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.
npx agentmods add skills/jansenanalytics/claudex/data-validatornpx skills add JansenAnalytics/claudex --skill data-validatorgit clone --depth 1 https://github.com/JansenAnalytics/claudexWhat 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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00059 | $0.01031 |
| Opus 5 | $0.00030 | $0.00515 |
| Sonnet 5 | $0.00012 | $0.00206 |
| Haiku 4.5 | $0.00006 | $0.00103 |
Grade A, and why
data-validator 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.
How it starts
The opening of the file, as written. The whole thing — 108 lines — stays where its author put it; the contents beside it link to each section on GitHub.
data-validator
Autonomous data quality validation for any data source — databases, CSVs, APIs, JSON files.
Quick Start
data-validator /path/to/data.csv --infer # Auto-detect schema & validate
data-validator /path/to/data.csv --schema s.json # Validate against schema
data-validator /path/to/data.csv --full # All checks including timeseries
Binary: ~/bin/data-validator
data-validator [command] <source> [options]
Commands
| Command | Description |
|---|---|
review (default) |
Full orchestrated validation |
schema |
Schema validation / inference |
quality |
Data quality scoring |
compare |
Diff two datasets |
integrity |
Referential integrity checks |
timeseries |
Time-series specific analysis |
report |
Generate report from results JSON |
Sources
file.csv— CSV with headersfile.json— JSON array of objectsfile.jsonl/file.ndjson— Newline-delimited JSONsqlite:/path/to/db.sqlite:tablename— SQLite tablehttp://api/endpoint— JSON API
Options
| Option | Description |
|---|---|
--schema FILE |
Schema JSON to validate against |
--infer |
Auto-infer schema from data |
--compare FILE2 |
Compare with second dataset |
--timeseries |
Run time-series analysis |
--time-field NAME |
Specify time field |
--primary-key NAME |
Primary key field (default: id) |
--quick |
Schema + quality only |
--full |
All checks |
--output-dir DIR |
Write individual reports to directory |
--format md|json |
Output format |
--output FILE |
Output file (per-script) |
Schema Format
{
"fields": {
"id": { "type": "integer", "required": true, "unique": true, "min": 1 },
"email": { "type": "string", "required": true, "pattern": "^[^@]+@[^@]+$" },
"amount": { "type": "number", "min": 0, "max": 1000000 },
"status": { "type": "enum", "values": ["active", "inactive", "pending"] },
"created_at": { "type": "date", "format": "ISO8601", "after": "2020-01-01" },
"tags": { "type": "array", "minLength": 1, "itemType": "string" }
}
}
What ships with it
8 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 2d ago First seen · 108 lines · 59 tokens per session scan A 11d6188ca4fa
data-validator is a skill published in the GitHub repository JansenAnalytics/claudex (5 stars, last pushed 2mo ago), licensed MIT. It adds 59 tokens to every session and 1,031 once invoked, about $0.0003 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 2026-08-31.
Other skills, from other repositories
data-engineer
Builds data infrastructure — ETL/ELT pipelines, data warehousing, stream processing, data quality, orchestration (Airflow/Dagster), and analytics engineering (dbt). Use when the user asks to build data pipelines, set up ETL/ELT workflows, design a data warehouse, configure stream processing, or implement analytics…
analytics-audit
Audit analytics implementations for tracking gaps and data quality issues.
analyzing-network-flow-data-with-netflow
Parse NetFlow v9 and IPFIX records to detect volumetric anomalies, port scanning, data exfiltration, and C2 beaconing patterns. Uses the Python netflow library to decode flow records, builds traffic baselines, and applies statistical analysis to identify flows with abnormal byte counts, connection durations, and…
info-funnel
3-6 阶递减漏斗, 突出转化率 / 筛选比例 / 流量损耗。竖图适合 IG Story / 小红书.
build-with-tinybase
Scaffold, extend, and verify reactive local-first JavaScript or TypeScript applications with TinyBase. Use when choosing TinyBase for in-memory tabular or key-value state, generating an app with create-tinybase, adding schemas or UI bindings, configuring browser or database persistence, configuring MergeableStore…
docetl
Build and run LLM-powered data processing pipelines with DocETL. Use when users say "docetl", want to analyze unstructured data, process documents, extract information, or run ETL tasks on text. Helps with data collection, pipeline creation, execution, and optimization.