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/advisenpx skills add Abilityai/cornelius --skill advisegit clone --depth 1 https://github.com/Abilityai/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/skills/abilityai/cornelius/advise)<a href="https://agentmods.dev/skills/abilityai/cornelius/advise"><img src="https://agentmods.dev/badge/skills/abilityai/cornelius/advise.svg" alt="Measured on agentmods" 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 | $0.00027 | $0.00815 |
| Opus 5 | $0.00014 | $0.00407 |
| Sonnet 5 | $0.00005 | $0.00163 |
| Haiku 4.5 | $0.00003 | $0.00081 |
Grade A, and why
advise 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 4d 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 — 101 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Advise
Help solve problems by grounding advice in your accumulated knowledge and frameworks.
Purpose
Turn natural language problems into KB-grounded advice. Fast path: no subagents, no changelogs, no multi-layer expansion.
Problem
$ARGUMENTS
Process
Step 1: Extract Search Terms (no tool calls - just reasoning)
From the problem description, identify 3-4 keyword clusters that would match relevant KB content:
- Core concepts (what domain is this?)
- Related frameworks (what mental models apply?)
- Analogous patterns (what similar problems exist?)
Example:
- Problem: "Should I focus on fundraising or product development?"
- Search terms:
decision making tradeoffs,explore exploit,focus prioritization,opportunity cost
Step 2: Parallel Knowledge Retrieval
Run 3-4 searches in parallel (single message, multiple Bash calls):
resources/local-brain-search/run_search.sh "search term 1" --limit 3 --json
resources/local-brain-search/run_search.sh "search term 2" --limit 3 --json
resources/local-brain-search/run_search.sh "search term 3" --limit 3 --json
Step 3: Read Top Insights
From the search results, read 2-3 of the most relevant note files in parallel:
# Use Read tool on the top-scoring, most relevant files
Step 3.5: Check BDG Context (optional, if top results are frameworks)
For any top result that looks like a framework or key insight, check its BDG context:
resources/brain-graph/run_brain_graph.sh inspect "Top Result Name" --json
This reveals: lifecycle phase (is it generative?), staleness (is it still fresh?), and typed edges (what does it drive?). Prioritize generative frameworks over reflective notes. Warn if citing a stale note.
Step 4: Synthesize Advice
Combine the retrieved insights to address the original problem:
- Apply frameworks from the notes to the specific situation
- Cite specific notes: [[Note Title]]
- Highlight tensions or tradeoffs the KB reveals
- Give concrete recommendations grounded in your own thinking
- Prioritize generative notes (lifecycle > 0.6) - these are the user's strongest frameworks
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.
- 4d ago First seen · 101 lines · 27 tokens per session scan A e04b7f9937d3
advise is a skill published in the GitHub repository Abilityai/cornelius (105 stars, last pushed 10d ago), licensed MIT. It adds 27 tokens to every session and 815 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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