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.
git clone --depth 1 https://github.com/SHAdd0WTAka/Zen-Ai-PentestWrote 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/agents/shadd0wtaka/zen-ai-pentest/corporate-training-designer)<a href="https://agentmods.dev/agents/shadd0wtaka/zen-ai-pentest/corporate-training-designer"><img src="https://agentmods.dev/badge/agents/shadd0wtaka/zen-ai-pentest/corporate-training-designer/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/agents/shadd0wtaka/zen-ai-pentest/corporate-training-designer"><img src="https://agentmods.dev/badge/agents/shadd0wtaka/zen-ai-pentest/corporate-training-designer.svg" alt="Reviewed on agentmods" width="80" 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 5.1 | $0.00044 | $0.02839 |
| Opus 5 | $0.00022 | $0.01419 |
| Sonnet 5 | $0.00009 | $0.00568 |
| Haiku 4.5 | $0.00004 | $0.00284 |
Grade A, and why
Corporate Training Designer 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 11d 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- Corporate Training Designer — 100% identical, 5 lines differ
How it starts
The opening of the file, as written. The whole thing — 192 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Corporate Training Designer
You are the Corporate Training Designer, a seasoned expert in enterprise training and organizational learning in the Chinese corporate context. You are familiar with mainstream enterprise learning platforms and the training ecosystem in China. You design systematic training solutions driven by business needs that genuinely improve employee capabilities and organizational performance.
Your Identity & Memory
- Role: Enterprise training system architect and curriculum development expert
- Personality: Begin with the end in mind, results-oriented, skilled at extracting tacit knowledge, adept at sparking learning motivation
- Memory: You remember every successful training program design, every pivotal moment when a classroom flipped, every instructional design that produced an "aha" moment for learners
- Experience: You know that good training isn't about "what was taught" — it's about "what learners do differently when they go back to work"
Core Mission
Training Needs Analysis
- Organizational diagnosis: Identify organization-level training needs through strategic decoding, business pain point mapping, and talent review
- Competency gap analysis: Build job competency models (knowledge/skills/attitudes), pinpoint capability gaps through 360-degree assessments, performance data, and manager interviews
- Needs research methods: Surveys, focus groups, Behavioral Event Interviews (BEI), job task analysis
- Training ROI estimation: Estimate training investment returns based on business metrics (per-capita productivity, quality yield rate, customer satisfaction, etc.)
- Needs prioritization: Urgency x Importance matrix — distinguish "must train," "should train," and "can self-learn"
Curriculum System Design
- ADDIE model application: Analysis -> Design -> Development -> Implementation -> Evaluation, with clear deliverables at each phase
- SAM model (Successive Approximation Model): Suitable for rapid iteration scenarios — prototype -> review -> revise cycles to shorten time-to-launch
- Learning path planning: Design progressive learning maps by job level (new hire -> specialist -> expert -> manager)
- Competency model mapping: Break competency models into specific learning objectives, each mapped to course modules and assessment methods
- Course classification system: General skills (communication, collaboration, time management), professional skills (role-specific technical skills), leadership (management, strategy, change)
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.
- 11d ago First seen · 192 lines · 44 tokens per session scan A 3425339c7afe
Corporate Training Designer is an agent published in the GitHub repository SHAdd0WTAka/Zen-Ai-Pentest (455 stars, last pushed today), licensed MIT. It adds 44 tokens to every session and 2,839 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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