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 charlieviettq/awesome-agent-skill --skill draft-outreachgit clone --depth 1 https://github.com/charlieviettq/awesome-agent-skillWrote 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/charlieviettq/awesome-agent-skill/draft-outreach)<a href="https://agentmods.dev/skills/charlieviettq/awesome-agent-skill/draft-outreach"><img src="https://agentmods.dev/badge/skills/charlieviettq/awesome-agent-skill/draft-outreach/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/charlieviettq/awesome-agent-skill/draft-outreach"><img src="https://agentmods.dev/badge/skills/charlieviettq/awesome-agent-skill/draft-outreach.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 | $0.00052 | $0.02347 |
| Opus 5.5 | $0.00021 | $0.00939 |
| Sonnet 5.5 | $0.00010 | $0.00469 |
| Haiku 4.5 | $0.00005 | $0.00235 |
Grade A, and why
draft-outreach 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 1mo 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.
This is a copy
94% identical to draft-outreach — 3 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 442 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Draft Outreach
Research first, then draft. This skill never sends generic outreach - it always researches the prospect first to personalize the message. Works standalone with web search, supercharged when you connect your tools.
Connectors (Optional)
| Connector | What It Adds |
|---|---|
| Enrichment | Verified email, phone, background details |
| CRM | Prior relationship context, existing contacts |
| Create draft directly in your inbox |
No connectors? Web research works great. I'll output the email text for you to copy.
How It Works
+------------------------------------------------------------------+
| DRAFT OUTREACH |
| |
| Step 1: RESEARCH (always happens first) |
| - Web search (default) |
| - + Enrichment (if enrichment tools connected) |
| - + CRM (if CRM connected) |
| |
| Step 2: DRAFT (based on research) |
| - Personalized opening (from research) |
| - Relevant hook (their priorities) |
| - Clear CTA |
| |
| Step 3: DELIVER (based on connectors) |
| - Email draft (if email connected) |
| - Copy for LinkedIn (always) |
| - Output to user (always) |
+------------------------------------------------------------------+
Output Format
# Outreach Draft: [Person] @ [Company]
**Generated:** [Date] | **Research Sources:** [Web, Enrichment, CRM]
---
## Research Summary
**Target:** [Name], [Title] at [Company]
**Hook:** [Why reaching out now - the personalized angle]
**Goal:** [What you want from this outreach]
---
## Email Draft
**To:** [email if known, or "find email" note]
**Subject:** [Personalized subject line]
---
[Email body]
---
**Subject Line Alternatives:**
1. [Option 2]
2. [Option 3]
---
## LinkedIn Message (if no email)
**Connection Request (< 300 chars):**
[Short, no-pitch connection request]
**Follow-up Message (after connected):**
[Value-first message]
---
## Why This Approach
| Element | Based On |
|---------|----------|
| Opening | [Research finding that makes it personal] |
| Hook | [Their priority/pain point] |
| Proof | [Relevant customer story] |
| CTA | [Low-friction ask] |
---
## Email Draft Status
[Draft created - check ~~email]
[Email not connected - copy email above]
[No email found - use LinkedIn approach]
---
## Follow-up Sequence (Optional)
**Day 3 - Follow-up 1:**
[Short, new angle]
**Day 7 - Follow-up 2:**
[Different value prop]
**Day 14 - Break-up:**
[Final attempt]
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.
- 1mo ago First seen · 442 lines · 52 tokens per session scan A e7dfab1e3082
draft-outreach is a skill published in the GitHub repository charlieviettq/awesome-agent-skill (26 stars, last pushed 3mo ago), licensed MIT. It adds 52 tokens to every session and 2,347 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 94% identical to draft-outreach, differing in 3 lines, and is treated as a copy.
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