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.
npx skills add mardab96/b2b-lead-generation-claude-skills --skill ad-to-landing-promise-matchgit clone --depth 1 https://github.com/mardab96/b2b-lead-generation-claude-skillsWrote 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.
[](https://agentmods.dev/skills/mardab96/b2b-lead-generation-claude-skills/ad-to-landing-promise-match)<a href="https://agentmods.dev/skills/mardab96/b2b-lead-generation-claude-skills/ad-to-landing-promise-match"><img src="https://agentmods.dev/badge/skills/mardab96/b2b-lead-generation-claude-skills/ad-to-landing-promise-match/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.
<a href="https://agentmods.dev/skills/mardab96/b2b-lead-generation-claude-skills/ad-to-landing-promise-match"><img src="https://agentmods.dev/badge/skills/mardab96/b2b-lead-generation-claude-skills/ad-to-landing-promise-match.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00044 | $0.00760 |
| Opus 5 | $0.00022 | $0.00380 |
| Sonnet 5 | $0.00009 | $0.00152 |
| Haiku 4.5 | $0.00004 | $0.00076 |
Grade A, and why
ad-to-landing-promise-match 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.
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.
Ad To Landing Promise Match
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 ad to landing promise match.
- 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
- Extract the exact promise, audience, pain, proof, CTA and next step from each ad.
- Extract the same elements from the landing page hero, form, CTA and first proof block.
- Mark mismatches by severity: audience mismatch, outcome mismatch, offer mismatch, proof gap, CTA gap or commitment mismatch.
- Estimate the likely consequence: lower CVR, worse lead quality, higher bounce, form abandonment or sales confusion.
- Prioritize the smallest copy, CTA or page section fix that restores promise continuity.
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.
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.
- 12d ago First seen · 65 lines · 44 tokens per session scan A 306ddd0e327d
ad-to-landing-promise-match is a skill published in the GitHub repository mardab96/b2b-lead-generation-claude-skills (5 stars, last pushed 1mo ago), licensed MIT. It adds 44 tokens to every session and 760 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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