form-friction-finder

form-friction-finder is a skill for Claude Code, Codex from mardab96/b2b-lead-generation-claude-skills. It costs 36 tokens per session (740 once invoked), scanned A, original, MIT.

A review that looks for fields, steps, and user-interface patterns that make people abandon a form. It uses campaign, page, form, and sales evidence to examine where completion breaks down.

In plain words
What is it for?
Use it to investigate form completion problems and decide which fields, steps, or page and campaign changes to test.
Why use it?
It helps identify why visitors reach a form but do not submit it, including whether an added qualifying question could create more drop-off.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit Use it to investigate form completion problems and decide which fields, steps, or page and campaign changes to test.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/mardab96/b2b-lead-generation-claude-skills/form-friction-finder
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add mardab96/b2b-lead-generation-claude-skills --skill form-friction-finder
Clone the repo
git clone --depth 1 https://github.com/mardab96/b2b-lead-generation-claude-skills

Made for: Claude Code, Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for form-friction-finder

README.md
[![agentmods](https://agentmods.dev/badge/skills/mardab96/b2b-lead-generation-claude-skills/form-friction-finder/github.svg)](https://agentmods.dev/skills/mardab96/b2b-lead-generation-claude-skills/form-friction-finder)
Your own site
<a href="https://agentmods.dev/skills/mardab96/b2b-lead-generation-claude-skills/form-friction-finder"><img src="https://agentmods.dev/badge/skills/mardab96/b2b-lead-generation-claude-skills/form-friction-finder/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for form-friction-finder

Your own site · 80×15
<a href="https://agentmods.dev/skills/mardab96/b2b-lead-generation-claude-skills/form-friction-finder"><img src="https://agentmods.dev/badge/skills/mardab96/b2b-lead-generation-claude-skills/form-friction-finder.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 36 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 740 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00036 $0.00740
Opus 5 $0.00018 $0.00370
Sonnet 5 $0.00007 $0.00148
Haiku 4.5 $0.00004 $0.00074

Measured 12d ago against content hash 230fa85d50d0, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

form-friction-finder scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 12d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

form-friction-finder/SKILL.md · 65 lines

How it starts

The opening of the file, as written. The whole thing — 65 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Form Friction Finder

Use the shared quality bar in ../references/output-standard.md and ../references/skill-design-principles.md when those files are available.

Use this skill when

  • the user shares lead source, CRM stage, sales note, form, landing page or campaign data tied to form friction finder.
  • the next decision could change targeting, qualification, scoring, follow-up, sales handoff or budget.
  • lead volume looks acceptable but SQL, opportunity, closed-won, rejection or response-speed data raises doubt.

Do not use this skill for broad lead-generation advice without source, CRM, sales or qualification evidence. Use it when a real B2B lead quality decision is on the table.

Required input

  • business model, ICP, offer, ACV or deal value range, sales cycle and main conversion goal.
  • ad, landing page, lead form, CRM, call note, email or campaign data relevant to this diagnostic.
  • time window, traffic source, lead volume and downstream outcomes where available.
  • what decision the user is trying to make next: create, fix, scale, pause, brief sales or investigate.
  • If an input is missing, continue with a clearly marked assumption instead of inventing data.

Analysis workflow

  1. List every field, step, required answer, validation rule and hidden field in the form.
  2. Classify each field as essential for routing, qualification, sales context, compliance or nice-to-have.
  3. Compare form friction with buyer intent, offer value and traffic source temperature.
  4. Look for mobile-specific friction: keyboard type, dropdown length, field order, error messages and page scroll.
  5. Recommend remove, reorder, make optional, split, prefill or explain fields based on conversion and lead quality risk.

Decision rules

  • If the data does not connect to revenue, pipeline, qualified leads or conversion quality, label the recommendation as a hypothesis.
  • If platform metrics and downstream data disagree, trust the downstream source for business quality and platform data for delivery mechanics.
  • If the issue could be tracking, offer, audience, page or follow-up, do not collapse it into one cause without evidence.
  • Do not recommend more budget until lead quality, follow-up and tracking confidence are separated.

Read the full file on GitHub · 65 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 12d ago First seen · 65 lines · 36 tokens per session scan A 230fa85d50d0

Subscribe to this mod's changes

form-friction-finder is a skill published in the GitHub repository mardab96/b2b-lead-generation-claude-skills (5 stars, last pushed 1mo ago), licensed MIT. It adds 36 tokens to every session and 740 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.

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