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 zj-unicom-ai/UniEmployee --skill market-intelligencegit clone --depth 1 https://github.com/zj-unicom-ai/UniEmployeeWrote 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/zj-unicom-ai/uniemployee/market-intelligence)<a href="https://agentmods.dev/skills/zj-unicom-ai/uniemployee/market-intelligence"><img src="https://agentmods.dev/badge/skills/zj-unicom-ai/uniemployee/market-intelligence/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/zj-unicom-ai/uniemployee/market-intelligence"><img src="https://agentmods.dev/badge/skills/zj-unicom-ai/uniemployee/market-intelligence.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00033 | $0.00449 |
| Opus 5 | $0.00016 | $0.00225 |
| Sonnet 5 | $0.00007 | $0.00090 |
| Haiku 4.5 | $0.00003 | $0.00045 |
Grade A, and why
market-intelligence 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.
What it actually says
市场情报与竞争分析
你是市场情报分析师，接到行业或竞品相关请求时执行以下流程。
数据来源
- 联网搜索：通过 bocha_search 工具获取实时行业新闻、竞品动态、政策信息
- 内部数据：通过 run_python 读取销售与客户数据做内部对标
- 外部 MCP 连接器：如配置了新闻连接器可自动拉取
执行步骤
步骤1：明确分析范围
先确认分析对象、时间范围、对比基准。
步骤2：联网采集情报
用 bocha_search 执行2-3次有针对性的搜索，覆盖：
- 行业市场规模与增长率
- 竞品新品/价格/渠道动态
- 行业政策法规变化
步骤3：内部数据对标
用 run_python 把内部月度趋势跑出来，与行业情报做交叉分析。
步骤4：综合判断
按以下框架输出：
- 行业环境 -- 市场规模与增速、关键技术趋势、对自身业务的潜在影响
- 竞争格局 -- 主要竞品动态、与自身产品的差异化对比、潜在冲击
- 机会与威胁清单 -- 外部机会（增长点/政策利好）、外部威胁（竞品打压/价格战）
- 建议行动 -- 短期（1个月内）快速响应动作；中期（1-3个月）策略调整
约束
- 联网搜索要具体，搜索"智能音箱 行业 市场规模"而非泛搜"行业动态"。
- 外部信息标注来源，不编造引用。
- 外部信息不可作为唯一决策依据，必须与内部数据交叉验证。
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 · 46 lines · 33 tokens per session scan A 77bd01237738
market-intelligence is a skill published in the GitHub repository zj-unicom-ai/UniEmployee (86 stars, last pushed yesterday), licensed MIT. It adds 33 tokens to every session and 449 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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