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 kelpi-ai/meta-ads-skills --skill buyer-language-minergit clone --depth 1 https://github.com/kelpi-ai/meta-ads-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/kelpi-ai/meta-ads-skills/buyer-language-miner)<a href="https://agentmods.dev/skills/kelpi-ai/meta-ads-skills/buyer-language-miner"><img src="https://agentmods.dev/badge/skills/kelpi-ai/meta-ads-skills/buyer-language-miner/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/kelpi-ai/meta-ads-skills/buyer-language-miner"><img src="https://agentmods.dev/badge/skills/kelpi-ai/meta-ads-skills/buyer-language-miner.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 | $0.00053 | $0.00603 |
| Opus 5.5 | $0.00021 | $0.00241 |
| Sonnet 5.5 | $0.00011 | $0.00121 |
| Haiku 4.5 | $0.00005 | $0.00060 |
Grade A, and why
buyer-language-miner 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 2mo 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 — 42 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Buyer Language Miner
Doctrine
The most persuasive line in your next ad has already been written, by a customer, in a review or a Reddit thread. Copy built from real buyer language outperforms invented copy because it passes the recognition test instantly: the reader thinks "that is exactly what I said." Marketers say "creative fatigue"; owners say "the exact same ads that cost $6.85 a lead now cost $30.90." Mine the second kind. This is also how you find the deeper pains buyers only admit anonymously.
When to use
- Before Angle Writer (its PAIN lines should come from here).
- Before writing any landing page or hook.
- Works with no MCP. Needs raw material: your reviews, competitor reviews, community threads, support emails, sales-call notes.
Run it
Mine this buyer language for my offer: [ONE-LINE OFFER + WHO YOU THINK IT IS FOR]
Raw material: [PASTE reviews / thread links / support emails / call notes. Include COMPETITOR reviews and community threads about the PROBLEM, not just about products.]
1. Extract verbatim quotes only (no paraphrasing) and sort into:
- TRIGGERS: the moment that started the search ("the day X happened...")
- PAINS: the problem in their words, especially emotional ("am I just stupid?")
- DESIRED OUTCOMES: what better looks like, in their words
- OBJECTIONS: why they hesitate or distrust ("everyone selling X says X works")
- ALTERNATIVES: what they do instead (DIY, a cheaper tool, nothing)
2. Mark the 5-10 BUYER-ISMS: the quotes so vivid they could be a hook or an on-image line as-is.
3. Note the words they NEVER use (jargon I should delete from my copy).
4. Map each strong pain quote to a possible angle: WHO said it, what PROMISE would answer it.
Guardrails
- Verbatim means verbatim. A cleaned-up quote is a fabricated quote.
- Keep the source next to every quote. Public quotes can inspire copy; using someone's exact words in an ad may need permission and always needs judgment.
- Volume matters: one dramatic quote is an anecdote. Flag which pains repeat across many sources and which appeared once.
- Do not mine only happy reviews. Objections and alternatives are where the losing half of your funnel lives.
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 2mo ago First seen · 42 lines · 53 tokens per session scan A 4ebdda0bc8b1
buyer-language-miner is a skill published in the GitHub repository kelpi-ai/meta-ads-skills (5 stars, last pushed 4mo ago), licensed MIT. It adds 53 tokens to every session and 603 once invoked, about $0.0002 per session on Opus 5.5. 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.
buyer-language-miner compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| buyer-language-minerkelpi-ai/meta-ads-skills | Skill | 5 | 4mo ago | MIT | A | 53 | Claude Code, Codex |
| imagegenopenai/codex | Skill | 134k | today | Apache-2.0 | A | 113 | Codex |
| playwright-climicrosoft/playwright | Skill | 99k | today | Apache-2.0 | A | 19 | Claude Code |
| local-ai-agentsmicrosoft/ai-agents-for-beginners | Skill | 79k | today | MIT | A | 200 | Claude Code, Codex |
| systematic-debuggingobra/superpowers | Skill | 314k | 3d ago | MIT | A | 21 | Claude Code |
| next-cache-components-adoptionvercel/next.js | Skill | 143k | yesterday | MIT | A | 95 | Claude Code |
| next-partial-prefetching-adoptionvercel/next.js | Skill | 143k | yesterday | MIT | A | 103 | Claude Code |