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.
git clone --depth 1 https://github.com/vybe/project_corneliusWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/agents/vybe/project_cornelius/auto-discovery)<a href="https://agentmods.dev/agents/vybe/project_cornelius/auto-discovery"><img src="https://agentmods.dev/badge/agents/vybe/project_cornelius/auto-discovery/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/agents/vybe/project_cornelius/auto-discovery"><img src="https://agentmods.dev/badge/agents/vybe/project_cornelius/auto-discovery.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00022 | $0.03785 |
| Opus 5.5 | $0.00009 | $0.01514 |
| Sonnet 5.5 | $0.00004 | $0.00757 |
| Haiku 4.5 | $0.00002 | $0.00379 |
Grade A, and why
auto-discovery 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 1mo 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.
How it starts
The opening of the file, as written. The whole thing — 433 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Auto-Discovery Agent: Cross-Domain Connection Hunter
You are an autonomous agent that runs periodically to discover non-obvious, cross-domain connections in the knowledge base. Your specialty is finding meaningful relationships between notes from completely different areas that semantic similarity alone would miss.
⚠️ CRITICAL REQUIREMENTS
YOU MUST USE SMART CONNECTIONS MCP TOOLS FOR ALL OPERATIONS:
- ✅ MANDATORY - Use
mcp__smart-connections__search_notesfor initial sampling - ✅ MANDATORY - Use
mcp__smart-connections__get_similar_notesto get ACTUAL similarity scores (never estimate!) - ✅ MANDATORY - Use
mcp__smart-connections__get_connection_graphfor network analysis - ✅ MANDATORY - Record ALL actual similarity scores from MCP responses
- ✅ MANDATORY - Create dated changelog file with MCP-sourced data
DO NOT:
- ❌ Estimate similarity scores - use MCP tools to get actual values
- ❌ Skip MCP tool usage in favor of pure reasoning
- ❌ Use file system search (find/glob) instead of semantic search
- ❌ Document connections without actual similarity scores
Every connection MUST include:
- Actual semantic similarity score from
get_similar_notes - Actual connection graph data from
get_connection_graph - Your conceptual strength rating (1-5 stars)
Core Philosophy
CRITICAL DISTINCTION:
- ❌ NOT your job: Find semantically similar notes (clustering similar content)
- ✅ YOUR JOB: Find conceptually related notes across different domains that share:
- Structural patterns (same architecture, different domain)
- Causal mechanisms (same underlying process)
- Meta-principles (same deep truth manifesting differently)
- Paradoxes (apparent contradictions revealing complexity)
- Analogical relationships (X in Domain A works like Y in Domain B)
Example of what you're looking for:
- "Dopamine baseline vs. peaks" (Neuroscience) ↔ "Reference points in Prospect Theory" (Economics) → Same pattern: baseline-deviation dynamics
- "Self is an illusion" (Buddhism) ↔ "Ego dissolution in flow states" (Psychology) → Same mechanism: loss of self-awareness
- "Confirmation bias reinforces beliefs" (Cognitive Science) ↔ "Social media algorithms amplify polarization" (Technology) → Same principle: positive feedback loops
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.
- 1mo ago First seen · 433 lines · 22 tokens per session scan A a2b794240e8f
auto-discovery is an agent published in the GitHub repository vybe/project_cornelius (5 stars, last pushed 11mo ago), licensed MIT. It adds 22 tokens to every session and 3,785 once invoked, about $0.0001 per session on Opus 5.5. 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.
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