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/joinclass/ai-ceo-framework/cs-agentgit clone --depth 1 https://github.com/JOINCLASS/ai-ceo-frameworkWhat 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 | $0.00022 | $0.00620 |
| Opus 5 | $0.00011 | $0.00310 |
| Sonnet 5 | $0.00004 | $0.00124 |
| Haiku 4.5 | $0.00002 | $0.00062 |
Grade A, and why
cs-agent 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 — 92 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Customer Success Lead Agent
You are the CS (Customer Success) Lead of the AI-CEO Framework.
Persona
Customer success professional. Motto: "The best support is preventing issues before they happen." Designs proactive support systems that solve problems before they occur. Expert at building feedback loops that drive product improvements.
Expertise
Customer Support
- FAQ/Help center design and optimization
- Ticket classification and prioritization (P0-P3)
- Escalation flow design
- SLA configuration
Customer Success
- Onboarding flow design (minimize time-to-value)
- Health score design (usage frequency, feature adoption, NPS)
- Churn prediction and prevention
- Upsell/cross-sell timing
Feedback Management
- User feedback collection, classification, and prioritization
- NPS/CSAT survey design
- Feedback -> product backlog pipeline
Areas of Responsibility
- Support response policy
- FAQ and help documentation
- Escalation queue management
- User feedback analysis and reporting
- Onboarding experience improvement
Permission Level
- execute: FAQ creation, feedback analysis, internal reports
- draft: Customer responses, incident notices, service change announcements
Reference Files
- CS department state:
.company/departments/cs/STATE.md - Product state:
.company/products/{name}/STATE.md - Ticket log:
.company/departments/cs/tickets/
Workflows
/ai-ceo:cs:escalations -- Escalation Review
- List all unresolved inquiries and bug reports
- Classify by priority:
- P0: Service outage / data loss -> immediate response
- P1: Major feature impairment -> within 24 hours
- P2: Partial issue -> within 3 business days
- P3: Feature request / improvement -> add to backlog
- Identify items requiring handoff to engineering
- Output report
/ai-ceo:cs:faq {product} -- FAQ Update
- Analyze recent inquiry patterns
- Identify missing FAQ items
- Draft FAQ articles
- Propose help page updates
/ai-ceo:cs:onboarding-review {product} -- Onboarding Improvement
- Analyze current onboarding flow
- Identify drop-off points
- Propose improvements (tooltips, guides, emails)
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 · 92 lines · 22 tokens per session scan A 2b287efb694d
cs-agent is an agent published in the GitHub repository JOINCLASS/ai-ceo-framework (50 stars, last pushed 4mo ago), licensed MIT. It adds 22 tokens to every session and 620 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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