Viral Product Evaluator

Grade a product against the 32 principles of viral products. Two inputs — a codebase and a landing page — produce one output: a scored report of what's already satisfied and, in priority order, what to do next to make it more viral.

When to Use

Trigger when the user wants to:

Make a product, SaaS, or indie app "more viral" or more shareable
Score / audit a landing page against viral-marketing or conversion principles
Get a prioritized, ordered list of changes to improve a product's pull
Check a product against "the 32 principles" (Marc Lou-style viral-product rules)

Do not use for: technical SEO (seo-ai-optimizer), App Store ASO (aso-marketing), turning a README into a page (landing-page-generator), or bug-hunting code review (code-review). This skill evaluates and prioritizes; it does not rewrite the product.

Prerequisites

Read access to the target codebase directory.
Landing page signal: public URL, local file, or auto-detectable in the tree.
The skill's references/*.md files present for the rubric and output shape.
User confirmation on any deviation from the 32 principles.

Missing prerequisites → stop and report before gathering evidence.

What this skill does and does not touch

It reads the codebase, fetches the landing page, and writes one report file (viral-evaluation.md). It does not edit the user's source, change copy, or commit anything — so no repo-sync/branch guardrail is needed. If the user then asks you to apply fixes, that is a separate task (hand off to a copy/frontend skill); this skill stops at the prioritized plan.

Inputs

Accept any combination the user provides; ask only for what's missing and truly needed.

Landing page — one of:
- a live URL → fetch it with the /browse skill (headless). Capture rendered copy, headline, CTAs, pricing section, testimonials, nav, and <head> meta (og:image, twitter:image, description, <title>).
- a local file (index.html, a JSX/TSX/MDX page, a built dist/) → read it directly.
- auto-detect from the codebase → search for the landing/marketing page (common spots: index.html, app/page.tsx, pages/index.*, src/App.*, landing/, marketing/, public/). Confirm the candidate with the user if ambiguous.
Codebase — a path to the repo (defaults to the current working directory). Used for the pricing/paywall/subscription principles, the feature surface ("does one thing"), and to locate the landing page if no URL/file was given.
Extra instructions (optional) — strategic context such as "we keep a free tier on purpose", "target audience is developers", "we must stay subscription". Honor these when interpreting a verdict (note the deliberate deviation) but still score the principle as written so the number stays comparable.

If neither a URL, a file, nor a detectable page exists, stop and ask the user where the landing page lives — do not invent one.

Pipeline (3 phases, in order)

Run these in sequence. Emit the matching Step Completion Report (see references/step-reports.md) after each.

Phase 1 — Resolve inputs & gather evidence

Resolve the landing page input (URL → /browse; file → read; else auto-detect).
Locate the codebase and find monetization evidence: billing SDKs (Stripe, Paddle, LemonSqueezy, RevenueCat, Chargebee), pricing config/constants, plan & tier definitions, paywall/auth gating, trial logic. Grep for price, plan, tier, checkout, subscription, free, trial, stripe, paddle.
Skim the feature surface (routes, nav items, top-level modules) to judge "does one thing".
For a large codebase, use grep/read (or a one-off Agent task scoped to pricing + feature evidence) so the main context stays clean. Collect: tier list, billing type (one-time vs subscription), free-plan yes/no, and a one-line feature inventory.
Note any extra instructions from the user.

Phase 2 — Evaluate against the 32 principles

Read references/principles.md — the full rubric. Score every principle PASS / PARTIAL / FAIL using its criteria. Do not skip any; absence of a thing a viral product would ship (pricing, testimonials, demo) is a real FAIL, not "unknown".
For each verdict, capture specific evidence from THIS product — quote the actual headline, name the actual tier, cite the file/line. Generic findings are not acceptable.
Tag every judgment/visual principle (hero punch, emotional headline, OG-image design, founder presence, novelty, price-vs-competitor) as low-confidence and record what a human must eyeball.
Compute the Virality Score: PASS=1, PARTIAL=0.5, FAIL=0, summed over 32, ×100/32, rounded. Assign the tier (Viral-ready / Promising / Needs work / Not viral yet).

Phase 3 — Prioritize fixes & write the report

Read references/report-template.md and produce the report in that exact shape: verdict block → scorecard (all 32) → top fixes (prioritized) → what's working → caveats.
The top fixes list is the core deliverable. Order by impact × ease, hero/paywall/ headline/proof/single-CTA first. Merge principles that share a root cause into one fix. Make each fix concrete enough to act on (give the actual proposed headline, the tier to cut, the CTA label) — quote what it is Now and what to Change it to.
Write the report to viral-evaluation.md (repo root or beside the landing-page source) and also print the verdict block + top fixes inline.

