Content & Marketing · IN-DEPTH PROFILE

Landing Page Conversion Audit Skill

Evidence-bounded landing-page diagnosis with a short fix list ranked by expected business impact.

View listed source ↗Installation guide ↓
Best for

Growth teams reviewing paid-traffic landing and checkout pages

What you get

Bound the evidence + Audit the funnel

Main limitation

Cannot diagnose a broken audience or offer from page design alone.

First risk

Revenue claims inferred from incomplete funnel data. A visually plausible finding may be irrelevant when attribution, sample size, audience, or offer is wrong. State missing inputs, avoid lift promises, and validate material changes with measured experiments.

EVIDENCE FRESHNESS

Three checks, kept separate

A recent source check is not a runtime test or security audit.

Upstream sourceChecked 2026-09-24

Pinned revision · 1f564408

Open pinned commit ↗
SkillSignal profileEditorial metadata

Updated 2026-09-24

Runtime & securityNot independently verified

Source review does not certify behavior or safety.

IN PLAIN ENGLISH

What it does—and when it fits

Landing Page Conversion Audit is a GitHub Awesome Copilot workflow for diagnosing message mismatch, mobile friction, offer clarity, forms, trust, post-click paths, and measurement, then returning no more than seven fixes ranked by expected impact and evidence confidence.

This is aimed at Growth teams reviewing paid-traffic landing and checkout pages. Compare the examples below with your task, then review the limitations, permissions, and risks before installing.

What you get
  • Bound the evidenceCollect the rendered page, traffic source, sample ads, funnel volume, and device split.
  • Audit the funnelCheck message match, first viewport, offer, form, payment trust, follow-up, and tracking.
  • Rank the responseSeparate direct fixes, A/B tests, healthy elements, and areas that could not be checked.
What makes it different
  • Requires findings to name the element, failure mode, and specific change.
  • Explicitly limits claims when traffic or conversion data is sparse.
Community signalNo attributed third-party rating yet

No verified review text is in the current dataset. Use the linked source for the latest discussion.

Read the source note ↗

INSTALL BY AGENT

Choose your Agent

Paths come from official Agent docs or the universal installer behind skills.sh. Compatibility still follows this Skill's record.

Native

This Skill's current record explicitly names this Agent. Still inspect scripts, permissions, and external dependencies first.

Project install (recommended)
npx skills add github/awesome-copilot --skill landing-page-conversion-audit --agent github-copilot
Personal install
npx skills add github/awesome-copilot --skill landing-page-conversion-audit --agent github-copilot -g

Project install stays with this repository for team sharing. Personal install adds -g and works across repositories.

View install paths
Project path.agents/skills/landing-page-conversion-audit/
Personal path~/.copilot/skills/landing-page-conversion-audit/
Official agent docs

Copilot also accepts .github/skills and .claude/skills at project scope. Preview untrusted Skills before enabling scripts.

View path evidence ↗

TYPICAL WORKFLOW

A practical workflow

01

Bound the evidence

Collect the rendered page, traffic source, sample ads, funnel volume, and device split.

02

Audit the funnel

Check message match, first viewport, offer, form, payment trust, follow-up, and tracking.

03

Rank the response

Separate direct fixes, A/B tests, healthy elements, and areas that could not be checked.

THE TRADEOFFS

Advantages and tradeoffs

Notable strengths

  1. Requires findings to name the element, failure mode, and specific change.
  2. Explicitly limits claims when traffic or conversion data is sparse.

Limitations

  1. Cannot diagnose a broken audience or offer from page design alone.
  2. Expected-impact ordering is directional; the workflow forbids claiming transferable percentage lifts.

COMPARE THE TRADEOFF

Consider these close alternatives

These are deterministic nearby candidates, not paid placements or a universal ranking. Open the full profile before choosing.

01

Same job family · native to GitHub Copilot

ad-campaign-analyzer

Campaign performance analysis that separates signal from noise and recommends what to cut, scale, or test.
Tradeoff to check
Pure reasoning over exports cannot validate attribution, benchmark freshness, or platform data quality.
First risk
Small or biased samples drive budget decisions · High
Source state
Pinned · 1f564408
Open ad-campaign-analyzer profile ↗
02

Same job family · native to GitHub Copilot

prompt-optimizer

Finished, placeholder-free chat prompts shaped around the user's real task, audience, and output.
Tradeoff to check
A polished prompt cannot supply missing domain facts or guarantee the target model follows it.
First risk
Sensitive content embedded into the prompt · High
Source state
Pinned · 1f564408
Open prompt-optimizer profile ↗
03

Same job family · native to GitHub Copilot

competitor-ad-intelligence

Evidence-led analysis of public paid ads, creative patterns, landing pages, funnels, and market gaps.
Tradeoff to check
Public libraries can be incomplete, personalized, delayed, region-limited, or hard to scrape.
First risk
Unsupported performance inference · Medium
Source state
Pinned · 1f564408
Open competitor-ad-intelligence profile ↗
Compare more in the directory →

BEST FIT

Who it is for

→

Growth teams reviewing paid-traffic landing and checkout pages

→

Product teams deciding which conversion changes to test first

BEFORE YOU USE IT

Risks to review before use

High

Revenue claims inferred from incomplete funnel data

A visually plausible finding may be irrelevant when attribution, sample size, audience, or offer is wrong. State missing inputs, avoid lift promises, and validate material changes with measured experiments.

SECURITY

What the permission profile means

  • Use read-only access and sanitized analytics when auditing production funnels.
  • Do not alter checkout or tracking infrastructure without separate authorization.

Not a security certification. External ratings are attributed references. SkillSignal has not independently executed or security-reviewed this Skill.

COMMON QUESTIONS

Landing Page Conversion Audit Skill FAQ

What is the Landing Page Conversion Audit Skill?

Evidence-bounded landing-page diagnosis with a short fix list ranked by expected business impact. Landing Page Conversion Audit is a GitHub Awesome Copilot workflow for diagnosing message mismatch, mobile friction, offer clarity, forms, trust, post-click paths, and measurement, then returning no more than seven fixes ranked by expected impact and evidence confidence.

How do I install the Landing Page Conversion Audit Skill?

Open and review the listed source, choose the project or personal path for your Agent, then verify the first run in a controlled project. Open the pinned commit and read the current SKILL.md.

Is the Landing Page Conversion Audit Skill safe to use?

SkillSignal checked the source on 2026-09-24, but that is not a runtime test or security certification. Review the “Revenue claims inferred from incomplete funnel data” risk first and begin with the least access required.

INSIDE THE PACKAGE

Indexed files

SKILL.mdUpstream package contentSource-linked

TAGS

conversionlanding-pagescro

Original SkillSignal editorial profile grounded in GitHub Awesome Copilot commit d7e4ad98, checked 2026-09-23; not independently executed or security-certified.