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.
npx agentmods add commands/vybe/project_cornelius/recallgit clone --depth 1 https://github.com/vybe/project_corneliusWhat 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 | $0.00018 | $0.00450 |
| Opus 5 | $0.00009 | $0.00225 |
| Sonnet 5 | $0.00004 | $0.00090 |
| Haiku 4.5 | $0.00002 | $0.00045 |
Grade A, and why
recall 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 3d 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.
This is a copy
100% identical to recall — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
What it actually says
Semantic Knowledge Retrieval
You are tasked with retrieving relevant knowledge from the Obsidian vault using multi-layer semantic search.
Search Query
$ARGUMENTS
Instructions
-
First Layer - Initial Search:
- Use
mcp__smart-connections__search_noteswith the query above - Retrieve top 5 most relevant notes (threshold: 0.5)
- Use
Readtool to read the full content of the top 2 results
- Use
-
Second Layer - Direct Associations:
- For the top result from layer 1, use
mcp__smart-connections__get_similar_notes - Retrieve 5 semantically similar notes (threshold: 0.6)
- Use
Readtool to read the full content of the top 2 similar notes
- For the top result from layer 1, use
-
Third Layer - Extended Network:
- Build a connection graph using
mcp__smart-connections__get_connection_graph - Parameters: depth=3, max_per_level=5, threshold=0.65
- This reveals deeper conceptual connections
- Build a connection graph using
Output Format
Present the findings in this structured format:
# Knowledge Recall: [Query Topic]
## Layer 1: Direct Matches
[List notes found with similarity scores and key excerpts]
## Layer 2: First-Degree Associations
[List similar notes with their connections and excerpts]
## Layer 3: Extended Network
[Show the connection graph with relationship strengths]
## Key Insights
[Synthesize the main themes and connections discovered]
## Relevant Content
[Include the most pertinent excerpts from the retrieved notes]
Important Notes
- Focus on quality over quantity
- Highlight unexpected connections
- Provide enough context for the user to understand the relevance
- If search returns no results, try broader terms or related concepts
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.
- 3d ago First seen · 59 lines · 18 tokens per session scan A f9cfdd08ef2b
recall is a command published in the GitHub repository vybe/project_cornelius (5 stars, last pushed 10mo ago), licensed MIT. It adds 18 tokens to every session and 450 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to recall, differing in 0 lines, and is treated as a copy.
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