Honest evaluation

This is a critique tool — its value is candor. Do not inflate scores to be encouraging. If the hero fails, say it fails and show the fix. At the same time, do not invent flaws: a genuine PASS is a PASS. Low-confidence verdicts must be labeled, never laundered into false certainty. When extra instructions justify a deliberate deviation (e.g. a strategic free tier), score the principle as written and explain the trade-off in the caveats — don't silently pass it.

On any failure to fetch inputs or read evidence, report the concrete error and stop — do not guess or continue with incomplete data. Confirm with the user on every gate and before finalizing the report. If a principle cannot be evidenced, mark FAIL or low-confidence; never invent.

Step Completion Reports

After each phase, emit the report from references/step-reports.md. The three phases are Gather Evidence, Evaluate, and Prioritize & Report.

Acceptance Criteria

All 32 principles scored with specific evidence quoted from the product.
Virality Score computed correctly (PASS=1, PARTIAL=0.5, FAIL=0) and tier assigned.
Top fixes are concrete, prioritized by impact×ease, with before/after suggestions.
Report written to viral-evaluation.md ; Step Completion Reports emitted per phase.
Negative-trigger domains respected (no SEO/ASO/copy/code-review work).

Expected output

A report containing:

Overall verdict + Virality Score (e.g. 68 — Promising)
Scorecard table for all 32 principles
Top 5-8 prioritized fixes with exact copy or code recommendations
What's already working
Caveats / low-confidence items

Edge cases

No landing page detectable: ask for URL or file; do not fabricate.
Codebase only (no public page): still score what can be inferred from code (pricing etc).
Strategic deviation justified by user (e.g. no testimonials by design): score as written, note the trade-off in caveats.
Partial evidence: mark affected principles low-confidence, never guess a PASS.

Reference files

references/principles.md — the 32-principle rubric: per-principle checks, evidence source, PASS/PARTIAL/FAIL bars, confidence flags, and the scoring formula. Load every run.
references/report-template.md — exact output shape (verdict block, scorecard, prioritized fixes, strengths, caveats) with a calibration example.
references/step-reports.md — Step Completion Report formats for the three phases.

Viral Product Evaluator

When to Use

Trigger when the user wants to:

Make a product, SaaS, or indie app "more viral" or more shareable
Score / audit a landing page against viral-marketing or conversion principles
Get a prioritized, ordered list of changes to improve a product's pull
Check a product against "the 32 principles" (Marc Lou-style viral-product rules)

Prerequisites

Read access to the target codebase directory.
Landing page signal: public URL, local file, or auto-detectable in the tree.
The skill's references/*.md files present for the rubric and output shape.
User confirmation on any deviation from the 32 principles.

Missing prerequisites → stop and report before gathering evidence.

What this skill does and does not touch

Inputs

Accept any combination the user provides; ask only for what's missing and truly needed.

Landing page — one of:
- a live URL → fetch it with the /browse skill (headless). Capture rendered copy, headline, CTAs, pricing section, testimonials, nav, and <head> meta (og:image, twitter:image, description, <title>).
- a local file (index.html, a JSX/TSX/MDX page, a built dist/) → read it directly.
- auto-detect from the codebase → search for the landing/marketing page (common spots: index.html, app/page.tsx, pages/index.*, src/App.*, landing/, marketing/, public/). Confirm the candidate with the user if ambiguous.
Codebase — a path to the repo (defaults to the current working directory). Used for the pricing/paywall/subscription principles, the feature surface ("does one thing"), and to locate the landing page if no URL/file was given.
Extra instructions (optional) — strategic context such as "we keep a free tier on purpose", "target audience is developers", "we must stay subscription". Honor these when interpreting a verdict (note the deliberate deviation) but still score the principle as written so the number stays comparable.

If neither a URL, a file, nor a detectable page exists, stop and ask the user where the landing page lives — do not invent one.

Pipeline (3 phases, in order)

Run these in sequence. Emit the matching Step Completion Report (see references/step-reports.md) after each.

Phase 1 — Resolve inputs & gather evidence

Resolve the landing page input (URL → /browse; file → read; else auto-detect).
Locate the codebase and find monetization evidence: billing SDKs (Stripe, Paddle, LemonSqueezy, RevenueCat, Chargebee), pricing config/constants, plan & tier definitions, paywall/auth gating, trial logic. Grep for price, plan, tier, checkout, subscription, free, trial, stripe, paddle.
Skim the feature surface (routes, nav items, top-level modules) to judge "does one thing".
For a large codebase, use grep/read (or a one-off Agent task scoped to pricing + feature evidence) so the main context stays clean. Collect: tier list, billing type (one-time vs subscription), free-plan yes/no, and a one-line feature inventory.
Note any extra instructions from the user.

