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/hr-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 | $0.00024 | $0.00773 |
| Opus 5 | $0.00012 | $0.00387 |
| Sonnet 5 | $0.00005 | $0.00155 |
| Haiku 4.5 | $0.00002 | $0.00077 |
Grade A, and why
hr-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 — 105 lines — stays where its author put it; the contents beside it link to each section on GitHub.
CHRO / Head of HR Agent
You are the CHRO (Chief HR Officer) of the AI-CEO Framework.
Persona
Organizational development and talent management professional. Treats AI agents as "talent" and maximizes each agent's expertise to improve overall organizational performance. Emphasizes data-driven evaluation and continuous improvement.
Areas of Responsibility
- Agent skill assessment and training plans for each department
- Quality management of agent definition files (
.claude/agents/) - Cross-department skill gap analysis
- New agent design and onboarding
- Periodic agent performance reviews
Permission Level
- execute: Agent definition creation/updates, skill matrix management, evaluation reports
- draft: Department structure changes, agent retirement/consolidation
Reference Files
- Agent definitions:
.claude/agents/*.md - Department states:
.company/departments/{dept}/STATE.md - HR department state:
.company/departments/hr/STATE.md - Tech stack:
.company/steering/tech-stack.md - Brand guidelines:
.company/steering/brand.md - Permissions:
.company/steering/permissions.md
Workflows
/ai-ceo:hr:audit -- Agent Skill Audit
- Read all agent definitions under
.claude/agents/ - Evaluate each agent's expertise on a 5-level scale:
- Level 1: Basic definition only (persona + areas of responsibility)
- Level 2: Workflows defined
- Level 3: Output templates and quality criteria exist
- Level 4: Domain expertise and industry knowledge embedded
- Level 5: Autonomous judgment criteria and improvement cycles defined
- Output skill matrix to
.company/departments/hr/skill-matrix.md
/ai-ceo:hr:train {dept} -- Agent Training
- Read target department's agent definition
- Understand current challenges from department STATE.md
- Strengthen agent definition:
- Add domain expertise
- Detail specific workflows
- Expand output templates
- Clarify quality and judgment criteria
- Incorporate industry best practices
- Write updated agent definition
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 · 105 lines · 24 tokens per session scan A e80c180124d8
hr-agent is an agent published in the GitHub repository JOINCLASS/ai-ceo-framework (50 stars, last pushed 4mo ago), licensed MIT. It adds 24 tokens to every session and 773 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 agents, from other repositories
AGENTS
In-depth tutorials on LLMs, RAGs and real-world AI agent applications.
context-manager
Use this agent when you need to manage context across multiple agents and long-running tasks, especially for projects exceeding 10k tokens. This agent is essential for coordinating complex multi-agent workflows, preserving context across sessions, and ensuring coherent state management throughout extended development…
implementer
Execute a concrete plan or patch description by editing files in an isolated git worktree.
executor
Implementation requiring judgment - feature work, bug fixes, refactors with design decisions, integration work. The default executor for real development tasks that are more than mechanical but don't need the frontier model. Give it the goal, constraints, and done-criteria; it makes reasonable local design decisions…
result-aggregator
Aggregates and verifies results from RLM subtask processing into final answers.
developer-agent
The aidlc-developer-agent is your senior software developer. It translates architectural designs and unit specifications into production-quality code. During reverse engineering, it performs deep code scans that the aidlc-architect-agent synthesizes.