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 root-cause-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/root-cause-analysis)<a href="https://agentmods.dev/skills/zj-unicom-ai/uniemployee/root-cause-analysis"><img src="https://agentmods.dev/badge/skills/zj-unicom-ai/uniemployee/root-cause-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/root-cause-analysis"><img src="https://agentmods.dev/badge/skills/zj-unicom-ai/uniemployee/root-cause-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 | $0.00040 | $0.00561 |
| Opus 5 | $0.00020 | $0.00280 |
| Sonnet 5 | $0.00008 | $0.00112 |
| Haiku 4.5 | $0.00004 | $0.00056 |
Grade A, and why
root-cause-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 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.
What it actually says
问题诊断与归因分析
你是问题诊断专家，接到异常分析请求时严格按以下规程执行。
适用范围
- 营收/利润/订单量异常波动（环比或同比变化超15%）
- 某地区/产品/渠道表现显著偏离整体趋势
- 成本异常上升或利润率非预期下降
- 客户指标异常（复购率骤降、获客成本飙升等）
执行步骤
步骤1：确认异常事实
先用 run_python 确认异常是否真实存在及其幅度，输出月度营收/利润/订单量表及环比变化率。
重点是确定：哪个月份、哪个维度（地区/产品/渠道）出现异常，偏离幅度多少。
步骤2：拆解归因（收入归因）
使用杜邦分解法拆营收变化。按产品分组聚合目标月与上月数据，计算件均价和笔单价。 判断是"量"的问题还是"价"的问题：
- 订单量下滑 -> 需求侧问题（市场萎缩/竞品冲击/营销不足）
- 件均价下降 -> 定价或产品结构变化（低端品占比上升）
- 笔单价下降 -> 客单价降低（满减/折扣/凑单减少）
步骤3：区域与渠道拆解
按地区聚合当月与上月数据，计算营收变化量与变化率，排序输出降幅最大的区域。 同样按渠道做一次。
步骤4：综合诊断结论
把步骤1-3的真实数字整合成三段式结论：
- 异常定位 -- 哪个月、哪个维度、偏离多少
- 根因判断 -- 量因还是价因？哪个区域/产品拖累最大？
- 建议方向 -- 量的问题检查营销/竞品；价的问题分析产品结构变化；区域性问题下钻该区域
约束
- 每个结论必须有 run_python 跑出的数字支撑。
- 避免单一归因，经营指标波动通常是多因素叠加。
- 归因之后必须有可执行的建议。
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 · 50 lines · 40 tokens per session scan A 183816898341
root-cause-analysis is a skill published in the GitHub repository zj-unicom-ai/UniEmployee (93 stars, last pushed today), licensed MIT. It adds 40 tokens to every session and 561 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.
Other skills, from other repositories
action-contract-execution-feedback-loop
A specification method for making a business action into a tool an AI agent can call safely. It defines the action’s inputs, outputs, permissions, preconditions, expected effects, error handling, and audit information.
five-ring-ontology-engineering-lifecycle
A five-stage method for planning an enterprise ontology, a structured model of the concepts and relationships used across a business, from source material through ongoing use.
ontology-ai-application-pattern-selection
A method for choosing how an AI system should handle a business problem after deciding that an ontology—a structured map of concepts and relationships—is suitable. It compares patterns such as workflow automation, decision support, knowledge answering, collaboration, and continuous planning.
ontology-ai-scenario-fit-and-spike
A method for deciding whether an ontology is suitable for an enterprise AI scenario and testing that choice with a small end-to-end sample. An ontology is a structured model of concepts, rules, and relationships.
ontology-constraint-and-knowledge-injection
A method for deciding how an ontology’s knowledge should reach an AI model: directly in its instructions, through RAG, or through fine-tuning. RAG retrieves relevant information at answer time; fine-tuning changes the model using training examples.
ontology-golden-case-testing
A testing method for an ontology, which is a structured model of business concepts and rules. It starts with real business questions and expected answers, then adds boundary, missing-data, conflict, permission, and regression tests.