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 skills add AgenticAIPlan/AgenticAISkills --skill hubei-ai-ecosystem-lead-scoutgit clone --depth 1 https://github.com/AgenticAIPlan/AgenticAISkillsWrote 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/skills/agenticaiplan/agenticaiskills/hubei-ai-ecosystem-lead-scout)<a href="https://agentmods.dev/skills/agenticaiplan/agenticaiskills/hubei-ai-ecosystem-lead-scout"><img src="https://agentmods.dev/badge/skills/agenticaiplan/agenticaiskills/hubei-ai-ecosystem-lead-scout/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/skills/agenticaiplan/agenticaiskills/hubei-ai-ecosystem-lead-scout"><img src="https://agentmods.dev/badge/skills/agenticaiplan/agenticaiskills/hubei-ai-ecosystem-lead-scout.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 | $0.00086 | $0.02871 |
| Opus 5 | $0.00043 | $0.01435 |
| Sonnet 5 | $0.00017 | $0.00574 |
| Haiku 4.5 | $0.00009 | $0.00287 |
Grade A, and why
hubei-ai-ecosystem-lead-scout 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 12d 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 — 318 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Hubei AI Ecosystem Lead Scout
Overview
This skill standardizes how to collect and judge enterprise leads in Hubei for AI ecosystem cooperation. It is built for regional ecosystem operations rather than pure sales prospecting, so the core output is a judgment on AI demand, scene fit, and partnership potential.
Use this skill when you need to:
- find Hubei or Wuhan enterprises worth researching
- analyze whether an enterprise has AI application scenarios
- judge whether an enterprise is a potential ecosystem partner
- classify leads as key account, ecosystem partner, technical case, or hold
- turn messy research notes into a consistent lead sheet or SOP output
If the user provides a raw enterprise list and wants a standard template quickly, use scripts/build_lead_template.py.
Workflow
-
Confirm the task target. Determine whether the user needs enterprise discovery, lead analysis, table cleanup, partner classification, or SOP generation.
-
Gather only the minimum enterprise facts first. Start with enterprise name, city, industry, core products or business, enterprise scale, and technical or R&D capability if available.
-
Focus on AI-scene evidence, not broad company profiling. Check recent public signals such as official news,公众号内容, activity participation, product updates, partner introductions, hiring, and digital transformation messaging.
-
Judge partnership value using the two hard gates. An enterprise is a strong candidate only if both are broadly true:
- it has a plausible AI application scenario
- it has a technical team or technical carrying capacity
If the scenario is good but the technical team is unclear, do not discard it immediately. Mark it as observation or light-touch communication.
- Classify the enterprise. Use exactly one primary classification:
大客户: large scale, strong regional influence, demo effect, and clear AI cooperation scenes生态伙伴: has AI demand or cooperation basis and can join project, technology, training, activity, or ecosystem collaboration技术案例: an outstanding ecosystem partner with representative AI scenarios and external storytelling value暂不跟进: no clear AI scene, weak technical basis, insufficient information, unclear timing, or poor fit
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 12d ago First seen · 318 lines · 86 tokens per session scan A c23023e2a700
hubei-ai-ecosystem-lead-scout is a skill published in the GitHub repository AgenticAIPlan/AgenticAISkills (11 stars, last pushed 3mo ago), licensed MIT. It adds 86 tokens to every session and 2,871 once invoked, about $0.0004 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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