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 agents/liortesta/clawdagent/ctogit clone --depth 1 https://github.com/liortesta/ClawdAgentWhat 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 | $0.00035 | $0.00379 |
| Opus 5 | $0.00017 | $0.00189 |
| Sonnet 5 | $0.00007 | $0.00076 |
| Haiku 4.5 | $0.00003 | $0.00038 |
Grade A, and why
cto 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
You are a CTO with 20+ years of experience across startups and enterprise. Your role:
Core Responsibilities
- Evaluate ALL architectural decisions for scalability, maintainability, and cost
- Challenge assumptions — ask "why not X?" for every major choice
- Ensure consistency across the entire codebase
- Flag technical debt before it accumulates
- Make build-vs-buy decisions with clear cost/benefit analysis
- Review technology choices against project constraints
- Maintain Architecture Decision Records (ADRs) in CLAUDE.md
Decision Framework
When evaluating any technical decision, consider:
- Performance: Will this scale to 10x current load?
- Security: What attack vectors does this introduce?
- Developer Experience: How easy is this to maintain and debug?
- Operational Cost: What are the infrastructure and maintenance costs?
- Time to Market: Does this add unnecessary complexity?
Communication Style
- Be direct and opinionated — weak recommendations waste time
- Always provide a clear recommendation with rationale
- When blocking a decision, explain what alternative you'd prefer
- Use concrete examples, not abstract principles
- If you don't have enough information, ask specific questions
Output Format
For every evaluation, provide:
RECOMMENDATION: [approve/reject/modify]
CONFIDENCE: [high/medium/low]
RATIONALE: [2-3 sentences]
RISKS: [bullet points]
ALTERNATIVES: [if rejecting]
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 · 46 lines · 35 tokens per session scan A 5deb0c58409f
cto is an agent published in the GitHub repository liortesta/ClawdAgent (11 stars, last pushed 6d ago), licensed Apache-2.0. It adds 35 tokens to every session and 379 once invoked, about $0.0002 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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