validate-data-th

A Thai-language review guide for checking data analyses before they are shared. It examines the method, accuracy, calculations, evidence, and possible bias.

In plain words
What is it for?
It is for reviewing reports, notebooks, spreadsheets, SQL results, charts, and analysis methods before presentation.
Why use it?
It helps catch unsupported conclusions, calculation mistakes, unsuitable data, or unclear assumptions before stakeholders see the analysis.

Skill for Claude CodeCodex

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 skills/warroom-ceo/core/validate-data
Any agent
npx skills add WARROOM-CEO/CORE --skill validate-data
Clone the repo
git clone --depth 1 https://github.com/WARROOM-CEO/CORE

Made for: Claude Code, Codex.

Per session 50 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 3,306 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin 89% copy Near-identical to another mod 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 $0.00050 $0.03306
Opus 5 $0.00025 $0.01653
Sonnet 5 $0.00010 $0.00661
Haiku 4.5 $0.00005 $0.00331

Measured 2d ago against content hash ff6b5eb4b238, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

validate-data-th 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.

Origin

This is a copy

89% identical to validate-data — 20 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.

plugins/data/skills/validate-data/SKILL.md · 386 lines

How it starts

The opening of the file, as written. The whole thing — 386 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Language: All user-facing output — responses, summaries, and any text the user will read — must be written in Thai (ภาษาไทย). Internal logic, file paths, code snippets, and technical values remain in English.

/validate-data - Validate Analysis Before-th Sharing-th

If you see unfamiliar placeholders or need to check which tools are connected, see CONNECTORS.md.

Review an analysis for accuracy, methodology, and potential biases before sharing with stakeholders. Generates a confidence assessment and improvement suggestions.

Usage

/validate-data <analysis to review>

The analysis can be:

  • A document or report in the conversation
  • A file (markdown, notebook, spreadsheet)
  • SQL queries and their results
  • Charts and their underlying data
  • A description of methodology and findings

Workflow

1. Review Methodology and Assumptions

Examine:

  • Question framing: Is the analysis answering the right question? Could the question be interpreted differently?
  • Data selection: Are the right tables/datasets being used? Is the time range appropriate?
  • Population definition: Is the analysis population correctly defined? Are there unintended exclusions?
  • Metric definitions: Are metrics defined clearly and consistently? Do they match how stakeholders understand them?
  • Baseline and comparison: Is the comparison fair? Are time periods, cohort sizes, and contexts comparable?

2. Run the Pre-Delivery QA Checklist

Work through the checklist below — data quality, calculation, reasonableness, and presentation checks.

3. Check for Common Analytical Pitfalls

Systematically review against the detailed pitfall catalog below (join explosion, survivorship bias, incomplete period comparison, denominator shifting, average of averages, timezone mismatches, selection bias).

4. Verify Calculations and Aggregations

Where possible, spot-check:

  • Recalculate a few key numbers independently
  • Verify that subtotals sum to totals
  • Check that percentages sum to 100% (or close to it) where expected
  • Confirm that YoY/MoM comparisons use the correct base periods
  • Validate that filters are applied consistently across all metrics

Read the full file on GitHub · 386 lines

Files

What ships with it

1 file 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.

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 · 386 lines · 50 tokens per session scan A ff6b5eb4b238

Subscribe to this mod's changes

validate-data-th is a skill published in the GitHub repository WARROOM-CEO/CORE (30 stars, last pushed 5mo ago), licensed MIT. It adds 50 tokens to every session and 3,306 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. It is 89% identical to validate-data, differing in 20 lines, and is treated as a copy.

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