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.
git clone --depth 1 https://github.com/Amey-Thakur/AI-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/commands/amey-thakur/ai-skills/sales-outreach-sequence)<a href="https://agentmods.dev/commands/amey-thakur/ai-skills/sales-outreach-sequence"><img src="https://agentmods.dev/badge/commands/amey-thakur/ai-skills/sales-outreach-sequence/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/commands/amey-thakur/ai-skills/sales-outreach-sequence"><img src="https://agentmods.dev/badge/commands/amey-thakur/ai-skills/sales-outreach-sequence.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 | $0.00021 | $0.00236 |
| Opus 5 | $0.00010 | $0.00118 |
| Sonnet 5 | $0.00004 | $0.00047 |
| Haiku 4.5 | $0.00002 | $0.00024 |
Grade A, and why
sales-outreach-sequence 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 6d 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.
What it actually says
You were invoked as a slash command. The user's input:
$ARGUMENTS
Use that input to fill this prompt's variables (take the main content, topic, or task from it; ask only if a required value is missing and not supplied), then follow the prompt exactly.
Draft an outreach sequence for:
{prospect}
Offer: {offer}
Use agent-sales-pipeline and cold-email.
Produce three to four messages:
- First: references something specific and real about them, with one clear ask.
- Follow-ups: each adding new value rather than repeating the ask.
- Final: a clean close that makes it easy to say no.
For each: subject line, body under 120 words, and timing.
Rules: every message must reference something real and verifiable about the recipient. No false urgency, fake familiarity, or invented mutual connections. Make declining easy and explicit. A human reviews and sends; never send automatically. Contact rules differ by jurisdiction.
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.
- 6d ago First seen · 36 lines · 21 tokens per session scan A c3ac0a574fe8
sales-outreach-sequence is a command published in the GitHub repository Amey-Thakur/AI-SKILLS (7 stars, last pushed 7d ago), licensed MIT. It adds 21 tokens to every session and 236 once invoked, about $0.0001 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 2026-09-06.
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implement
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