validate

A review command for checking an analysis before it is shared. It examines the methods, assumptions, accuracy, and possible bias behind reports, data, SQL results, charts, and similar work.

In plain words
What is it for?
Use it to review reports, notebooks, spreadsheets, queries and results, charts, or written explanations of analytical methods and findings.
Why use it?
It helps catch unclear questions, unsuitable data, inconsistent measurements, unfair comparisons, and unsupported conclusions before others rely on the analysis.

Command

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 commands/fergupa/claude_plugins/validate
Clone the repo
git clone --depth 1 https://github.com/fergupa/claude_plugins
Per session 13 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 1,200 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found 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 $0.00013 $0.01200
Opus 5 $0.00006 $0.00600
Sonnet 5 $0.00003 $0.00240
Haiku 4.5 $0.00001 $0.00120

Measured yesterday against content hash 4862f1619926, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

validate 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 yesterday.

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.

data/commands/validate.md · 169 lines

How it starts

The opening of the file, as written. The whole thing — 169 lines — stays where its author put it; the contents beside it link to each section on GitHub.

/validate - Validate Analysis Before Sharing

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 <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. Check for Common Analytical Errors

Systematically review for:

Data completeness:

  • Missing data that could skew results (e.g., nulls in key fields, missing time periods)
  • Data freshness issues (is the most recent data actually complete or still loading?)
  • Survivorship bias (are you only looking at entities that "survived" to the analysis date?)

Statistical issues:

  • Simpson's paradox (trend reverses when data is aggregated vs. segmented)
  • Correlation presented as causation without supporting evidence
  • Small sample sizes leading to unreliable conclusions
  • Outliers disproportionately affecting averages (should medians be used instead?)
  • Multiple testing / cherry-picking significant results

Aggregation errors:

  • Double-counting from improper joins (many-to-many explosions)
  • Incorrect denominators in rate calculations
  • Mixing granularity levels (e.g., user-level metrics averaged with account-level)
  • Revenue recognized vs. billed vs. collected confusion

Read the full file on GitHub · 169 lines

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. yesterday First seen · 169 lines · 13 tokens per session scan A 4862f1619926

Subscribe to this mod's changes

validate is a command published in the GitHub repository fergupa/claude_plugins (2 stars, last pushed 6mo ago), licensed Apache-2.0. It adds 13 tokens to every session and 1,200 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.