Agent skill

Trustworthy Experiment Insights

by hashgraph-online in hashgraph-online/awesome-codex-plugins

Assess whether experiment results are credible enough to influence product decisions.

MITAuto-check passedDatabases

Install Trustworthy Experiment Insights

skills CLI
$ npx skills add hashgraph-online/awesome-codex-plugins --skill trustworthy-experiment-insights -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install hashgraph-online/awesome-codex-plugins trustworthy-experiment-insights --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/hashgraph-online/awesome-codex-plugins.git skills-src && mkdir -p .claude/skills && cp -r skills-src/plugins/LVTD-LLC/skills/skills/trustworthy-experiment-insights .claude/skills/trustworthy-experiment-insights && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
trustworthy-experiment-insights
GitHub stars
1.3k
Token cost
~833 tokens
SKILL.md length
250 words
Files
6 (incl. references)
Skills in repo
716
Repo updated
First seen
Licence
MIT

At a glance

Assess whether experiment results are credible enough to influence product decisions.

  • Works in 6 steps: Confirm the experiment was operationally… → Check power, practical significance, and… → Look for false positive risk: suspicious… → …
  • Checking false positive
  • SKILL.md covers Source Traceability, Reference Routing, Workflow and Output Format, plus 1 more section
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Trustworthy Experiment Insights is an agent skill from hashgraph-online/awesome-codex-plugins. Assess whether experiment results are credible enough to influence product decisions. Use when checking false positive or false negative risk, underpowered metrics, suspiciously large lifts, replication needs, meta-analysis, stratified sampling, covariate adjustment, or whether A/B test insights should be trusted.

Its SKILL.md is about 830 tokens, which your agent loads only when the skill is triggered. The skill folder holds 8 other files, including reference files (for example `guidelines.md`, `references/core/examples.md` and `references/core/knowledge.md`). Compatibility notes: Codex, Claude Code, and other Agent Skills-compatible clients.

It sits in Databases, covering A/B testing and Database administration. The repository describes itself as: A curated list of awesome OpenAI Codex / ChatGPT plugins, skills, and resources. The 1 Codex Marketplace. See live plugins at: https://hol.org/plugins/best-codex-plugins. The licence is MIT.

When your agent uses it

  • Checking false positive
  • False negative risk
  • Underpowered metrics
  • Suspiciously large lifts

Example prompts

  • “/trustworthy-experiment-insights”

Requirements

  • Compatibility (from SKILL.md): Codex, Claude Code, and other Agent Skills-compatible clients.

Workflow steps

6 steps, taken from the first numbered list in SKILL.md.

  1. Confirm the experiment was operationally valid enough to interpret.
  2. Check power, practical significance, and whether metrics were underpowered.
  3. Look for false positive risk: suspicious lift, many comparisons, early stop,
  4. Look for false negative risk: noisy metrics, small sample, low sensitivity,
  5. Compare with similar experiments or run meta-analysis when available.
  6. Recommend launch, replicate, extend, investigate, or reject the result.

What it can do on your machine

Read from SKILL.md and the folder at commit 3e1456a. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md (its code samples are markdown).

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

  • Compatibility

    Codex, Claude Code, and other Agent Skills-compatible clients.

    From compatibility in the SKILL.md frontmatter.

Context cost

Trustworthy Experiment Insights loads about 833 tokens when it runs, and up to ~2.9k if it reads all its reference files. Until then it costs about 87 tokens; SKILL.md has 250 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~87
When it runs · the whole SKILL.md, loaded when a task matches
~833
With references · SKILL.md plus every file in references/, read only if the agent opens them
~2.9k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from hashgraph-online/awesome-codex-plugins at commit 3e1456a, republished under its MIT licence (© hashgraph-online). 250 words, ~833 tokens.

Download SKILL.mdSave it as .claude/skills/trustworthy-experiment-insights/SKILL.md (or your agent's skills folder). This skill also uses 5 other files; get the full folder from GitHub.
name
trustworthy-experiment-insights
description
Assess whether experiment results are credible enough to influence product decisions. Use when checking false positive or false negative risk, underpowered metrics, suspiciously large lifts, replication needs, meta-analysis, stratified sampling, covariate adjustment, or whether A/B test insights should be trusted.
compatibility
Codex, Claude Code, and other Agent Skills-compatible clients.
license
MIT
metadata.version
0.1.0
metadata.displayName
Trustworthy Experiment Insights
metadata.category
Product Management
metadata.tags
practical-ab-testing,next-level-ab-testing,ab-testing,experimentation,analytics

Trustworthy Experiment Insights

Use this skill to decide whether an experiment result is believable enough to shape a product or engineering decision. It focuses on false positives, false negatives, power, replication, meta-analysis, stratified sampling, covariate adjustment, and suspicious result review.

