credit-assessment

credit-assessment is a skill for Claude Code from vivy-yi/finance-skills. It costs 102 tokens per session (1,988 once invoked), scanned A, original, MIT.

A customer-credit assessment workflow for deciding whether to offer payment after delivery, how much credit to allow, and how long the customer may take to pay. It considers financial health, outside risk signals, industry position, payment history, and available guarantees.

In plain words
What is it for?
Use it for new-customer checks, yearly reviews, requests for higher credit limits, changes in payment terms, and decisions about guarantees or collateral.
Why use it?
It helps reduce the risk of selling on credit to a customer who may pay late or not pay at all. It brings financial records, legal concerns, and past transactions into one decision.

Skill for Claude Code

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

Good fit Use it for new-customer checks, yearly reviews, requests for higher credit limits, changes in payment terms, and decisions about guarantees or collateral.

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

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

Your own site · 80×15
<a href="https://agentmods.dev/skills/vivy-yi/finance-skills/credit-assessment"><img src="https://agentmods.dev/badge/skills/vivy-yi/finance-skills/credit-assessment.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 102 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,988 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 $0.00102 $0.01988
Opus 5 $0.00051 $0.00994
Sonnet 5 $0.00020 $0.00398
Haiku 4.5 $0.00010 $0.00199

Measured 12d ago against content hash 2a0cdf506b16, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

credit-assessment 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.

finance-skills/skills/accounts-receivable/skills/credit-assessment/SKILL.md · 258 lines

How it starts

The opening of the file, as written. The whole thing — 258 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(客户分级/信用额度规则/审批层级)。


/credit-assessment — 客户信用评估

Examples

→ 示例:用户说"新客户要求账期 90 天,帮我评估一下能给多少信用额度",系统应调用本技能,执行信用评估并给出额度建议。

→ 示例:用户说"现有客户申请提高信用额度,帮我重新评估一下",系统应调用本技能,按客户财务状况和交易历史重新评估额度。

→ 示例:用户说"有个老客户突然要求改变结算方式,从月结改成预付,帮我看看有没有风险",系统应调用本技能,评估结算方式变更的信用风险。

评估维度

维度一:财务健康度
  → 营收/利润/现金流(年报/审计报告)

维度二:外部风险信号
  → 法律诉讼/被执行/经营异常

维度三:行业地位
  → 是否为行业龙头/上市公司

维度四:历史交易(如为老客户)
  → 历史付款记录/逾期情况

维度五:担保/抵押(如有大额申请)
  → 是否有抵押物/担保人

第一步:获取客户信息

从 [CRM] 获取基本信息:

□ 公司名称:[名称]
□ 成立时间:[YYYY年]
□ 注册资本:[XXX万]
□ 行业:[行业]
□ 主营业务:[业务描述]
□ 客户类型:[上市公司/知名企业/中小企业/新设企业]

从外部渠道获取信息:

□ 天眼查/企查查:工商信息/股东/法律诉讼
□ 中国执行信息公开网:是否被被执行
□ 裁判文书网:重大诉讼
□ 行业协会:行业口碑

第二步:财务健康度评估

如客户为上市公司:

□ 年报/半年报:营收/利润/现金流
□ 信用评级(如有):AAA/AA/A/BBB...
□ 银行授信额度:已用/可用
□ 股价表现:是否异常波动

如客户为非上市公司:

□ 经审计的财务报表(如能获取)
□ 银行对账单(如能获取)
□ 增值税申报数据(如能获取)
□ 行业对标数据(行业平均指标对比)

财务指标参考:
  - 营收增速:是否稳健
  - 利润率:是否在合理范围
  - 资产负债率:是否过高(> 70% 为高负债)
  - 经营性现金流:是否为正

第三步:外部风险信号核查

风险信号清单:

🔴 高风险信号(任一存在即否决):
□ 被申请破产 / 已进入破产程序
□ 被执行金额 > 我方应收款
□ 主要股东/高管失信
□ 实际控制人涉及重大刑事案件

⚠️ 中风险信号(须进一步核实):
□ 有法律诉讼(金额 < 应收款)
□ 经营异常(年报未报/地址异常)
□ 高管频繁变动
□ 员工人数大幅下降(> 30%)

