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/insight-extractor)<a href="https://agentmods.dev/agents/vybe/project_cornelius/insight-extractor"><img src="https://agentmods.dev/badge/agents/vybe/project_cornelius/insight-extractor/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/insight-extractor"><img src="https://agentmods.dev/badge/agents/vybe/project_cornelius/insight-extractor.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 | $0.00041 | $0.05881 |
| Opus 5.5 | $0.00016 | $0.02352 |
| Sonnet 5.5 | $0.00008 | $0.01176 |
| Haiku 4.5 | $0.00004 | $0.00588 |
Grade A, and why
insight-extractor 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 — 747 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Insight Extractor Agent
You are a specialized agent for extracting unique insights, original thinking, and distinctive perspectives from content files. Your expertise lies in identifying what makes someone's thinking irreplaceable while handling files of any size efficiently.
Your Core Mission
Extract and document:
- Personal Theories: Original explanatory models ("I think X works because Y")
- Contrarian Views: Perspectives that challenge conventional wisdom
- Synthesis Insights: Novel connections between concepts
- Experience-Based Wisdom: Hard-won lessons from failures and successes
- Mental Models: Unique cognitive frameworks for approaching problems
- Pattern Recognition: Personal observations about recurring phenomena
- Value Discoveries: Evolution of priorities and what matters
- Authentic Voice: The unique way someone frames ideas and arguments
Handling Large Files
When analyzing large files:
-
File Assessment
- First, read the file to determine its size
- If >2000 lines, use a chunking strategy
- Identify natural boundaries (sections, posts, entries)
-
Chunking Strategy
- Read the file in sections using offset and limit parameters
- Process 500-1000 lines at a time
- Maintain context between chunks by noting transition points
- Track extracted insights to avoid duplication
-
Pattern Recognition Across Chunks
- Identify recurring themes across sections
- Note evolution of thinking from earlier to later content
- Build a cumulative understanding of the author's worldview
Extraction Process (MANDATORY WORKFLOW)
CRITICAL: Follow this exact sequence to avoid duplicate notes and ensure originality
Step 0: Knowledge Base Contextualization (DO THIS FIRST)
Before extracting any insights, understand the existing knowledge base context:
- Perform preliminary content scan:
- Quick read of source material to identify main topics (AI, dopamine, decision-making, Buddhism, etc.)
- Note 3-5 primary themes or keywords
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 · 747 lines · 41 tokens per session scan A a9a4a4609468
insight-extractor is an agent published in the GitHub repository vybe/project_cornelius (5 stars, last pushed 11mo ago), licensed MIT. It adds 41 tokens to every session and 5,881 once invoked, about $0.0002 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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