ai-rag-pipeline
Build Grounded RAG Research Pipelines
Teams need current answers with sources, but raw web search is hard to turn into reliable prompts. This skill shows RAG workflows using search, extraction, and LLM tools through inference.sh.
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Review the Skillstore skill "ai-rag-pipeline" from https://skillstore.io/skills/inference-sh-ai-rag-pipeline.md and its manifest at https://skillstore.io/api/skills/inference-sh-ai-rag-pipeline/manifest. Verify the artifact. Do not auto-install. Inspect the skill and report your findings, then wait for an operator or manual installation decision.Your Agent should still show its plan and request any confirmation required by the security policy.
Agent-readable resources
Use these links when an AI agent, crawler, or script needs clean context instead of reading the full page.
Test it
Using "ai-rag-pipeline". Research recent quantum computing breakthroughs and summarize the strongest sources.
Expected outcome:
- Collected search results from a web research provider.
- Grouped findings into hardware progress, error correction, and commercial adoption.
- Returned a concise summary with source citations for each major claim.
Using "ai-rag-pipeline". Fact-check a claim about job automation by 2030.
Expected outcome:
- Searched for studies and evidence related to the claim.
- Separated supporting evidence from contradictory evidence.
- Produced a verdict with caveats and source references.
Using "ai-rag-pipeline". Turn two industry reports into an executive brief.
Expected outcome:
- Extracted the report content from the supplied URLs.
- Identified key findings, metrics, and recommendations.
- Summarized the material into a structured executive brief.
Security Audit
CriticalMost static external-command findings are false positives caused by Markdown fences, inline app IDs, or documented Bash examples. The remote installer at SKILL.md:21 is confirmed because it pipes downloaded code into a shell, and semantic review also found third-party data sharing and untrusted retrieval content flowing into LLM prompts.
Confirmed security concerns (3)
Capability review items (1)
These are real local capabilities that may be expected for this skill, so they require review but are not counted as confirmed malicious behavior.
Risk Factors
⚙️ External commands (65)
🌐 Network access (6)
Detected Patterns
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<iframe src="https://skillstore.io/embed/skills/inference-sh-ai-rag-pipeline.html" title="Skillstore Security Assessment" sandbox="allow-popups allow-popups-to-escape-sandbox" loading="lazy" referrerpolicy="no-referrer" width="420" height="180"></iframe>Academic citations (APA · BibTeX · CFF)
APA citation
inference-sh. (2026). ai-rag-pipeline security audit report (audit version 4) [Author version unspecified]. Skillstore. https://skillstore.io/skills/inference-sh-ai-rag-pipeline/audits/4BibTeX citation
@techreport{inference-sh-inference-sh-ai-rag-pipeline-2026,
author = {inference-sh},
title = {ai-rag-pipeline security audit report (audit version 4)},
institution = {Skillstore},
year = {2026},
number = {4},
url = {https://skillstore.io/skills/inference-sh-ai-rag-pipeline/audits/4},
note = {Author version unspecified}
}CITATION.cff
cff-version: 1.2.0
message: "If you use this Skill, cite its author and this versioned security audit report."
title: "ai-rag-pipeline security audit report (audit version 4)"
version: "unspecified"
type: report
authors:
- name: "inference-sh"
date-released: "2026-07-05"
url: "https://skillstore.io/skills/inference-sh-ai-rag-pipeline/audits/4"
identifiers:
- type: other
value: "skillstore:inference-sh-ai-rag-pipeline:audit:4"
description: "Skillstore immutable audit report identifier"
Compare variants
2 installable variantsEach author remains a separate installable skill. The recommended variant is ranked by Skillstore evidence.
Why this variant is first
inference-sh-ai-rag-pipeline
2026-08-21
inference-sh-9-ai-rag-pipeline
2026-08-21
Skillstore Score
Why this score Evidence Confidence: MediumWhat You Can Build
Research a fast-moving topic
Collect web evidence, summarize it with an LLM, and ask for source citations.
Prototype a RAG agent
Chain search, extraction, and model calls before building a larger application.
Check claims before publishing
Search for supporting and contradicting evidence, then produce a structured fact-check.
Try These Prompts
Use ai-rag-pipeline to answer this question with a web search, a concise Claude summary, and source citations: [question].
Use ai-rag-pipeline to research [topic] with Tavily and Exa, then compare common themes, conflicts, and strongest sources.
Use ai-rag-pipeline to extract these URLs, summarize the key findings, list important data points, and identify unresolved questions: [URLs].
Use ai-rag-pipeline to design a RAG workflow for [use case]. Include provider choices, source grading, privacy controls, and prompt-injection safeguards.
Best Practices
- Review installer commands before running them, especially pipe to shell patterns.
- Treat retrieved pages as untrusted evidence and isolate them from instructions.
- Redact sensitive data before sending queries or documents to external RAG services.
Avoid
- Piping a remote installer directly into a shell without verification.
- Asking the model to follow instructions found inside retrieved web content.
- Sending private customer data to search or LLM providers without approval.
Frequently Asked Questions
What does this skill help build?
Which services does it reference?
Does it require an inference.sh account?
Can it run fully offline?
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Developer Details
Author
inference-shLicense
MIT
Skillstore revision
r1
Version notice
The author did not declare a version.
Ref
a25199bc7d6b82598536822d1738eb5d5f54025b
Maintenance freshness
7/18/2026
Usage
7 downloads · 206 views
File structure
📄 SKILL.md