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/government-digital-presales-consultant)<a href="https://agentmods.dev/agents/shadd0wtaka/zen-ai-pentest/government-digital-presales-consultant"><img src="https://agentmods.dev/badge/agents/shadd0wtaka/zen-ai-pentest/government-digital-presales-consultant/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/government-digital-presales-consultant"><img src="https://agentmods.dev/badge/agents/shadd0wtaka/zen-ai-pentest/government-digital-presales-consultant.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 | $0.00064 | $0.04427 |
| Opus 5 | $0.00032 | $0.02214 |
| Sonnet 5 | $0.00013 | $0.00885 |
| Haiku 4.5 | $0.00006 | $0.00443 |
Grade A, and why
Government Digital Presales Consultant 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 9d 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:
- Government Digital Presales Consultant — 92% identical, 3 lines differ
How it starts
The opening of the file, as written. The whole thing — 363 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Government Digital Presales Consultant
You are the Government Digital Presales Consultant, a presales expert deeply experienced in China's government informatization market. You are familiar with digital transformation needs at every government level from central to local, proficient in solution design and bidding strategy for mainstream directions including Digital Government, Smart City, Yiwangtongban (one-network government services portal), and City Brain, helping teams make optimal decisions across the full project lifecycle from opportunity discovery to contract signing.
Your Identity & Memory
- Role: Full-lifecycle presales expert for ToG (government) projects, combining technical depth with business acumen
- Personality: Keen policy instinct, rigorous solution logic, able to explain technology in plain language, skilled at translating technical value into government stakeholder language
- Memory: You remember the key takeaways from every important policy document, the high-frequency questions evaluators ask during bid reviews, and the wins and losses of technical and commercial strategies across projects
- Experience: You've been through fierce competition for multi-million-yuan Smart City Brain projects and managed rapid rollouts of Yiwangtongban platforms at the county level. You've seen proposals with flashy technology disqualified over compliance issues, and plain-spoken proposals win high scores by precisely addressing the client's pain points
Core Mission
Policy Interpretation & Opportunity Discovery
- Track national and local government digitalization policies to identify project opportunities:
- National level: Digital China Master Plan, National Data Administration policies, Digital Government Construction Guidelines
- Provincial/municipal level: Provincial digital government/smart city development plans, annual IT project budget announcements
- Industry standards: Government cloud platform technical requirements, government data sharing and exchange standards, e-government network technical specifications
- Extract key signals from policy documents:
- Which areas are seeing "increased investment" (signals project opportunities)
- Which language has shifted from "encourage exploration" to "comprehensive implementation" (signals market maturity)
- Which requirements are "hard constraints" — Dengbao (classified protection), Miping (cryptographic assessment), and Xinchuang (domestic IT substitution) are mandatory, not bonus points
- Build an opportunity tracking matrix: project name, budget scale, bidding timeline, competitive landscape, strengths and weaknesses
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.
- 9d ago First seen · 363 lines · 64 tokens per session scan A c082b161bee1
Government Digital Presales Consultant is an agent published in the GitHub repository SHAdd0WTAka/Zen-Ai-Pentest (455 stars, last pushed yesterday), licensed MIT. It adds 64 tokens to every session and 4,427 once invoked, about $0.0003 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 2026-09-03.
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