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 skills/abilityai/cornelius/recallnpx skills add Abilityai/cornelius --skill recallgit clone --depth 1 https://github.com/Abilityai/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 | $0.00021 | $0.00751 |
| Opus 5 | $0.00010 | $0.00376 |
| Sonnet 5 | $0.00004 | $0.00150 |
| Haiku 4.5 | $0.00002 | $0.00075 |
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.
How it starts
The opening of the file, as written. The whole thing — 104 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Semantic Knowledge Retrieval
You are tasked with retrieving relevant knowledge from the Obsidian vault using multi-layer semantic search.
Local Brain Search
Use Local Brain Search for all semantic search operations. Spreading activation mode recommended for synthesis queries.
Scripts:
# Static search (fast, exact matches)
resources/local-brain-search/run_search.sh "query" --limit 10 --json
# Spreading activation search (follows graph connections)
resources/local-brain-search/run_search.sh "query" --mode spreading --limit 10 --json
# Find connections
resources/local-brain-search/run_connections.sh "Note Name" --json
# Find hubs
resources/local-brain-search/run_connections.sh --hubs --json
Search Query
$ARGUMENTS
Instructions
-
First Layer - Initial Search:
- Use spreading activation for better context:
resources/local-brain-search/run_search.sh "$ARGUMENTS" --mode spreading --limit 5 --json - Use
Readtool to read the full content of the top 2 results
- Use spreading activation for better context:
-
Second Layer - Direct Associations:
- For the top result from layer 1, get connections:
resources/local-brain-search/run_connections.sh "Top Result Note" --json - Use
Readtool to read the full content of the top 2 connected notes
- For the top result from layer 1, get connections:
-
Third Layer - Extended Network:
- For additional context, check hub notes and bridges:
resources/local-brain-search/run_connections.sh --hubs --json - This reveals deeper conceptual connections
- For additional context, check hub notes and bridges:
Output Format
Present the findings in this structured format:
# Knowledge Recall: [Query Topic]
## Layer 1: Direct Matches
[List notes found with similarity/activation scores and key excerpts]
## Layer 2: First-Degree Associations
[List connected notes with their relationships and excerpts]
## Layer 3: Extended Network
[Show hub notes and bridge connections]
## Key Insights
[Synthesize the main themes and connections discovered]
## Relevant Content
[Include the most pertinent excerpts from the retrieved notes]
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 · 104 lines · 21 tokens per session scan A 227bfd6ce6c9
recall is a skill published in the GitHub repository Abilityai/cornelius (104 stars, last pushed 10d ago), licensed MIT. It adds 21 tokens to every session and 751 once invoked, about $0.0001 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 2026-08-30.
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auto-perf-optimize
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chat-perf
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chat-pet-sprite-creation
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cpu-profile-analysis
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