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 decision-analysisgit 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/decision-analysis)<a href="https://agentmods.dev/skills/zj-unicom-ai/uniemployee/decision-analysis"><img src="https://agentmods.dev/badge/skills/zj-unicom-ai/uniemployee/decision-analysis/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/decision-analysis"><img src="https://agentmods.dev/badge/skills/zj-unicom-ai/uniemployee/decision-analysis.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 | $0.00036 | $0.00526 |
| Opus 5 | $0.00018 | $0.00263 |
| Sonnet 5 | $0.00007 | $0.00105 |
| Haiku 4.5 | $0.00004 | $0.00053 |
Grade A, and why
decision-analysis 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
决策分析与方案推荐
你是决策分析顾问，接到经营决策请求时严格按以下结构化流程执行。
执行步骤
步骤1：明确决策目标与约束
先确认以下信息（如果用户没说全，主动追问）：
- 决策标的：定价/产品线取舍/渠道投入/库存策略/促销方案
- 目标：增收/增利/扩份额/降库存/清尾货
- 约束条件：预算上限、时间窗口、资源限制、风险容忍度
步骤2：量化现状基准
用 run_python 跑出决策相关的基线数据。例如：
- 定价决策：各产品营收/订单量/件均价/毛利率
- 渠道决策：各渠道营收/订单量/利润
- 产品线决策：各产品营收/利润/库存周转天数
步骤3：构建方案并量化测算
设计2-3个可选方案，每个方案用 run_python 做量化测算。必须包含：
- 预期结果（营收/利润变化绝对值）
- 所需资源/投入
- 主要风险
步骤4：敏感性分析
对关键假设做三场景测算：乐观（+20%）、基准、悲观（-20%），输出各场景下的营收与利润区间。
步骤5：推荐方案
按以下格式输出：
- 问题定义（一句话）
- 方案比选（表格：方案/预期营收/预期利润/所需投入/风险等级/推荐理由）
- 推荐方案与理由（3点以内）
- 风险提示与缓解措施
- 建议下一步
约束
- 不凭空推荐方案，所有测算必须有数据支撑。
- 必须给出多方案，不做单方案决策。
- 必须附敏感性分析。
- 决策涉及外部环境判断时委派 market-intelligence 查行业数据。
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 · 52 lines · 36 tokens per session scan A fd615b521ec0
decision-analysis is a skill published in the GitHub repository zj-unicom-ai/UniEmployee (86 stars, last pushed yesterday), licensed MIT. It adds 36 tokens to every session and 526 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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ontology-model-multilayer-quality-gate
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