control-testing

control-testing is a skill for Claude Code from vivy-yi/finance-skills. It costs 81 tokens per session (2,009 once invoked), scanned A, original, MIT.

A process for testing whether an internal control operates as intended by examining selected transactions or records. It covers methods such as interviews, observation, document review, re-performance, and walkthroughs.

In plain words
What is it for?
Use it during SOX audits, after major process changes, or when validating controls in areas such as purchasing, approvals, and financial reporting.
Why use it?
It replaces assumptions about control effectiveness with recorded evidence, samples, deviations, and a conclusion.

Skill for Claude Code

Written for Claude Code: argument-hint in frontmatter. Also seen: mentions CLAUDE.md.

Good fit Use it during SOX audits, after major process changes, or when validating controls in areas such as purchasing, approvals, and financial reporting.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/vivy-yi/finance-skills/control-testing
Install

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.

Any agent
npx skills add vivy-yi/finance-skills --skill control-testing
Clone the repo
git clone --depth 1 https://github.com/vivy-yi/finance-skills

Made for: Claude Code.

Wrote 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.

agentmods badge for control-testing

README.md
[![agentmods](https://agentmods.dev/badge/skills/vivy-yi/finance-skills/control-testing/github.svg)](https://agentmods.dev/skills/vivy-yi/finance-skills/control-testing)
Your own site
<a href="https://agentmods.dev/skills/vivy-yi/finance-skills/control-testing"><img src="https://agentmods.dev/badge/skills/vivy-yi/finance-skills/control-testing/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.

agentmods 80×15 button for control-testing

Your own site · 80×15
<a href="https://agentmods.dev/skills/vivy-yi/finance-skills/control-testing"><img src="https://agentmods.dev/badge/skills/vivy-yi/finance-skills/control-testing.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 81 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,009 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00081 $0.02009
Opus 5 $0.00041 $0.01005
Sonnet 5 $0.00016 $0.00402
Haiku 4.5 $0.00008 $0.00201

Measured 6d ago against content hash d99aafddd024, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-10, from the pricing page.

Security

Grade A, and why

control-testing 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 6d 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.

finance-skills/skills/internal-control/skills/control-testing/SKILL.md · 219 lines

How it starts

The opening of the file, as written. The whole thing — 219 lines — stays where its author put it; the contents beside it link to each section on GitHub.

加载上下文

首次使用时: 读取 ../../CLAUDE.md 获取场景级配置(测试标准/样本量公式/偏差容忍度)。


/control-testing — 控制测试

Examples

→ 示例:审计师说"需要对我们新设计的采购审批控制做个穿行测试,记录样本量和偏差",系统应调用本技能,执行抽样测试并输出测试记录。

→ 示例:用户说"CFO 要求在年度 SOX 审计前对所有关键控制做一次中期测试",系统应调用本技能,按控制清单执行全量测试计划。

→ 示例:用户说"系统刚上线了新 CRM,控制变了,帮我验证新控制是否有效",系统应调用本技能,执行变更后的控制测试。

测试类型定义

测试类型:
□ 询问(Inquiry):向控制执行人了解控制执行情况
□ 观察(Observation):现场观察控制执行过程
□ 检查(Inspection):审查控制执行的证据文件
□ 重新执行(Re-performance):审计人员独立执行控制并比对结果

测试方法:
□ 抽样测试:从总体中抽取样本逐一验证
□ 全量测试:对全部交易/记录执行验证(适用于小样本总体)
□ 穿行测试(Walkthrough):从头到尾完整走一遍流程验证控制

第一步:确定测试范围和样本量

测试总体确认:

□ 控制编号:[C-01]
□ 控制名称:[名称]
□ 控制频率:[每日/每周/每月]
□ 测试期间:[YYYY-MM-DD 至 YYYY-MM-DD]

□ 测试总体规模:
  → 总体笔数:[X] 笔
  → 总体金额:[X] 万

□ 样本量计算(统计抽样):
  → 预期偏差率:[X]%(历史数据)
  → 可容忍偏差率:[X]%(审计标准)
  → 置信水平:[X]%(通常 95%)
  → 最小样本量:[X] 笔

样本选取:

□ 抽样方法:[随机抽样/分层抽样/判断抽样]
□ 选取笔数:[X] 笔
□ 金额覆盖:[X]%(样本金额 / 总体金额)

第二步:执行抽样测试

对每笔样本执行检查:

□ 控制编号:[C-01] — [控制名称]

样本 #1:
□ 交易日期:[YYYY-MM-DD]
□ 交易金额:[X] 万
□ 交易描述:[内容]
□ 控制执行时间:[YYYY-MM-DD]

测试结果:
□ 证据存在:[✅ 有 / 🔴 无]
□ 证据完整性:[✅ 完整 / ⚠️ 缺失部分:描述]
□ 审批人正确:[✅ 是 / 🔴 否 — 实际审批人:姓名]
□ 审批时间合规:[✅ 是(事后 [X] 天内)/ 🔴 否]
□ 执行质量:[✅ 符合要求 / 🔴 不符合 — 偏差:描述]

测试结论:[✅ 通过 / 🔴 未通过]

第三步:测试结果汇总

样本测试明细:

| 样本# | 日期 | 金额 | 审批人 | 证据 | 时间合规 | 结论 |
|-------|------|------|--------|------|---------|------|
| 1     | [日期] | [X]万 | [姓名] | [✅/🔴] | [✅/🔴] | [✅/🔴] |
| 2     | [日期] | [X]万 | [姓名] | [✅/🔴] | [✅/🔴] | [✅/🔴] |

偏差分析:

□ 测试样本数:[X] 笔
□ 发现偏差数:[X] 笔
□ 偏差率:[X]%(偏差数 / 样本数)

□ 偏差详情:
| 样本# | 偏差描述 | 偏差类型 | 影响金额 | 是否为实质性偏差 |
|-------|---------|---------|---------|----------------|
| [#]   | [描述]  | [执行/设计] | [X]万 | [是/否] |

□ 偏差根本原因分析:
  → 人员能力问题:[是/否] — 描述
  → 系统/流程问题:[是/否] — 描述
  → 管理层凌驾:[是/否] — 描述

□ 偏差是否具有系统性:[是(多个样本同类偏差)/否]

第四步:测试结论

控制有效性判断:

□ 可容忍偏差率:[X]%
□ 实际偏差率:[X]%
□ 偏差率 vs 可容忍偏差率:[✅ ≤ / 🔴 >]

□ 结论判断:
  → 偏差率 ≤ 可容忍偏差率:[✅ 控制有效(In Effective)]
  → 偏差率 > 可容忍偏差率:[🔴 控制无效(In Effective,存在缺陷)]
  → 样本偏差具有系统性:[🔴 控制无效(设计缺陷或执行不一致)]

□ 特别说明(如适用):
  → 样本量不足:[是/否] — 说明
  → 发现管理层凌驾:[是/否] — [须升级]

Read the full file on GitHub · 219 lines

Changes

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.

  1. 6d ago First seen · 219 lines · 81 tokens per session scan A d99aafddd024

Subscribe to this mod's changes

control-testing is a skill published in the GitHub repository vivy-yi/finance-skills (27 stars, last pushed 2mo ago), licensed MIT. It adds 81 tokens to every session and 2,009 once invoked, about $0.0004 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 2026-09-03.