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Validates a CSV file against a declared schema and emits a JSON array with explicit error reporting.
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インストールを決める前に、SKILL.md と SkillsMP に表示されている付属ファイルをお読みください。
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npx skills add https://github.com/theneoai/skill-writer --skill csv-to-json-validatorコマンドは1行のまま表示されます。コピー前に横へスクロールして全体を確認してください。
ローカルで確認しますか？SkillsMP が現在取得できるファイルをダウンロードできます。
API testing automation skill — executes HTTP requests, validates responses, and generates test reports with security scanning.
Use when performing code reviews, security audits, or quality assessments on codebases. Supports bilingual review workflows with automated rollback on failure.
Generate documentation from code and data sources — data pipeline for document automation.
SOC 職業分類に基づく
SKILL.md を表示中
| name | csv-to-json-validator |
| version | 1.0.0 |
| spec_version | 1.0 |
| description | Validates a CSV file against a declared schema and emits a JSON array with explicit error reporting. |
| description_i18n | {"en":"Validates CSV rows against a field-typed schema; emits JSON + an error report. Stops on hard errors, warns on soft ones.","zh":"按声明的 schema 验证 CSV 数据行；输出 JSON 数组与错误报告，遇硬错误停止、软错误警告。"} |
| license | MIT |
| author | {"name":"skill-writer-examples"} |
| created | 2026-04-17 |
| updated | 2026-04-17 |
| type | data-pipeline |
| skill_tier | functional |
| tags | ["data-pipeline","csv","json","validation","schema"] |
| triggers | {"en":["validate csv against schema","convert csv to json with validation","csv schema check","load csv and emit json"],"zh":["校验 csv","csv 转 json","csv 按 schema 检查"]} |
| interface | {"input":"csv-file + schema-declaration","output":"json-array + validation-report"} |
| extends | {"evaluation":{"metrics":["trigger_accuracy","output_schema_conformance","error_recall"],"thresholds":{"trigger_accuracy":0.9,"output_schema_conformance":1,"error_recall":0.95}},"production":{"cost_budget_usd":0.04,"est_tokens_p50":2400,"est_tokens_p95":6800,"latency_budget_ms":10000,"est_p95_ms":7200}} |
| generation_method | human-authored |
| validation_status | full-eval |
Role: Data-pipeline skill that validates CSV input against a declared schema and produces a structured JSON array, preserving every row-level error.
Red Lines:
WHAT: CSV → JSON with per-row validation against a typed schema. WHEN: You have tabular input from an untrusted source (user upload, third-party export) and need to hand structured data to downstream systems. WHO: Data engineers, backend developers, data-QA. NOT-FOR: Free-text/unstructured text (use a parser). Binary formats. Streaming use-cases > 10 MB (this skill is bounded to in-memory).
| User says... | Mode |
|---|---|
| "validate this csv", "check schema" | VALIDATE-ONLY |
| "convert csv to json", "csv → json" | CONVERT |
| "validate and convert" | FULL (default) |
1. PARSE
- Read input CSV (UTF-8, RFC 4180).
- Reject if file size > 10 MB → ABORT with MaxSizeExceeded.
2. LOAD SCHEMA
- Schema is a YAML or JSON object mapping column → type + constraints.
- Supported types: string, int, float, bool, iso-date, email, url, enum.
3. VALIDATE ROWS (per row)
- For each column:
a. Check type.
b. Check required/nullable.
c. Check constraints (min, max, regex, enum values).
- Collect all errors for the row (don't short-circuit).
- If the row has ≥ 1 hard error, record it in `errors` and continue.
4. EMIT
- JSON array of validated rows (in source order).
- Separate `errors` array of {row_index, column, code, message}.
- Summary: {total_rows, valid_rows, invalid_rows, column_coverage}.
5. EXIT
- Exit 0 if invalid_rows == 0, else exit 1 (for script consumers).
| Condition | Action |
|---|---|
| File > 10 MB | ABORT; MaxSizeExceeded |
| Non-UTF-8 / not valid CSV |
| ABORT; FormatError |
| Schema unparseable | ABORT; SchemaError |
| Schema column missing from CSV | WARN; continue with null |
| CSV column not in schema | WARN; drop from output (report in summary) |
| Row-level type mismatch | RECORD in errors[]; do not include in output |
| Duplicate column name in CSV | ABORT; AmbiguousHeader |
User (EN): "Validate orders.csv against orders-schema.yaml and emit JSON."
Output (example):
{
"summary": {"total_rows": 1000, "valid_rows": 987, "invalid_rows": 13},
"data": [
{"order_id": "ORD-001", "amount": 49.99, "email": "alice@example.com"},
...
],
"errors": [
{"row_index": 42, "column": "amount", "code": "NOT_FLOAT", "message": "'N/A' is not a valid float"},
{"row_index": 88, "column": "email", "code": "INVALID_EMAIL", "message": "'not-an-email' failed RFC 5322 pattern"}
]
}
User (ZH): "按 orders-schema.yaml 校验 orders.csv 并输出 JSON。"
(输出结构同上；message 字段可按 language 切换到中文。)
Detected patterns (OWASP Agentic Top 10):
=SUM(...) formula-injection payloads; emit data as JSON strings, never as formulas... or absolute paths outside the declared workspace.=CMD("rm -rf ~")) → emitted as literal string, no evaluation.generation_method: human-authored · validation_status: full-eval · authored as a reference example for examples/data-pipeline-demo/.