Knowledge Retrieval
Semantic search over ingested documents using RAG (LlamaIndex/ChromaDB or Foundational RAG)
Install
$ agentstack add skill-open-gitagent-opengap-knowledge-retrieval ✓ scanned · ✓ verified, works with Claude Code, Cursor, and more.
Security review
✓ PassedNo issues found. Passed automated security review. · v0.1.0 How review works →
- ✓ Prompt-injection patterns
- ✓ Secret / credential exfiltration
- ✓ Dangerous shell & filesystem operations
- ✓ Untrusted network calls
- ✓ Known-malicious package signatures
What it can access
- ✓ Network access No
- ✓ Filesystem access No
- ✓ Shell / process execution No
- ✓ Environment & secrets No
- ✓ Dynamic code execution No
From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.
Verified badge
Passed review? Show it. Paste this badge into your README, it links to the public security report.
Links to your public security report.[](https://agentstack.voostack.com/security/report/skill-open-gitagent-opengap-knowledge-retrieval) Reliability & compatibility
Declared compatibility
Compatibility is declared by the source manifest. End-to-end runtime verification is coming, see below.
We're building live execution health for every listing: tool-call success rate, median latency, uptime, and last-checked timestamps, measured, not self-reported. It isn't live yet, so we don't show numbers we can't stand behind.
How agent discovery & health will work →About
Knowledge Retrieval
Perform semantic search over a pre-ingested document collection using Retrieval-Augmented Generation (RAG). Backed by LlamaIndex with ChromaDB or NVIDIA Foundational RAG.
When to Use
- Searching internal or pre-ingested documents and reports
- Finding information in PDFs, whitepapers, or technical documentation
- Retrieving domain-specific knowledge not available on the open web
- This is the highest priority source — check the knowledge base first before web or paper searches
How to Use
- Formulate a semantic search query describing the information needed
- Call
knowledge_retrievalwith the query - Review returned chunks for relevance
- Note the citation metadata (filename, page number) for sourcing
Result Format
Results are returned as text chunks with citation metadata:
Relevant text passage from the ingested document...
Citation: filename.pdf, p.12Constraints
- Searches only over documents that have been ingested into the knowledge index
- Returns ranked chunks based on semantic similarity
- Citation format:
Citation: filename.ext, p.X - Each call counts toward the researcher's 8-call limit per task
Backend Options
- LlamaIndex + ChromaDB — Local vector store with LlamaIndex orchestration
- NVIDIA Foundational RAG — NVIDIA-hosted RAG service with NeMo Retriever
Source & license
This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.
- Author: open-gitagent
- Source: open-gitagent/opengap
- License: MIT
- Homepage: https://gitagent.sh
Install and usage instructions live in the source repository linked above.
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Versions
- v0.1.0 Imported from the upstream source.