Source Traceability

Primary source: Next-Level A/B Testing by Leemay Nassery. Guidance is transformed and paraphrased from Chapter 6 on false positives and negatives, meta-analysis, metric sensitivity, stratified random sampling, covariate adjustments, replication, longer runs, and statistical power.

Related skills:

  • ab-test-results-readout for standard experiment reporting.
  • experiment-sensitivity-optimization for improving precision before or during experiment design.
  • experiment-verification-monitoring for operational validity checks.

Reference Routing

NeedRead
Insight-quality conceptsreferences/core/knowledge.md
Credibility and follow-up rulesreferences/core/rules.md
Result-review scenariosreferences/core/examples.md
Step-by-step credibility reviewworkflows/review-experiment-credibility.md

Workflow

  1. Confirm the experiment was operationally valid enough to interpret.
  2. Check power, practical significance, and whether metrics were underpowered.
  3. Look for false positive risk: suspicious lift, many comparisons, early stop, weak prior, or contradiction with prior experiments.
  4. Look for false negative risk: noisy metrics, small sample, low sensitivity, or over-broad metric choice.
  5. Compare with similar experiments or run meta-analysis when available.
  6. Recommend launch, replicate, extend, investigate, or reject the result.

Output Format

markdown
# Experiment Insight Credibility Review

## Result Under Review
[Experiment, metric, observed result, and proposed decision.]

## Credibility Assessment
[Trust | Trust with caveats | Replicate | Extend | Investigate | Do not trust]

## Evidence
| Check | Finding | Risk |
|-------|---------|------|

## Follow-Up
- Replication needed:
- Longer run needed:
- Meta-analysis/comparison:
- Variance reduction opportunity:

## Decision Guidance
[What decision can be made now, and what should wait.]

Quality Bar

  • Do not celebrate a result before checking whether it could be a false positive.
  • Do not dismiss a flat result before checking power and sensitivity.
  • Do not compare against prior experiments without noting differences in population, metric, design, and timing.
  • Do not use statistical checks to hide operational failures; verify experiment health first.

© hashgraph-online, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 5 other files (references) in plugins/LVTD-LLC/skills/skills/trustworthy-experiment-insights of hashgraph-online/awesome-codex-plugins.

  • SKILL.md
  • guidelines.md
  • references/core/examples.md
  • references/core/knowledge.md
  • references/core/rules.md
  • workflows/review-experiment-credibility.md

Open the folder on GitHubat commit 3e1456a

Compare with similar skills

Trustworthy Experiment Insights next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Trustworthy Experiment Insights compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Trustworthy Experiment Insights this skillhashgraph-online/awesome-codex-plugins1.3k—~833Automated safety check: PassMIT
Jebo Literature Positioningbrycewang-stanford/Awesome-Journal-Skills1.2k—~1.2kAutomated safety check: PassMIT
Hybrid Cloud Outboxesgetsentry/sentry46k—~4.8kAutomated safety check: PassCustom licence
Replicate Video AdJingyi-Wu-Richael/replicate-video-ad1081 repos~1.6kAutomated safety check: PassNone
Sea Orm 2FlyinPancake/yoink112—~2.9kAutomated safety check: PassApache-2.0
Pixel Perfect ReplicationYu-369/VibeCurb979—~8.7kAutomated safety check: PassMIT

Similar skills

  • Jebo Literature Positioning

    brycewang-stanford/Awesome-Journal-Skills

    A skill your agent uses when staking a Journal of Economic Behavior & Organization (JEBO) manuscript's contribution against the behavioral, experimental, and organizational literatures.

    1.2k GitHub stars~1.2k tokensUpdated 14 days ago
    DatabasesAuto-check passed
  • Hybrid Cloud Outboxes

    getsentry/sentry

    Official

    Guide for creating and maintaining outbox-based eventually consistent operations in Sentry.

    46k GitHub stars~4.8k tokensUpdated yesterday
    DatabasesAuto-check passed
  • Replicate Video Ad

    Jingyi-Wu-Richael/replicate-video-ad

    Analyze a reference video frame by frame and turn its visual grammar, story beats, dialogue, product reveal, proof sequence, and conversion structure into a production-ready ecommerce story-ad…

    108 GitHub starsUsed in 1 repo~1.6k tokens
    DatabasesAuto-check passed
  • Sea Orm 2

    FlyinPancake/yoink

    Expert guidance for SeaORM 2.0, Rust's async ORM with strongly-typed columns, nested ActiveModels, Entity Loader API, and entity-first workflow.

    112 GitHub stars~2.9k tokensUpdated 6 days ago
    DatabasesAuto-check passed
  • Image-to-code replication pipeline. An agent skill from Yu-369/VibeCurb.

    979 GitHub stars~8.7k tokensUpdated 2 mo ago
    DatabasesAuto-check passed
  • Guide for setting up thread-safe database connection pooling (FireDAC / UniDAC) in multithreaded Horse applications.

