Is knowledge-retrieval safe to install?

SaferSkills independently audited knowledge-retrieval (Agent Skill) and scored it 100/100 (green). The audit ran 55 deterministic rules across Security, Supply Chain, Maintenance, Transparency, and Community; it found 0 high-severity and 0 lower-severity findings. The full rule-by-rule trace and per-finding evidence are below. Free, methodology-open.

CategoryWeightCategory scoreContribution

Securityprompt, exec, net, exfil, eval

35%

100

35.0 pts

Supply chainhash, typosquat, maintainer, lockfile

20%

100

20.0 pts

Maintenancestaleness, pinning, CI

15%

100

15.0 pts

TransparencySKILL.md, perms, README

15%

100

15.0 pts

Communityinstalls, verify, response

15%

100

15.0 pts

Findings & checks · 0 flagged

Securityscore 100 · 0 findings

✓—All security checks passedNo findings in this category for the latest scan.pass

Supply chainscore 100 · 0 findings

✓—All supply chain checks passedNo findings in this category for the latest scan.pass

Maintenancescore 100 · 0 findings

✓—All maintenance checks passedNo findings in this category for the latest scan.pass

Transparencyscore 100 · 0 findings

✓—All transparency checks passedNo findings in this category for the latest scan.pass

Communityscore 100 · 0 findings

✓—All community checks passedNo findings in this category for the latest scan.pass

The primary manifest — the file an agent reads to learn what this artifact does.

examples/nvidia-deep-researcher/skills/knowledge-retrieval/SKILL.md · 1 file

examples/nvidia-deep-researcher/skills/knowledge-retrieval/SKILL.md1.5 KB · Markdown

name: knowledge-retrieval

description: Semantic search over ingested documents using RAG (LlamaIndex/ChromaDB or Foundational RAG)

allowed-tools: knowledge-retrieval

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_retrieval with 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.12

Constraints

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

knowledge-retrieval— agent skill

Is knowledge-retrieval safe to install?

Knowledge Retrieval

When to Use

How to Use

Result Format

Constraints

Backend Options

Audit the pieces. Scan the whole. Decide.