Agent skill

Holdback Experiment Design

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

Design degradation holdbacks and long-term cumulative holdbacks for product experiments and feature rollouts.

MITAuto-check passedResearch & Science

Install Holdback Experiment Design

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

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

GitHub CLI
$ gh skill install hashgraph-online/awesome-codex-plugins holdback-experiment-design --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/holdback-experiment-design .claude/skills/holdback-experiment-design && 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
holdback-experiment-design
GitHub stars
1.3k
Token cost
~736 tokens
SKILL.md length
214 words
Files
6 (incl. references)
Skills in repo
714
Repo updated
First seen
Licence
MIT

At a glance

Design degradation holdbacks and long-term cumulative holdbacks for product experiments and feature rollouts.

  • Works in 6 steps: State why short-term A/B evidence is… → Choose degradation or long-term… → Define who remains withheld, for how… → …
  • A team needs a long-term counterfactual
  • SKILL.md covers Source Traceability, Related Advanced Skills, Reference Routing and Workflow, plus 2 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Holdback Experiment Design is an agent skill from hashgraph-online/awesome-codex-plugins. Design degradation holdbacks and long-term cumulative holdbacks for product experiments and feature rollouts. Use when a team needs a long-term counterfactual, wants to measure delayed impact after launch, is worried about metric degradation over time, needs to decide holdback size or duration, or must weigh the user/business cost of withholding a feature.

Its SKILL.md is about 740 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 Research & Science, covering Experimental design. 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

  • A team needs a long-term counterfactual
  • Wants to measure delayed impact after launch
  • Is worried about metric degradation over time
  • Needs to decide holdback size

Example prompts

  • “/holdback-experiment-design”

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. State why short-term A/B evidence is insufficient.
  2. Choose degradation or long-term cumulative holdback.
  3. Define who remains withheld, for how long, and from what experience.
  4. Select long-term metrics and guardrails.
  5. Estimate the user, business, and ethical cost of withholding.
  6. Define monitoring cadence, exit criteria, and communication plan.

What it can do on your machine

Read from SKILL.md and the folder at commit 9e7b281. 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

Holdback Experiment Design loads about 736 tokens when it runs, and up to ~2k if it reads all its reference files. Until then it costs about 96 tokens; SKILL.md has 214 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~96
When it runs · the whole SKILL.md, loaded when a task matches
~736
With references · SKILL.md plus every file in references/, read only if the agent opens them
~2k

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 9e7b281, republished under its MIT licence (© hashgraph-online). 214 words, ~736 tokens.

Download SKILL.mdSave it as .claude/skills/holdback-experiment-design/SKILL.md (or your agent's skills folder). This skill also uses 5 other files; get the full folder from GitHub.
name
holdback-experiment-design
description
Design degradation holdbacks and long-term cumulative holdbacks for product experiments and feature rollouts. Use when a team needs a long-term counterfactual, wants to measure delayed impact after launch, is worried about metric degradation over time, needs to decide holdback size or duration, or must weigh the user/business cost of withholding a feature.
compatibility
Codex, Claude Code, and other Agent Skills-compatible clients.
license
MIT
metadata.version
0.1.0
metadata.displayName
Holdback Experiment Design
metadata.category
Product Management
metadata.tags
practical-ab-testing,ab-testing,experimentation,holdbacks,product-analytics

Holdback Experiment Design

Use this skill to plan holdbacks that preserve a comparison group after a feature rollout. Holdbacks are useful when effects may appear later, accumulate, or degrade after launch.

Source Traceability

Primary source: Practical A/B Testing by Leemay Nassery. Guidance is transformed and paraphrased from chapter 3 lines 2487-2836. Experiment-type context comes from chapter 3 lines 2013-2486.

  • long-term-impact-evaluation: use before detailed holdback design when the team should compare holdbacks against post-period analysis, continuous monitoring, CLV models, or hybrid methods.
  • trustworthy-experiment-insights: use when deciding whether long-term or holdback evidence is credible enough to drive a product decision.

