups)
and user.clearance >= doc.classification
)
Prompt Injection Prevention
def sanitize_retrieved_context(chunks: list[str]) -> str:
"""Sanitize retrieved chunks before including in prompt."""
sanitized = []
for chunk in chunks:
# Remove potential instruction patterns
cleaned = remove_instruction_patterns(chunk)
# Escape special characters
escaped = escape_prompt_chars(cleaned)
sanitized.append(escaped)
return "\n".join(sanitized)
Data Classification
| Level | Description | Handling |
|---|---|---|
| Public | Open information | No restrictions |
| Internal | Company-only | Tenant isolation |
| Confidential | Sensitive | Encryption + audit |
| Restricted | Highly sensitive | Need-to-know basis |
Security Checklist
- Tenant isolation implemented
- Document-level access control
- Retrieved content sanitized
- Audit logging enabled
- Data encryption at rest
- Secure API authentication
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.
- 1mo ago First seen · 79 lines · 47 tokens per session scan A 56b88c8ee6a9
rag-cag-security is a skill published in the GitHub repository jpoutrin/product-forge (16 stars, last pushed 7mo ago), licensed MIT. It adds 47 tokens to every session and 473 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 2026-09-03.
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llm-app-patterns
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Generate vector embeddings via 9Router /v1/embeddings using OpenAI / Gemini / Mistral / Voyage / Nvidia / GitHub embedding models for RAG, semantic search, similarity. Use when the user wants embeddings, vectors, RAG, semantic search, or to embed text.
similarity-search-patterns
Implement efficient similarity search with vector databases. Use when building semantic search, implementing nearest neighbor queries, or optimizing retrieval performance.
embedding-strategies
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.
edd
Eval-driven development loop for this repo (rag-loop, an aircraft-systems RAG app on LangGraph, OpenAI, Qdrant Cloud, and TruLens). Use when the user wants to run an experiment, inspect feedback metrics, or decide keep-or-reject on a config/prompt change.