claude-code

Platform-specific instructions for installing and running a Claude Code skill that reconciles financial data from files. The reconciliation script uses Python libraries and does not use the network while running.

In plain words
What is it for?
Use it to install the skill, run `scripts/reconcile.py` with a configuration file, and reconcile spreadsheets or other configured inputs while exposing only summary results.
Why use it?
It sets boundaries for file access and model input, checks source-file integrity, and requires a data-processing confirmation before writing output.

Agent

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.

agentmods
npx agentmods add agents/cxtx/finance-copilot-skills/claude-code
Clone the repo
git clone --depth 1 https://github.com/cxtx/finance-copilot-skills
Per session 0 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 468 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
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 $0.00000 $0.00468
Opus 5 $0.00000 $0.00234
Sonnet 5 $0.00000 $0.00094
Haiku 4.5 $0.00000 $0.00047

Measured 2d ago against content hash 1a1bf340af3c, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

claude-code 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 2d 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.

skills/fcs-reconcile/agents/claude-code.md · 32 lines

What it actually says

Claude Code 平台专属配置

平台专属适配说明(安装 / 发现 / 调用 / 权限 / 网络限制),不含对账业务逻辑(NFR-08)。 支持级别:格式兼容(未逐项实测),验证项清单见 docs/governance/agent-install.md §4。

安装位置

范围 目标目录
用户级 ~/.claude/skills/fcs-reconcile/
项目级 <项目根>/.claude/skills/fcs-reconcile/

安装方式统一走提示词,见 README「安装」。装完须新开会话才加载。

运行约定

  • 入口scripts/reconcile.py --config <config.yaml>
  • 解释器:必须是装过 requirements.txtpandas / openpyxl / pyyaml)的那一个; 与安装依赖时用的解释器不一致的表现是 ModuleNotFoundError: pandas
  • 网络:运行期禁用——guard.block_network() 在管线第一步执行,外连抛 NetworkDisabledError。仅安装期(装依赖)需要网络。
  • 文件访问:仅用户在配置里指定的输入与输出路径;源文件只读,运行前后 SHA-256 比对; 输出路径覆盖输入时直接拒绝执行。
  • 上交给模型的内容:只有 summary(匹配率、各状态计数、勾稽结论)。 原始明细、DataFrame、标准行不越界,终端也不打印交易明细。

运行前确认

Skill 须先展示数据处理声明(读写文件数、原始数据是否进模型、网络状态、源文件是否修改、 输入完整性校验)并等待用户确认,确认前不落盘。声明格式见 SKILL.md「数据处理声明」。

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. 2d ago First seen · 32 lines · 0 tokens per session scan A 1a1bf340af3c

Subscribe to this mod's changes

claude-code is an agent published in the GitHub repository cxtx/finance-copilot-skills (2 stars, last pushed 23d ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 468 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 2026-08-31.