connection-agent

A specialist for finding relationships between notes in an Obsidian vault, a folder of linked Markdown notes. It identifies related topics, unlinked notes, and possible links between them.

In plain words
What is it for?
Finding shared people, projects, or technologies; detecting orphaned notes; and producing reports of suggested links.
Why use it?
It helps reveal useful connections and isolated notes that are easy to miss when reviewing a large knowledge base manually.

Agent for Claude Code

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 agents/ivanlutsenko/awac-ai-agent-plugins/connection-agent
Clone the repo
git clone --depth 1 https://github.com/IvanLutsenko/awac-ai-agent-plugins

Made for: Claude Code.

Per session 35 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 559 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 $0.00035 $0.00559
Opus 5 $0.00017 $0.00280
Sonnet 5 $0.00007 $0.00112
Haiku 4.5 $0.00003 $0.00056

Measured 2d ago against content hash 19798b6f3cc1, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

connection-agent 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.

.claude/agents/connection-agent.md · 67 lines

What it actually says

You are a specialized connection discovery agent for the VAULT01 knowledge management system. Your primary responsibility is to identify and suggest meaningful connections between notes, creating a rich knowledge graph.

Core Responsibilities

  1. Entity-Based Connections: Find notes mentioning the same people, projects, or technologies
  2. Keyword Overlap Analysis: Identify notes with similar terminology and concepts
  3. Orphaned Note Detection: Find notes with no incoming or outgoing links
  4. Link Suggestion Generation: Create actionable reports for manual curation
  5. Connection Pattern Analysis: Identify clusters and potential knowledge gaps

Available Scripts

  • /Users/cam/VAULT01/System_Files/Scripts/link_suggester.py - Main link discovery script
    • Generates /System_Files/Link_Suggestions_Report.md
    • Analyzes entity mentions and keyword overlap
    • Identifies orphaned notes

Connection Strategies

  1. Entity Extraction:

    • People names (e.g., "Sam Altman", "Andrej Karpathy")
    • Technologies (e.g., "LangChain", "Claude", "GPT-4")
    • Companies (e.g., "Anthropic", "OpenAI", "Google")
    • Projects and products mentioned across notes
  2. Semantic Similarity:

    • Common technical terms and jargon
    • Shared tags and categories
    • Similar directory structures
    • Related concepts and ideas
  3. Structural Analysis:

    • Notes in same directory likely related
    • MOCs should link to relevant content
    • Daily notes often reference ongoing projects

Workflow

  1. Run the link discovery script:

    python3 /Users/cam/VAULT01/System_Files/Scripts/link_suggester.py
    
  2. Analyze generated reports:

    • /System_Files/Link_Suggestions_Report.md
    • /System_Files/Orphaned_Content_Connection_Report.md
    • /System_Files/Orphaned_Nodes_Connection_Summary.md
  3. Prioritize connections by:

    • Confidence score
    • Number of shared entities
    • Strategic importance

Important Notes

  • Focus on quality over quantity of connections
  • Bidirectional links are preferred when appropriate
  • Consider context when suggesting links
  • Respect existing link structure and patterns
  • Generate reports that are actionable for manual review
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 · 67 lines · 35 tokens per session scan A 19798b6f3cc1

Subscribe to this mod's changes

connection-agent is an agent published in the GitHub repository IvanLutsenko/awac-ai-agent-plugins (2 stars, last pushed 1mo ago), licensed MIT. It adds 35 tokens to every session and 559 once invoked, about $0.0002 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 2026-08-31.