Phase 2 — Evaluate against the 32 principles

Read references/principles.md — the full rubric. Score every principle PASS / PARTIAL / FAIL using its criteria. Do not skip any; absence of a thing a viral product would ship (pricing, testimonials, demo) is a real FAIL, not "unknown".
For each verdict, capture specific evidence from THIS product — quote the actual headline, name the actual tier, cite the file/line. Generic findings are not acceptable.
Tag every judgment/visual principle (hero punch, emotional headline, OG-image design, founder presence, novelty, price-vs-competitor) as low-confidence and record what a human must eyeball.
Compute the Virality Score: PASS=1, PARTIAL=0.5, FAIL=0, summed over 32, ×100/32, rounded. Assign the tier (Viral-ready / Promising / Needs work / Not viral yet).

Phase 3 — Prioritize fixes & write the report

Read references/report-template.md and produce the report in that exact shape: verdict block → scorecard (all 32) → top fixes (prioritized) → what's working → caveats.
The top fixes list is the core deliverable. Order by impact × ease, hero/paywall/ headline/proof/single-CTA first. Merge principles that share a root cause into one fix. Make each fix concrete enough to act on (give the actual proposed headline, the tier to cut, the CTA label) — quote what it is Now and what to Change it to.
Write the report to viral-evaluation.md (repo root or beside the landing-page source) and also print the verdict block + top fixes inline.

Honest evaluation

Step Completion Reports

After each phase, emit the report from references/step-reports.md. The three phases are Gather Evidence, Evaluate, and Prioritize & Report.

Acceptance Criteria

All 32 principles scored with specific evidence quoted from the product.
Virality Score computed correctly (PASS=1, PARTIAL=0.5, FAIL=0) and tier assigned.
Top fixes are concrete, prioritized by impact×ease, with before/after suggestions.
Report written to viral-evaluation.md ; Step Completion Reports emitted per phase.
Negative-trigger domains respected (no SEO/ASO/copy/code-review work).

Expected output

A report containing:

Overall verdict + Virality Score (e.g. 68 — Promising)
Scorecard table for all 32 principles
Top 5-8 prioritized fixes with exact copy or code recommendations
What's already working
Caveats / low-confidence items

Edge cases

No landing page detectable: ask for URL or file; do not fabricate.
Codebase only (no public page): still score what can be inferred from code (pricing etc).
Strategic deviation justified by user (e.g. no testimonials by design): score as written, note the trade-off in caveats.
Partial evidence: mark affected principles low-confidence, never guess a PASS.

Reference files

references/principles.md — the 32-principle rubric: per-principle checks, evidence source, PASS/PARTIAL/FAIL bars, confidence flags, and the scoring formula. Load every run.
references/report-template.md — exact output shape (verdict block, scorecard, prioritized fixes, strengths, caveats) with a calibration example.
references/step-reports.md — Step Completion Report formats for the three phases.

Adoption

luongnv89/viral-product-evaluator

$ install --global

Security Scan Results

SKILL.md

Viral Product Evaluator

When to Use

Prerequisites

What this skill does and does not touch

Inputs

Pipeline (3 phases, in order)

Phase 1 — Resolve inputs & gather evidence

Phase 2 — Evaluate against the 32 principles

Phase 3 — Prioritize fixes & write the report

Honest evaluation

Step Completion Reports

Acceptance Criteria

Expected output

Edge cases

Reference files

Related Skills

luongnv89/issue-work-loop

luongnv89/herdr-agent-comms

luongnv89/doc-manager

luongnv89/diagram-generator

luongnv89/viral-product-evaluator

$ install --global

Security Scan Results

SKILL.md

Viral Product Evaluator

When to Use

Prerequisites

What this skill does and does not touch

Inputs

Pipeline (3 phases, in order)

Phase 1 — Resolve inputs & gather evidence

Phase 2 — Evaluate against the 32 principles

Phase 3 — Prioritize fixes & write the report

Honest evaluation

Step Completion Reports

Acceptance Criteria

Expected output

Edge cases

Reference files

Related Skills

luongnv89/issue-work-loop

luongnv89/herdr-agent-comms

luongnv89/doc-manager

luongnv89/diagram-generator