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 agentmods add skills/100xpercent/pop-pay-python/pop-pay-skillnpx skills add 100xPercent/pop-pay-python --skill pop-pay-skillgit clone --depth 1 https://github.com/100xPercent/pop-pay-pythonWrote 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/100xpercent/pop-pay-python/pop-pay-skill)<a href="https://agentmods.dev/skills/100xpercent/pop-pay-python/pop-pay-skill"><img src="https://agentmods.dev/badge/skills/100xpercent/pop-pay-python/pop-pay-skill.svg" alt="Measured on agentmods" 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 | $0.00035 | $0.01739 |
| Opus 5 | $0.00017 | $0.00870 |
| Sonnet 5 | $0.00007 | $0.00348 |
| Haiku 4.5 | $0.00003 | $0.00174 |
Grade A, and why
pop-pay 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 4d 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 — 204 lines — stays where its author put it; the contents beside it link to each section on GitHub.
What This Skill Does
Gives your OpenClaw agent the ability to pay at any online store using your own existing credit card — no account to create, no SaaS subscription, no external service to trust.
Your card number is stored in your local system keychain and is never placed in the agent's context window. When payment is approved, credentials are injected directly into the browser's payment form via CDP (Chrome DevTools Protocol — an open protocol maintained by Google) in a separate process — the agent never sees them. If your agent is compromised by a prompt injection attack, the attacker cannot steal your card.
Privacy & Data Flow
All payment logic runs on your machine. There are no Point One Percent servers involved in the payment path.
| Component | Default | Data stays |
|---|---|---|
| Card credentials | Local system keychain | Your machine only |
| Spend policy | ~/.config/pop-pay/.env |
Your machine only |
| Guardrail engine | keyword mode (zero API calls) |
Your machine only |
| Guardrail engine (optional) | llm mode — uses your own API key |
Your API provider |
| Webhook notifications | Disabled by default — only active if POP_WEBHOOK_URL is set |
Your chosen endpoint |
Keyword guardrail (default): evaluates transactions locally with no external calls.
LLM guardrail (opt-in): uses your own POP_LLM_API_KEY — no data is sent to Point One Percent.
Setup (One Time)
# Install from PyPI (https://pypi.org/project/pop-pay/)
pip install pop-pay
pop-pay setup # securely stores your card in the system keychain
pop-pay setup --profile # stores billing info (name, address, email)
Then add to your OpenClaw config:
{
"mcpServers": {
"pop-pay": {
"command": "pop-pay",
"args": ["serve"]
}
}
}
Set your spend policy in ~/.config/pop-pay/.env:
POP_ALLOWED_CATEGORIES='["amazon","shopify","aws"]'
POP_MAX_AMOUNT_PER_TX=100
POP_MAX_DAILY_BUDGET=300
POP_AUTO_INJECT=true # set to false to review injections manually
POP_REQUIRE_HUMAN_APPROVAL=false # set to true for manual confirmation on every payment
What ships with it
2 files 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.
- 4d ago First seen · 204 lines · 35 tokens per session scan A 21835b502961
pop-pay is a skill published in the GitHub repository 100xPercent/pop-pay-python (1 stars, last pushed 1mo ago), licensed MIT. It adds 35 tokens to every session and 1,739 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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