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 adam-lagerhausen/b2b-marketing-skills --skill ai-pmm-reviewergit clone --depth 1 https://github.com/adam-lagerhausen/b2b-marketing-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/adam-lagerhausen/b2b-marketing-skills/ai-pmm-reviewer)<a href="https://agentmods.dev/skills/adam-lagerhausen/b2b-marketing-skills/ai-pmm-reviewer"><img src="https://agentmods.dev/badge/skills/adam-lagerhausen/b2b-marketing-skills/ai-pmm-reviewer/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/adam-lagerhausen/b2b-marketing-skills/ai-pmm-reviewer"><img src="https://agentmods.dev/badge/skills/adam-lagerhausen/b2b-marketing-skills/ai-pmm-reviewer.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 | $0.00045 | $0.02129 |
| Opus 5 | $0.00023 | $0.01064 |
| Sonnet 5 | $0.00009 | $0.00426 |
| Haiku 4.5 | $0.00005 | $0.00213 |
Grade A, and why
ai-pmm-reviewer 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 — 253 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AI PMM Reviewer
When to use
Use this skill when you have an AI-generated or AI-assisted B2B marketing draft and need PMM judgment before it ships.
Use it for:
- Reviewing blog posts, landing pages, launch copy, emails, ads, sales enablement, positioning statements, messaging frameworks, and executive narratives
- Checking whether a draft matches a messaging framework or positioning strategy
- Finding where the draft sounds generic, inflated, or interchangeable with competitors
- Turning customer calls, notes, transcripts, or summaries into review evidence
- Producing a sharper rewrite after the strategic issues are clear
The core belief: AI makes the average PMM faster, not better. LLM output trends toward average. It often sounds fluent but familiar, and buyers can smell it. AI earns its keep in review: checking a 2,000-word blog against the messaging framework, catching gaps, summarizing customer calls, and helping the PMM say, "I talked to 10 customers, and here is what they said."
AI gets the work to 60% fast. The last 40% requires customer conversations, market instinct, hard positioning choices, voice, and rewriting.
Inputs
Ask for or infer these inputs:
- Draft content to review
- Company, product, and category
- Target audience, segment, persona, and buying role
- Messaging framework, positioning statement, launch brief, campaign brief, or sales narrative if available
- Customer voice, call notes, quotes, objections, win/loss notes, or research
- Competitive alternatives, including the status quo
- Intended channel and job of the asset
- Desired review depth: quick pass, detailed PMM review, or rewrite-ready teardown
If key inputs are missing, still review the draft, but label assumptions and lower confidence. Do not invent customer truth. Mark missing evidence as a gap.
Review workflow
1. Identify the job of the draft
Before editing words, define what the asset is supposed to do.
Ask:
- Who is the buyer or reader?
- What decision, belief, objection, or action should this asset influence?
- Where will it appear?
- What does the reader already believe?
- What must be different after reading it?
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 · 253 lines · 45 tokens per session scan A 9fb3419a0ebc
ai-pmm-reviewer is a skill published in the GitHub repository adam-lagerhausen/b2b-marketing-skills (5 stars, last pushed 2mo ago), licensed MIT. It adds 45 tokens to every session and 2,129 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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