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 simbajigege/book2skills --skill analyzing-financial-statementsgit clone --depth 1 https://github.com/simbajigege/book2skillsWrote 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/simbajigege/book2skills/analyzing-financial-statements)<a href="https://agentmods.dev/skills/simbajigege/book2skills/analyzing-financial-statements"><img src="https://agentmods.dev/badge/skills/simbajigege/book2skills/analyzing-financial-statements/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/simbajigege/book2skills/analyzing-financial-statements"><img src="https://agentmods.dev/badge/skills/simbajigege/book2skills/analyzing-financial-statements.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 | $0.00162 | $0.01725 |
| Opus 5 | $0.00081 | $0.00863 |
| Sonnet 5 | $0.00032 | $0.00345 |
| Haiku 4.5 | $0.00016 | $0.00172 |
Grade A, and why
analyzing-financial-statements 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.
How it starts
The opening of the file, as written. The whole thing — 180 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Skill: 财报透视——识别上市公司资产、利润、现金流质量
知识来源:《一本书读懂财报》肖星著,清华大学经管学院教授,2019年修订版 架构说明:本文件为总调度器(orchestrator),具体分析由各 subskill 独立执行
SKILL.md(总调度器,你在这里)
└── subskills/
├── m1_industry_analysis/ ← 行业背景分析
│ ├── module.md
│ └── references/case_library.md
├── m2_asset_debt_analysis/ ← 资产质量检查
│ ├── module.md
│ └── references/case_library.md
├── m3_profit_analysis/ ← 利润质量验证
├── m4_cashflow_analysis/ ← 现金流健康度
├── m5_solvency_analysis/ ← 周转率与偿债能力
└── m6_roic_analysis/ ← 好公司综合评定
Skill 目的
帮助用户通过三张财务报表识别上市公司的真实质量:
- 资产是否优质:账面资产背后的现实是什么
- 利润是否真实:利润是赚来的还是做出来的
- 现金流是否健康:企业经营的血液流动是否正常
核心原则(贯穿所有 subskill):
- 任何财务数字必须放在行业背景下才有意义
- 分析的终点是"经营事实",不是数字本身
- 关键判断必须用原文案例佐证,案例在各 subskill 的
references/case_library.md中
调用时机
以下任一情况触发本 Skill:
- 用户提供公司名称/代码,询问财务是否健康
- 用户提供财务数据,询问某项指标是否正常
- 用户想识别财报中的风险信号或造假迹象
- 用户想综合评估某家公司的投资价值
路由规则
收到用户问题后,按以下规则决定调用哪些 subskill:
| 用户问题类型 | 必须执行 | 可选执行 |
|---|---|---|
| "这家公司财务健康吗?" / 综合分析 | M1 → M2 → M3 → M4 → M5 → M6 | — |
| "这个行业财务特征是什么?" | M1 | — |
| "资产有没有问题?" / "有无造假风险?" | M1 → M2 | M5 |
| "利润是真实的吗?" | M1 → M3 | M4 |
| "现金流健康吗?" | M4 | M1 |
| "应收账款/存货/固定资产正不正常?" | M1 → M2 | M5 |
| "是好公司吗?值得投资吗?" | M1 → M6 | M3、M5 |
| "债务风险大吗?" | M1 → M5 | M4 |
| "识别财务红旗" | M1 → M2 → M3 → M4 | M5 |
执行规则:
- M1 几乎总是第一步,为其他 subskill 提供行业基准
- 每个 subskill 的输出作为下一个 subskill 的输入
- 用户数据不足时先追问,不用假设填充数据
- 单一问题不全跑,按路由表精确调用
执行方式
调用某个 subskill 时,Read 其 skill.md 文件,按内部步骤执行:
调用 M1 → Read subskills/m1_industry_analysis/module.md
调用 M2 → Read subskills/m2_asset_debt_analysis/module.md
调用 M3 → Read subskills/m3_profit_analysis/module.md
调用 M4 → Read subskills/m4_cashflow_analysis/module.md
调用 M5 → Read subskills/m5_solvency_analysis/module.md
调用 M6 → Read subskills/m6_roic_analysis/module.md
各 subskill 的原文案例在其自己的 references/case_library.md 中,
subskill 执行时会自行 Read 该文件查找引用案例。
多模块联合输出格式
What ships with it
15 files 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.
- analyzing-financial-statements.zip 60 KB
- LICENSE.txt 557 B
- README.md 3.1 KB
- subskills/m1_industry_analysis/module.md 9.6 KB
- subskills/m1_industry_analysis/references/case_library.md 11 KB
- subskills/m2_asset_debt_analysis/module.md 15 KB
- subskills/m2_asset_debt_analysis/references/case_library.md 11 KB
- subskills/m3_profit_analysis/module.md 7.5 KB
- subskills/m3_profit_analysis/references/case_library.md 10 KB
- subskills/m4_cashflow_analysis/module.md 5.1 KB
- subskills/m4_cashflow_analysis/references/case_library.md 11 KB
- subskills/m5_solvency_analysis/module.md 5.4 KB
- subskills/m5_solvency_analysis/references/case_library.md 12 KB
- subskills/m6_roic_analysis/module.md 5.5 KB
- subskills/m6_roic_analysis/references/case_library.md 13 KB
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 · 180 lines · 162 tokens per session scan A ae13c009ac56
analyzing-financial-statements is a skill published in the GitHub repository simbajigege/book2skills (162 stars, last pushed 16d ago), licensed MIT. It adds 162 tokens to every session and 1,725 once invoked, about $0.0008 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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