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 shawnla90/gtm-coding-agent --skill clearbox-onboardgit clone --depth 1 https://github.com/shawnla90/gtm-coding-agentWrote 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/shawnla90/gtm-coding-agent/clearbox-onboard)<a href="https://agentmods.dev/skills/shawnla90/gtm-coding-agent/clearbox-onboard"><img src="https://agentmods.dev/badge/skills/shawnla90/gtm-coding-agent/clearbox-onboard/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/shawnla90/gtm-coding-agent/clearbox-onboard"><img src="https://agentmods.dev/badge/skills/shawnla90/gtm-coding-agent/clearbox-onboard.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00107 | $0.01687 |
| Opus 5 | $0.00053 | $0.00843 |
| Sonnet 5 | $0.00021 | $0.00337 |
| Haiku 4.5 | $0.00011 | $0.00169 |
Grade A, and why
clearbox-onboard 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 9d 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 — 111 lines — stays where its author put it; the contents beside it link to each section on GitHub.
clearbox-onboard
One domain in, a full Clearbox offer pack out, every claim sourced.
The clearbox.to onboarding form asks for a name, a one-liner (80 chars max), and selling points, and warns: "Don't rush this one. Clearbox scores Reddit content against what you write here." This skill is the not-rushing. It researches the company first, then fills the form's exact shapes plus the fields behind the form (keywords, competitors, tracked subreddits). The stakes are structural: your keywords and competitors drive the matching, and the subreddit suggestion pass runs once at onboarding and is never re-run. Write these fields like they are permanent, because they mostly are. Output is pasted by the user into the form; there is no API path today.
Inputs
A website domain (e.g. acme.com); optionally any of: a knowledge brief, a codebase path, or the company's own-words description — whatever is supplied gets read first.
Before you write a word
Read FACTCHECK.md in this directory and hold every output to it. Short version: every selling point traces to a URL, "Unlike X" claims need a source on X too, never assume the category from the name, the user's brief is intent not fact, absence claims ("only X does...") are traps, and no invented subreddit or brand names.
The standard
The form's own example one-liner is the shape: "HubSpot is a CRM for B2B sales teams." The bar to clear: a stranger reads one sentence and knows what the product is, who uses it, and why it matters. Twenty-five words or fewer usually gets there; 80 characters is the hard limit.
Steps
1. Read what the user gave you
Brief, codebase, own-words description — read it all before researching, so you know the positioning intent.
Gotcha: user materials are authoritative on positioning intent, never on public claims. A claim the company makes about itself still needs to exist on their site (or somewhere public) before it enters a scored field. If it exists nowhere public, flag it: "publish this first or it stays out."
What ships with it
3 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.
- 9d ago First seen · 111 lines · 107 tokens per session scan A e32f36065db9
clearbox-onboard is a skill published in the GitHub repository shawnla90/gtm-coding-agent (142 stars, last pushed 6d ago), licensed MIT. It adds 107 tokens to every session and 1,687 once invoked, about $0.0005 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 2026-08-30.
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