knowledge-retrieval

A search tool for finding meaning-related matches in documents that were loaded into a private knowledge base. RAG means retrieving relevant document passages before generating an answer.

In plain words
What is it for?
Use it to search preloaded documents and return relevant passages with the source filename and page number.
Why use it?
It lets you find information in internal PDFs, reports, and technical documents without relying only on exact keyword matches or the public web.

Skill for Claude CodeCodex

Install

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.

agentmods
npx agentmods add skills/open-gitagent/opengap/knowledge-retrieval
Any agent
npx skills add open-gitagent/opengap --skill knowledge-retrieval
Clone the repo
git clone --depth 1 https://github.com/open-gitagent/opengap

Made for: Claude Code, Codex.

Per session 28 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 336 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5 $0.00028 $0.00336
Opus 5 $0.00014 $0.00168
Sonnet 5 $0.00006 $0.00067
Haiku 4.5 $0.00003 $0.00034

Measured 2d ago against content hash b15b4ea2aa0b, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

knowledge-retrieval 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 2d 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.

examples/nvidia-deep-researcher/skills/knowledge-retrieval/SKILL.md · 46 lines

What it actually says

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

  1. Formulate a semantic search query describing the information needed
  2. Call knowledge_retrieval with the query
  3. Review returned chunks for relevance
  4. 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
Changes

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.

  1. 2d ago First seen · 46 lines · 28 tokens per session scan A b15b4ea2aa0b

Subscribe to this mod's changes

knowledge-retrieval is a skill published in the GitHub repository open-gitagent/opengap (2,921 stars, last pushed 2mo ago), licensed MIT. It adds 28 tokens to every session and 336 once invoked, about $0.0001 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 2026-08-30.

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