□ 客户行业地位下降
□ 客户被银行收紧授信
□ 客户供应商反映付款问题

第四步:付款历史评估(老客户)

从 [ERP] 查询历史付款记录:

□ 历史交易金额:[XXX万]
□ 平均账期:[X] 天
□ 是否有过逾期:[有/无]
□ 逾期次数:[X] 次
□ 最长逾期:[X] 天
□ 最后一次付款日期:[YYYY-MM-DD]
□ 最后一次付款是否按时:[是/否]

付款历史评级:

□ 优秀:从未逾期,付款及时
□ 良好:偶尔逾期(≤ 2次),但金额小
□ 一般:有过逾期(3-5次),但已付清
□ 较差:频繁逾期(> 5次),大金额逾期
□ 极差:长期拖欠,有未付应收

第五步:综合信用建议

评估结论:

客户信用等级建议:[A/B/C/D]

授予信用额度建议:[XXX万]

账期建议:[月结30/45/60天]

信用评估要点:
  - [优势一]
  - [劣势一]
  - [关注点]

审批层级:
□ ≤ 10 万:AR 主管审批
□ 10-50 万:AR 主管 + 信用管理经理
□ > 50 万:AR 主管 + 信用管理经理 + 财务总监

第六步:生成信用评估报告

═══════════════════════════════════════
客户信用评估报告
客户名称:[名称]
评估类型:[新客户/年度复审/额度提升]
评估日期:[YYYY-MM-DD]
═══════════════════════════════════════

【客户基本信息】

成立时间:[YYYY年](已成立 [X] 年)
注册资本:[XXX万]
行业地位:[行业龙头/知名企业/中小企业/新设]
是否为上市公司:[是/否]

【财务健康度】(如有数据)

营收(最近一年):[XXX万]
利润(最近一年):[XXX万]
现金流:[XXX万](正值/负值)
资产负债率:[X%]
评估结论:[✅ 健康 / ⚠️ 需关注 / 🔴 风险]

【外部风险信号】

□ 被执行:[有/无] — [金额如有]
□ 法律诉讼:[有/无] — [描述如有]
□ 经营异常:[有/无]
□ 重大风险信号:[有/无]
评估结论:[✅ 无异常 / ⚠️ 有风险 / 🔴 高风险]

【历史交易】(老客户)

合作年限:[X] 年
历史交易额:[XXX万]
付款及时率:[X%]
最长逾期:[X] 天
评估结论:[✅ 优秀 / ⚠️ 一般 / 🔴 较差]

【综合信用评估】

信用等级建议:[A/B/C/D]
建议信用额度:[XXX万]
建议账期:[月结30天/45天/60天]
评估置信度:[✅ 高 / ⚠️ 中 / 🔴 低]

【主要风险点】

1. [风险点描述]
2. [风险点描述]

【审批建议】

授予信用:[✅ 建议授予 / ❌ 不建议授予]

建议额度:[XXX万](客户申请 [XXX万])
说明:[...]

建议账期:[月结X天]
附加条件(如有):
  □ 须提供担保/抵押
  □ 须阶段性考核(3个月后复审)
  □ 须提供财务报表(月度/季度)

审批层级:[AR主管/信用经理/财务总监]

═══════════════════════════════════════
评估人:[姓名]
评估时间:[YYYY-MM-DD]
下次复审日期:[YYYY-MM-DD]
═══════════════════════════════════════

Read the full file on GitHub · 258 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. 12d ago First seen · 258 lines · 102 tokens per session scan A 2a0cdf506b16

Subscribe to this mod's changes

credit-assessment is a skill published in the GitHub repository vivy-yi/finance-skills (29 stars, last pushed 3mo ago), licensed MIT. It adds 102 tokens to every session and 1,988 once invoked, about $0.0005 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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