Reference Routing

NeedRead
Holdback conceptsreferences/core/knowledge.md
Design and cost rulesreferences/core/rules.md
Scenario examplesreferences/core/examples.md
Step-by-step planningworkflows/design-holdback.md

Workflow

  1. State why short-term A/B evidence is insufficient.
  2. Choose degradation or long-term cumulative holdback.
  3. Define who remains withheld, for how long, and from what experience.
  4. Select long-term metrics and guardrails.
  5. Estimate the user, business, and ethical cost of withholding.
  6. Define monitoring cadence, exit criteria, and communication plan.

Output Format

markdown
# Holdback Plan

## Purpose
[What long-term question this holdback answers.]

## Holdback Type
[Degradation | Long-term cumulative]

## Population And Duration
- Holdback population:
- Rollout population:
- Duration:
- Removal criteria:

## Metrics
| Metric | Role | Readout Cadence | Concern |
|--------|------|-----------------|---------|

## Cost Of Withholding
- User cost:
- Business cost:
- Ethical or trust concern:

## Decision Rules
- Continue holdback if:
- End holdback if:
- Escalate if:

Quality Bar

  • Do not create a holdback without a specific long-term question.
  • Do not withhold a clearly valuable feature longer than the question requires.
  • Do not ignore the opportunity cost to held-back users.
  • Monitor guardrails while the holdback is active.

© 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/holdback-experiment-design of hashgraph-online/awesome-codex-plugins.

  • SKILL.md
  • guidelines.md
  • references/core/examples.md
  • references/core/knowledge.md
  • references/core/rules.md
  • workflows/design-holdback.md

Open the folder on GitHubat commit 9e7b281

Compare with similar skills

Holdback Experiment Design 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.

Holdback Experiment Design compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Holdback Experiment Design this skillhashgraph-online/awesome-codex-plugins1.3k—~736Automated safety check: PassMIT
Scientific Critical Thinkingweapp-tailwindcss/weapp-tailwindcss1.9k22 repos~5.9kAutomated safety check: NotesMIT
Benchmark Paper TemplateHKUSTDial/Supervisor-Skills8.7k—~2.8kAutomated safety check: PassCC-BY-4.0
Claim-Driven Experiment PlannerzjYao36/Auto-Research-Refine1286 repos~2.3kAutomated safety check: NotesNone
Research Refine PipelinezjYao36/Auto-Research-Refine1285 repos~1.4kAutomated safety check: NotesNone
Metabolic Study Planneraiming-lab/AutoResearchClaw15k—~1.9kAutomated safety check: PassMIT

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Questions about Holdback Experiment Design

What does Holdback Experiment Design do?

Design degradation holdbacks and long-term cumulative holdbacks for product experiments and feature rollouts. Holdback Experiment Design is an agent skill from hashgraph-online/awesome-codex-plugins. Design degradation holdbacks and long-term cumulative holdbacks for product experiments and feature rollouts.

When should I use Holdback Experiment Design?

Holdback Experiment Design fits situations like: A team needs a long-term counterfactual; wants to measure delayed impact after launch; is worried about metric degradation over time; needs to decide holdback size.

How do I install Holdback Experiment Design in Claude Code?

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

How do I install Holdback Experiment Design in Codex?

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

Can I use Holdback Experiment Design 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 holdback-experiment-design -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/holdback-experiment-design, .gemini/skills/holdback-experiment-design, .github/skills/holdback-experiment-design and .opencode/skills/holdback-experiment-design in your project.

What does Holdback Experiment Design need to run?

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

Does Holdback Experiment Design 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 Holdback Experiment Design 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 Holdback Experiment Design use?

Holdback Experiment Design 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 Holdback Experiment Design use?

About 736 tokens (SKILL.md is roughly 2.9k 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 1.2k tokens, read only when the agent opens those files.

What are the alternatives to Holdback Experiment Design?

Skills that share tags, products or a category with Holdback Experiment Design: Scientific Critical Thinking (weapp-tailwindcss/weapp-tailwindcss, 1.9k stars), Benchmark Paper Template (HKUSTDial/Supervisor-Skills, 8.7k stars), Claim-Driven Experiment Planner (zjYao36/Auto-Research-Refine, 128 stars) and Research Refine Pipeline (zjYao36/Auto-Research-Refine, 128 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Holdback Experiment Design?

hashgraph-online (a GitHub organization) maintains it in hashgraph-online/awesome-codex-plugins, which has 1,255 GitHub stars. The repository holds 714 skills in this directory. The repository was last updated on October 9, 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.