Agent skill

Long Term Impact Evaluation

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

Choose methods for measuring long-term product impact after or beyond an A/B test.

MITAuto-check passedMarketing & SEO

Install Long Term Impact Evaluation

skills CLI
$ npx skills add hashgraph-online/awesome-codex-plugins --skill long-term-impact-evaluation -a claude-code

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

GitHub CLI
$ gh skill install hashgraph-online/awesome-codex-plugins long-term-impact-evaluation --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/long-term-impact-evaluation .claude/skills/long-term-impact-evaluation && 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
long-term-impact-evaluation
GitHub stars
1.3k
Token cost
~770 tokens
SKILL.md length
225 words
Files
6 (incl. references)
Skills in repo
714
Repo updated
First seen
Licence
MIT

At a glance

Choose methods for measuring long-term product impact after or beyond an A/B test.

  • Works in 6 steps: State why short-term experiment metrics… → Identify the expected time horizon and… → Compare holdback, post-period analysis,… → …
  • Comparing long-term holdbacks
  • 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

Long Term Impact Evaluation is an agent skill from hashgraph-online/awesome-codex-plugins. Choose methods for measuring long-term product impact after or beyond an A/B test. Use when comparing long-term holdbacks, post-period analysis, continuous monitoring, CLV models, delayed effects, short-term versus long-term metric tradeoffs, or lower-cost alternatives to long-term holdbacks.

Its SKILL.md is about 770 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 Marketing & SEO, covering A/B testing. 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

  • Comparing long-term holdbacks
  • Post-period analysis
  • Continuous monitoring
  • Delayed effects

Example prompts

  • “/long-term-impact-evaluation”

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 experiment metrics are insufficient.
  2. Identify the expected time horizon and delayed mechanisms.
  3. Compare holdback, post-period analysis, continuous monitoring, and CLV model
  4. Weigh accuracy against user cost, business cost, complexity, and confounding.
  5. Choose the simplest method that answers the long-term question.
  6. Define metric cadence, interpretation limits, and follow-up decisions.

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

Long Term Impact Evaluation loads about 770 tokens when it runs, and up to ~2.7k if it reads all its reference files. Until then it costs about 80 tokens; SKILL.md has 225 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~80
When it runs · the whole SKILL.md, loaded when a task matches
~770
With references · SKILL.md plus every file in references/, read only if the agent opens them
~2.7k

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). 225 words, ~770 tokens.

Download SKILL.mdSave it as .claude/skills/long-term-impact-evaluation/SKILL.md (or your agent's skills folder). This skill also uses 5 other files; get the full folder from GitHub.
name
long-term-impact-evaluation
description
Choose methods for measuring long-term product impact after or beyond an A/B test. Use when comparing long-term holdbacks, post-period analysis, continuous monitoring, CLV models, delayed effects, short-term versus long-term metric tradeoffs, or lower-cost alternatives to long-term holdbacks.
compatibility
Codex, Claude Code, and other Agent Skills-compatible clients.
license
MIT
metadata.version
0.1.0
metadata.displayName
Long-Term Impact Evaluation
metadata.category
Product Management
metadata.tags
practical-ab-testing,next-level-ab-testing,ab-testing,experimentation,holdbacks

Long-Term Impact Evaluation

Use this skill to choose a practical method for measuring product impact beyond the initial experiment window. It compares long-term holdbacks, post-period analysis, continuous monitoring, and customer lifetime value models.

Source Traceability

Primary source: Next-Level A/B Testing by Leemay Nassery. Guidance is transformed and paraphrased from Chapter 8 on long-term impact, short-term and long-term metric relationships, holdbacks, post-period analysis, continuous monitoring, and CLV models.

Related skills:

  • holdback-experiment-design for detailed holdback planning.
  • trustworthy-experiment-insights for result credibility and false positives.
  • experimentation-strategy-roadmap for cost, quality, and complexity tradeoffs.

