Hybrid search (vector + BM25 via RRF + metadata boost) against the pgvector Knowledge base, with optional RAG synthesis.

Custom licenceAuto-check: notesAI & LLM Engineering

Install Knowledge Query

skills CLI
$ npx skills add evolution-foundation/evo-nexus --skill knowledge-query -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install evolution-foundation/evo-nexus knowledge-query --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/evolution-foundation/evo-nexus.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.claude/skills/knowledge-query .claude/skills/knowledge-query && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
knowledge-query
GitHub stars
545
Token cost
~804 tokens
SKILL.md length
247 words
Files
1
Skills in repo
118
Repo updated
First seen
Licence
Custom licence

At a glance

Hybrid search (vector + BM25 via RRF + metadata boost) against the pgvector Knowledge base, with optional RAG synthesis.

  • Works in 2 steps: Identify active connection → Hybrid search
  • The user asks factual questions that should be grounded in indexed documents (e.g.
  • SKILL.md covers When to trigger, Arguments, Workflow and Output, plus 1 more section
  • Needs ANTHROPIC_API_KEY

What it does

Knowledge Query is an agent skill from evolution-foundation/evo-nexus. Hybrid search (vector + BM25 via RRF + metadata boost) against the pgvector Knowledge base, with optional RAG synthesis. Use when the user asks factual questions that should be grounded in indexed documents (e.g., 'what do we know about X', 'search the knowledge base for Y', '@knowledge <query'). Pass answer=true to synthesize a narrative response with citations instead of raw snippets.

Its SKILL.md is about 800 tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in AI & LLM Engineering, covering Retrieval-augmented generation, Knowledge bases and Citation management. It works with pgvector and Anthropic API. The repository describes itself as: The open source operating system for AI-powered businesses.

When your agent uses it

  • The user asks factual questions that should be grounded in indexed documents (e.g.
  • What do we know about X
  • Search the knowledge base for Y
  • @knowledge <query)

Example prompts

  • “what do we know about X”
  • “search the knowledge base for Y”
  • “@knowledge <query”
  • “/knowledge-query”

Requirements

  • Python 3
  • A credential in ANTHROPIC_API_KEY

Workflow steps

2 steps, taken from the step headings in SKILL.md.

  1. Identify active connection
  2. Hybrid search

What it can do on your machine

Read from SKILL.md and the folder at commit 7f5dd76. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md (its code samples are python).

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names these keys or tokens, usually read from environment variables:

    • ANTHROPIC_API_KEY

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Knowledge Query loads about 804 tokens when it runs. Until then it costs about 102 tokens; SKILL.md has 247 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~102
When it runs · the whole SKILL.md, loaded when a task matches
~804

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check: notes

The automated check noted patterns worth knowing about, such as sudo or a known installer.

  • NoteMentions a .env fileSKILL.md:81
    nthropic` SDK (`ANTHROPIC_API_KEY` from `.env`). Model: `claude-haiku-4-5-20251001`. Max tokens: 800.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

Its licence (Custom licence) doesn't allow us to republish the file, so here is its outline and opening line. It has 247 words (~804 tokens).

“Group: Consumption. Hybrid search on pgvector + optional RAG (LLM synthesis with citations).”

— opening of SKILL.md by evolution-foundation, Custom licence
name
knowledge-query

Read the full SKILL.md on GitHub

Files

Just SKILL.md in .claude/skills/knowledge-query of evolution-foundation/evo-nexus.

Open the folder on GitHubat commit 7f5dd76

Compare with similar skills

Knowledge Query next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Knowledge Query compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Knowledge Query this skillevolution-foundation/evo-nexus545—~804Automated safety check: NotesCustom licence
Gnogmickel/gno1151 repos~1.6kAutomated safety check: PassMIT
Gnogmickel/gno115—~11kAutomated safety check: PassMIT
Penguin SDKPrism-Shadow/penguin-harness2.5k—~11kAutomated safety check: PassApache-2.0
RAG AssistantAtmosphere/atmosphere3.8k—~504Automated safety check: PassApache-2.0
Vault Contextual RetrievalAgriciDaniel/claude-obsidian15k—~1.4kAutomated safety check: PassMIT

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Questions about Knowledge Query

What does Knowledge Query do?

Hybrid search (vector + BM25 via RRF + metadata boost) against the pgvector Knowledge base, with optional RAG synthesis. Knowledge Query is an agent skill from evolution-foundation/evo-nexus. Hybrid search (vector + BM25 via RRF + metadata boost) against the pgvector Knowledge base, with optional RAG synthesis.

When should I use Knowledge Query?

Knowledge Query fits situations like: the user asks factual questions that should be grounded in indexed documents (e.g; what do we know about X; search the knowledge base for Y; @knowledge <query).

How do I install Knowledge Query in Claude Code?

Run `npx skills add evolution-foundation/evo-nexus --skill knowledge-query -a claude-code`. Or copy the skill folder (.claude/skills/knowledge-query in evolution-foundation/evo-nexus) into .claude/skills/knowledge-query in your project. Claude Code loads it when a task matches its description.

How do I install Knowledge Query in Codex?

Run `npx skills add evolution-foundation/evo-nexus --skill knowledge-query -a codex`. Or copy the skill folder (.claude/skills/knowledge-query in evolution-foundation/evo-nexus) into .agents/skills/knowledge-query in your project. Codex loads it when a task matches its description.

Can I use Knowledge Query in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add evolution-foundation/evo-nexus --skill knowledge-query -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/knowledge-query, .gemini/skills/knowledge-query, .github/skills/knowledge-query and .opencode/skills/knowledge-query in your project.

What does Knowledge Query need to run?

Going by SKILL.md and its folder, Knowledge Query needs credentials named ANTHROPIC_API_KEY. Our summary lists: Python 3; A credential in ANTHROPIC_API_KEY.

Does Knowledge Query access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Knowledge Query safe to install?

Our automated static check of SKILL.md found notes only (mentions a .env file), nothing it rates as a warning. It is not a guarantee. Review the folder before installing.

What licence does Knowledge Query use?

Knowledge Query has a licence file (the repository's licence) that doesn't match a standard licence. Read it on GitHub before reusing the skill.

How many tokens does Knowledge Query use?

About 804 tokens (SKILL.md is roughly 3.2k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Knowledge Query?

Skills that share tags, products or a category with Knowledge Query: Gno (gmickel/gno, 115 stars), Gno (gmickel/gno, 115 stars), Penguin SDK (Prism-Shadow/penguin-harness, 2.5k stars) and RAG Assistant (Atmosphere/atmosphere, 3.8k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Knowledge Query?

evolution-foundation (a GitHub organization) maintains it in evolution-foundation/evo-nexus, which has 545 GitHub stars. The repository holds 118 skills in this directory. The repository was last updated on May 13, 2026.

Source: evolution-foundation/evo-nexus on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.