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/quick-searchnpx skills add Abilityai/cornelius --skill quick-searchgit 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 | $0.00024 | $0.00471 |
| Opus 5 | $0.00012 | $0.00235 |
| Sonnet 5 | $0.00005 | $0.00094 |
| Haiku 4.5 | $0.00002 | $0.00047 |
Grade A, and why
quick-search 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.
What it actually says
Quick Search
Fastest possible knowledge base retrieval. No subagents, no multi-layer orchestration, no changelogs.
Purpose
Return relevant notes and their graph neighborhood in minimal tool calls. Designed for speed over thoroughness.
Query
$ARGUMENTS
Process
Execute these two commands in parallel (single message, two Bash calls):
# 1. Semantic search (6-14s - the unavoidable cost)
resources/local-brain-search/run_search.sh "$ARGUMENTS" --limit 5 --json
# 2. Graph connections for likely top hit (0.3s - nearly free)
resources/local-brain-search/run_connections.sh "$ARGUMENTS" --json
Then read the top result file using Read tool.
That's it. Three tool calls. Present results and stop.
Output Format
Keep it brief:
## [Query]
**Top matches:**
1. [[Note Title]] (0.XX) - [one-line summary from content]
2. [[Note Title]] (0.XX) - [one-line summary]
3. [[Note Title]] (0.XX) - [one-line summary]
**Graph neighborhood** (for top hit):
- Outgoing: [[Note]], [[Note]], ...
- Incoming: [[Note]], [[Note]], ...
**Top result content:**
[First ~30 lines of the highest-scoring note]
Rules
- NO subagent spawning - do everything inline
- NO changelog creation - this is a read-only lookup
- NO multi-layer expansion - one search, one connection call, done
- NO spreading activation - use static mode (faster for simple lookups)
- Parallel execution - run search and connections in the same message
- Maximum 3 tool calls - search + connections + read top file
- If the user wants deeper analysis, tell them to use
/recallor/find-connections
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.
- 2d ago First seen · 66 lines · 24 tokens per session scan A 9df8a4dcfd67
quick-search is a skill published in the GitHub repository Abilityai/cornelius (104 stars, last pushed 9d ago), licensed MIT. It adds 24 tokens to every session and 471 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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