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/abilityai/cornelius/deep-researchgit 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 | $0.00017 | $0.04097 |
| Opus 5 | $0.00009 | $0.02048 |
| Sonnet 5 | $0.00003 | $0.00819 |
| Haiku 4.5 | $0.00002 | $0.00410 |
Grade A, and why
deep-research 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.
How it starts
The opening of the file, as written. The whole thing — 556 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Deep Research & Knowledge Integration Pipeline
You are orchestrating a fully autonomous research → extraction → connection discovery workflow to expand the knowledge base with cutting-edge insights.
Input Processing
User Input: $ARGUMENTS
Execution Modes:
- Directed Mode - User specifies topic(s):
$ARGUMENTS = "neuroscience of habits"or$ARGUMENTS = "multi-agent systems, safety alignment" - Autonomous Mode - You select topics:
$ARGUMENTS = ""or$ARGUMENTS = "auto"
Mission
Execute a complete 3-phase autonomous research pipeline:
- RESEARCH - Gather cutting-edge papers and developments
- EXTRACT - Pull unique insights from research findings
- CONNECT - Map connections to existing knowledge base
Critical Requirement: ALL extracted insights MUST be stored in Document Insights folder structure to keep separate from main Brain.
Phase 1: Topic Selection & Research Planning
A. If User Provided Topic(s) (Directed Mode)
- Parse
$ARGUMENTSfor topic(s) - Validate topics are research-worthy
- Plan research scope for each topic
B. If Autonomous Mode
Analyze knowledge base to identify research opportunities:
-
Read knowledge base analysis:
cat ./knowledge-base-analysis.md -
Check recent activity:
ls -lt ./Brain/Document\ Insights/ | head -10 -
Identify gaps based on:
- Underrepresented domains in knowledge-base-analysis.md
- Missing connections flagged in recent changelogs
- Emerging themes from existing insights
- User's recent work patterns
- CLAUDE.md priorities and future directions
-
Select 1-3 research topics that would:
- Fill identified gaps
- Build on existing strengths (e.g., Buddhism-Neuroscience-AI triangle)
- Connect underexplored domains
- Add empirical validation to intuitive frameworks
- Challenge or extend current thinking
Examples of Good Topic Selection:
- "Neuroscience of habits and behavior change" (if habit formation underrepresented)
- "Collective intelligence and swarm behavior" (if group dynamics missing)
- "Embodied cognition and interoception" (if embodiment gap identified)
- "Complexity science and emergence" (if systems thinking needed)
- "Creativity neuroscience and insight generation" (if creative process mechanics missing)
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 · 556 lines · 17 tokens per session scan A dd7b44d98bf6
deep-research is a command published in the GitHub repository Abilityai/cornelius (104 stars, last pushed 9d ago), licensed MIT. It adds 17 tokens to every session and 4,097 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.
Other commands, from other repositories
git
Git operations with intelligent commit messages and workflow optimization.
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.