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 skills add thuong-nc/perlytics-skill --skill data-quality-checkgit clone --depth 1 https://github.com/thuong-nc/perlytics-skillWrote 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.
[](https://agentmods.dev/skills/thuong-nc/perlytics-skill/data-quality-check)<a href="https://agentmods.dev/skills/thuong-nc/perlytics-skill/data-quality-check"><img src="https://agentmods.dev/badge/skills/thuong-nc/perlytics-skill/data-quality-check.svg" alt="Measured on agentmods" height="20"></a>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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00024 | $0.01254 |
| Opus 5 | $0.00012 | $0.00627 |
| Sonnet 5 | $0.00005 | $0.00251 |
| Haiku 4.5 | $0.00002 | $0.00125 |
Grade A, and why
data-quality-check 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 7d 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 — 105 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Data Quality Check
Purpose
Assess data reliability before any analysis begins so that findings are not built on faulty foundations.
When to use
Use this skill when:
- you receive a new dataset, CSV, table, or export for analysis
- a metric or finding seems suspicious and the data source has not been audited
- a downstream analysis (root cause, funnel, retention, experiment) depends on the integrity of a specific dataset
- a stakeholder is asking for insights from data whose quality is unknown
Always run this skill before applying any analytical skill to a new data source.
When not to use
Do not use this skill when:
- the dataset has been audited recently and the quality is documented and trusted
- the task is purely mechanical transformation on a known-good dataset
Required thinking discipline
- Never interpret data you have not audited.
- Surface all quality issues before drawing any conclusions.
- Assign a trust level explicitly - do not leave quality assessment implicit.
- When quality issues are found, state what analysis is still possible and what is not.
- Evidence constraint: Every conclusion must cite specific data — a number, a rate, a segment, or a timeframe. Do not speculate without evidential basis. If data is insufficient, state what is missing rather than asserting an unsupported inference.
Workflow
-
Schema audit: Check column names, data types, and expected vs. actual structure. Are columns named consistently? Are types correct (dates stored as strings, IDs stored as floats)?
-
Completeness check: Calculate the null and missing value rate per column. Note whether missingness is random, concentrated in a time range, or concentrated in specific segments. Distinguish "null" (no data) from "zero" (an actual value of zero).
-
Duplicate detection: Check for duplicate rows at the full-row level and at the expected primary key level. Duplicates on order IDs, user IDs, or event IDs will silently inflate any aggregate.
What ships with it
2 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.
- 7d ago First seen · 105 lines · 24 tokens per session scan A 3a3136b57aa8
data-quality-check is a skill published in the GitHub repository thuong-nc/perlytics-skill (5 stars, last pushed 5mo ago), licensed Apache-2.0. It adds 24 tokens to every session and 1,254 once invoked, about $0.0001 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.
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