Reference Routing

NeedRead
Long-term evaluation conceptsreferences/core/knowledge.md
Method selection and risk rulesreferences/core/rules.md
Scenario examplesreferences/core/examples.md
Step-by-step method selectionworkflows/choose-long-term-evaluation.md

Workflow

  1. State why short-term experiment metrics are insufficient.
  2. Identify the expected time horizon and delayed mechanisms.
  3. Compare holdback, post-period analysis, continuous monitoring, and CLV model options.
  4. Weigh accuracy against user cost, business cost, complexity, and confounding.
  5. Choose the simplest method that answers the long-term question.
  6. Define metric cadence, interpretation limits, and follow-up decisions.

Output Format

markdown
# Long-Term Impact Evaluation Plan

## Long-Term Question
[What delayed or sustained effect must be measured.]

## Recommended Method
[Holdback | Post-period analysis | Continuous monitoring | CLV model | Hybrid]

## Tradeoffs
| Method | Benefit | Cost/Risk | Fit |
|--------|---------|-----------|-----|

## Measurement Plan
- Short-term metric:
- Long-term metric:
- Time horizon:
- Confounders:

## Decision Rules
- Continue measuring if:
- End or revise if:
- Escalate if:

Quality Bar

  • Do not create a long-term holdback when a lower-cost method can answer the decision well enough.
  • Do not use post-period analysis without accounting for external factors such as seasonality or campaigns.
  • Do not rely on CLV models without naming model drift and behavior-change risk.
  • Do not confuse continuous monitoring with causal long-term measurement.

© 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/long-term-impact-evaluation of hashgraph-online/awesome-codex-plugins.

  • SKILL.md
  • guidelines.md
  • references/core/examples.md
  • references/core/knowledge.md
  • references/core/rules.md
  • workflows/choose-long-term-evaluation.md

Open the folder on GitHubat commit 9e7b281

Compare with similar skills

Long Term Impact Evaluation 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.

Long Term Impact Evaluation compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Long Term Impact Evaluation this skillhashgraph-online/awesome-codex-plugins1.3k—~770Automated safety check: PassMIT
Ab Testingcoreyhaines31/marketingskills54k3 repos~3.1kAutomated safety check: PassMIT
AnalyticsNexus-JPF/note-companion8707 repos~2.2kAutomated safety check: PassMIT
Ad Test Designeraaron-he-zhu/aaron-marketing-skills2.9k2 repos~2.8kAutomated safety check: PassApache-2.0
Ab Test Analyzeririnabuht12-oss/marketing-skills4k—~1.4kAutomated safety check: PassNone
Ab Test Store Listingappeeky/aso-skills2.2k—~1.8kAutomated safety check: PassMIT

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Categories

Questions about Long Term Impact Evaluation

What does Long Term Impact Evaluation do?

Choose methods for measuring long-term product impact after or beyond an A/B test. Long Term Impact Evaluation is an agent skill from hashgraph-online/awesome-codex-plugins. Choose methods for measuring long-term product impact after or beyond an A/B test.

When should I use Long Term Impact Evaluation?

Long Term Impact Evaluation fits situations like: comparing long-term holdbacks; post-period analysis; continuous monitoring; delayed effects.

How do I install Long Term Impact Evaluation in Claude Code?

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

How do I install Long Term Impact Evaluation in Codex?

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

Can I use Long Term Impact Evaluation 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 long-term-impact-evaluation -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/long-term-impact-evaluation, .gemini/skills/long-term-impact-evaluation, .github/skills/long-term-impact-evaluation and .opencode/skills/long-term-impact-evaluation in your project.

What does Long Term Impact Evaluation need to run?

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

Does Long Term Impact Evaluation 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 Long Term Impact Evaluation 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 Long Term Impact Evaluation use?

Long Term Impact Evaluation 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 Long Term Impact Evaluation use?

About 770 tokens (SKILL.md is roughly 3.1k 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 Long Term Impact Evaluation?

Skills that share tags, products or a category with Long Term Impact Evaluation: Ab Testing (coreyhaines31/marketingskills, 54k stars), Analytics (Nexus-JPF/note-companion, 870 stars), Ad Test Designer (aaron-he-zhu/aaron-marketing-skills, 2.9k stars) and Ab Test Analyzer (irinabuht12-oss/marketing-skills, 4k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Long Term Impact Evaluation?

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.