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 fdueblab/Micro-Agent --skill financial_risk_modelinggit clone --depth 1 https://github.com/fdueblab/Micro-AgentWrote 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/fdueblab/micro-agent/financial_risk_modeling)<a href="https://agentmods.dev/skills/fdueblab/micro-agent/financial_risk_modeling"><img src="https://agentmods.dev/badge/skills/fdueblab/micro-agent/financial_risk_modeling/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/fdueblab/micro-agent/financial_risk_modeling"><img src="https://agentmods.dev/badge/skills/fdueblab/micro-agent/financial_risk_modeling.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 | $0.00000 | $0.00610 |
| Opus 5.5 | $0.00000 | $0.00244 |
| Sonnet 5.5 | $0.00000 | $0.00122 |
| Haiku 4.5 | $0.00000 | $0.00061 |
Grade A, and why
financial_risk_modeling 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 22d 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
你正在生成一个金融风控类算法模型（欺诈检测/信用评估/可疑交易监测等）。以下是本领域的强制规范，请严格遵循：
数据纪律（需求分析阶段逐项确认）
- 不平衡是常态：正样本（欺诈/违约/可疑）占比通常 <1%。禁止使用 accuracy 作为目标或汇报指标
- 多币种必查：涉及金额时先确认是否多币种，是则先归一化（汇率表快照 + 日期注明），币种未知返回 None 降级、禁止猜默认汇率
- 缺失语义：金融数据缺失本身是信号（如收入未申报），缺失单独成箱/成类，禁止直接填均值/众数
- 聚合维度：交易流水类数据优先构造账户/对手方级聚合特征（笔数、总额、币种数、对手方集中度、时间聚集度）
建模范式
- 有标签（监督）：
LogisticRegression(class_weight="balanced")或树模型；评估用 PR-AUC / KS / Recall@Precision；决策阈值在验证集上按目标召回标定（注明标定依据），禁止硬编码 0.5 - 无标签（异常）：
IsolationForest（contamination 按业务预估欺诈率设置并注明）；或 z-score/分位数统计规则 - 评分卡任务：WOE 编码 + IV 筛选（IV>0.5 优先怀疑标签泄漏）+ 逻辑回归 + 标准分数刻度（base_score/PDO 注明）；分箱边界仅基于训练数据
- 重采样只在训练集做：SMOTE 在 train_test_split 之后、仅对训练集，时序数据禁止 SMOTE
输出契约（监管可审计要求）
每个输出必须携带决策依据：
reason字段：命中了哪条规则/名单、统计偏离的具体数值- 概率/分数与标签同时输出
- 无法判定时：
confidence=0+ 明确"无法判定"原因，禁止无依据默认类别
阈值出处
所有阈值（大额线、风险分界、汇率）必须注释来源：监管文件条款 / 数据统计依据 / 业务方提供，禁止无出处魔数。
汇报口径
model_summary 中并列：模型指标 vs 基线（多数类或简单规则基线），并说明误报/漏报权衡的业务含义。
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.
- 22d ago First seen · 31 lines · 0 tokens per session scan A 3a942defd287
financial_risk_modeling is a skill published in the GitHub repository fdueblab/Micro-Agent (101 stars, last pushed 17d ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 610 tokens. 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 2026-09-25.
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