    1.4k GitHub stars~1.4k tokensUpdated today
    DatabasesAuto-check passed

More from hashgraph-online/awesome-codex-plugins

All 715 skills in this repo
  • Anime Reaction Gif

    hashgraph-online/awesome-codex-plugins

    Create original anime-style reaction stickers as looping GIFs and MP4 previews, using generated character pose sheets and timed key poses.

    1.3k GitHub stars~922 tokensUpdated today
    Auto-check passed
  • Calibredb

    hashgraph-online/awesome-codex-plugins

    Manage and query Calibre libraries with the calibredb CLI (local paths or Calibre Content server URLs).

    1.3k GitHub stars~1k tokensUpdated today
    Auto-check passed
  • Rust API Test Harness

    hashgraph-online/awesome-codex-plugins

    A skill your agent uses when adding, changing, testing, or debugging Rust HTTP APIs and services, especially when Codex needs black-box integration tests, random-port app startup, real database test…

    1.3k GitHub stars~1.7k tokensUpdated today
    Auto-check passed
  • Art

    hashgraph-online/awesome-codex-plugins

    Make a studio's game look like something at build time — a cover from a real frame of the game (free), painted covers, backdrops, textures and character plates from image models through the…

    1.3k GitHub stars~2.5k tokensUpdated today
    Auto-check passed
  • Calle

    hashgraph-online/awesome-codex-plugins

    Use CALL-E from Codex through the calle CLI. An agent skill from hashgraph-online/awesome-codex-plugins.

    1.3k GitHub stars~2.9k tokensUpdated today
    Auto-check passed
  • Game Balance Economy

    hashgraph-online/awesome-codex-plugins

    Balance game difficulty, resources, rewards, probability, progression, economies, and dominant strategies.

    1.3k GitHub stars~618 tokensUpdated today
    Auto-check passed

Questions about Trustworthy Experiment Insights

What does Trustworthy Experiment Insights do?

Assess whether experiment results are credible enough to influence product decisions. Trustworthy Experiment Insights is an agent skill from hashgraph-online/awesome-codex-plugins. Assess whether experiment results are credible enough to influence product decisions.

When should I use Trustworthy Experiment Insights?

Trustworthy Experiment Insights fits situations like: checking false positive; false negative risk; underpowered metrics; suspiciously large lifts.

How do I install Trustworthy Experiment Insights in Claude Code?

Run `npx skills add hashgraph-online/awesome-codex-plugins --skill trustworthy-experiment-insights -a claude-code`. Or copy the skill folder (plugins/LVTD-LLC/skills/skills/trustworthy-experiment-insights in hashgraph-online/awesome-codex-plugins) into .claude/skills/trustworthy-experiment-insights in your project. Claude Code loads it when a task matches its description.

How do I install Trustworthy Experiment Insights in Codex?

Run `npx skills add hashgraph-online/awesome-codex-plugins --skill trustworthy-experiment-insights -a codex`. Or copy the skill folder (plugins/LVTD-LLC/skills/skills/trustworthy-experiment-insights in hashgraph-online/awesome-codex-plugins) into .agents/skills/trustworthy-experiment-insights in your project. Codex loads it when a task matches its description.

Can I use Trustworthy Experiment Insights in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add hashgraph-online/awesome-codex-plugins --skill trustworthy-experiment-insights -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/trustworthy-experiment-insights, .gemini/skills/trustworthy-experiment-insights, .github/skills/trustworthy-experiment-insights and .opencode/skills/trustworthy-experiment-insights in your project.

What does Trustworthy Experiment Insights need to run?

SKILL.md names no scripts, command-line tools or credentials: Trustworthy Experiment Insights is instructions for the agent only. Compatibility (from SKILL.md): Codex, Claude Code, and other Agent Skills-compatible clients..

Does Trustworthy Experiment Insights access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Trustworthy Experiment Insights safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Trustworthy Experiment Insights use?

Trustworthy Experiment Insights is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Trustworthy Experiment Insights use?

About 833 tokens (SKILL.md is roughly 3.3k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 2k tokens, read only when the agent opens those files.

What are the alternatives to Trustworthy Experiment Insights?

Skills that share tags, products or a category with Trustworthy Experiment Insights: Jebo Literature Positioning (brycewang-stanford/Awesome-Journal-Skills, 1.2k stars), Hybrid Cloud Outboxes (getsentry/sentry, 46k stars), Replicate Video Ad (Jingyi-Wu-Richael/replicate-video-ad, 108 stars) and Sea Orm 2 (FlyinPancake/yoink, 112 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Trustworthy Experiment Insights?

hashgraph-online (a GitHub organization) maintains it in hashgraph-online/awesome-codex-plugins, which has 1,267 GitHub stars. The repository holds 716 skills in this directory. The repository was last updated on October 10, 2026.

Source: hashgraph-online/awesome-codex-plugins on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.