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data-transformer
Converts data between structured formats (JSON, CSV, YAML, TOML, XML) with schema mapping and validation
用 Codex 或 Claude 帮你安装 复制这段 Prompt,粘贴到 Codex、Claude 或其他助手里,让它检查 Skill 页面并帮你完成安装。
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Converts data between structured formats (JSON, CSV, YAML, TOML, XML) with schema mapping and validation
用 Codex 或 Claude 帮你安装 复制这段 Prompt,粘贴到 Codex、Claude 或其他助手里,让它检查 Skill 页面并帮你完成安装。
基于 SOC 职业分类
| name | data-transformer |
| description | Converts data between structured formats (JSON, CSV, YAML, TOML, XML) with schema mapping and validation |
| version | 1.0.0 |
| author | go-on-team |
| tags | ["data","conversion","json","csv","yaml","toml","xml","etl"] |
| min_go_on_version | 1.0.0 |
Converts structured data between common serialization formats (JSON, CSV, YAML, TOML, XML) with optional field mapping, type coercion, filtering, sorting, and validation. Supports nested flattening, array expansion, and custom delimiter configuration.
| Parameter | Type | Description |
|---|---|---|
input | string | Source data as a string |
input_format | string | Input format: json, csv, yaml, toml, xml |
output_format | string | Output format: json, csv, yaml, toml, xml |
field_mapping | object | Optional: map of {source_field: target_field} renames |
filter | string | Optional: filter expression (e.g. age > 18) |
pretty | boolean | Optional: pretty-print output (default: true) |
{
"input": "name,age,email\nAlice,30,alice@example.com\nBob,25,bob@example.com\nCharlie,35,charlie@example.com",
"input_format": "csv",
"output_format": "json",
"pretty": true
}
Example output:
[
{ "name": "Alice", "age": 30, "email": "alice@example.com" },
{ "name": "Bob", "age": 25, "email": "bob@example.com" },
{ "name": "Charlie", "age": 35, "email": "charlie@example.com" }
]
Input (JSON with nested address object) → output (flat CSV with address.street, address.city columns):
{
"input": "[{\"name\":\"Alice\",\"address\":{\"street\":\"123 Main\",\"city\":\"Springfield\"}}]",
"input_format": "json",
"output_format": "csv",
"field_mapping": { "address.street": "street", "address.city": "city" }
}
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Analyzes code for performance bottlenecks including N+1 queries, O(n^2) or worse algorithms, unnecessary allocations, sync I/O in async contexts, excessive cloning, missing caching opportunities, and large payload transfers. Supports Rust, Python, TypeScript, and Go.
Analyzes, improves, and restructures LLM prompts for clarity, efficiency, and reliability
Analyzes source code for common security vulnerabilities including SQL injection, XSS, command injection, hardcoded secrets, insecure deserialization, path traversal, and SSRF