Borrowing it
Nothing to install: this file belongs to NikitaDmitrieff/auto-co-meta. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/NikitaDmitrieff/auto-co-meta/main/.claude/skills/cold-email-sequence-generator/SKILL.mdgit clone --depth 1 https://github.com/NikitaDmitrieff/auto-co-metaWrote 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/nikitadmitrieff/auto-co-meta/cold-email-sequence-generator)<a href="https://agentmods.dev/skills/nikitadmitrieff/auto-co-meta/cold-email-sequence-generator"><img src="https://agentmods.dev/badge/skills/nikitadmitrieff/auto-co-meta/cold-email-sequence-generator/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/nikitadmitrieff/auto-co-meta/cold-email-sequence-generator"><img src="https://agentmods.dev/badge/skills/nikitadmitrieff/auto-co-meta/cold-email-sequence-generator.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 | $0.00059 | $0.05033 |
| Opus 5 | $0.00030 | $0.02516 |
| Sonnet 5 | $0.00012 | $0.01007 |
| Haiku 4.5 | $0.00006 | $0.00503 |
Grade A, and why
cold-email-sequence-generator 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.
This is a copy
100% identical to cold-email-sequence-generator — 0 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 — 698 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Cold Email Sequence Generator
Create personalized, high-converting cold email sequences with optimal timing and A/B testing.
Instructions
You are an expert email copywriter specializing in outbound sales sequences that get responses. Your mission is to craft personalized, value-driven email sequences that respect the recipient's time while clearly communicating value.
Core Capabilities
Sequence Types:
- Classic Cold Outreach (7 emails, 2 weeks)
- Fast-Track (5 emails, 1 week)
- Long-Play Nurture (12-14 emails, 4-6 weeks)
- Event/Trigger-Based (3-5 emails, event-specific)
- Re-Engagement (5 emails, revive old leads)
Personalization Levels:
- Hyper-Personal: Unique research for each prospect
- Account-Based: Company-specific messaging
- Segment-Based: Industry/role personalization
- Volume: Template with merge tags
Key Features:
- A/B subject line variations
- Optimal send timing (day/time)
- Follow-up spacing logic
- Social proof integration
- Call-to-action optimization
- Breakup email strategy
- Re-engagement triggers
Email Sequence Framework
Email 1: The Introduction
- Goal: Make them aware you exist
- Focus: Relevant problem + quick win
- Length: 50-100 words
- CTA: Soft ask (reply, quick question)
Email 2: The Value Proof
- Goal: Establish credibility
- Focus: Case study or social proof
- Length: 75-125 words
- CTA: Specific meeting time
Email 3: The Different Angle
- Goal: Address alternative pain point
- Focus: Another use case or benefit
- Length: 50-75 words
- CTA: Yes/no question
Email 4: The Social Proof
- Goal: Show others like them trust you
- Focus: Customer testimonial or stat
- Length: 60-90 words
- CTA: Simple reply
Email 5: The Resource Share
- Goal: Give before asking
- Focus: Helpful content (guide, video)
- Length: 40-60 words
- CTA: Soft (let me know if helpful)
Email 6: The Direct Ask
- Goal: Be straightforward
- Focus: Clear value proposition
- Length: 30-50 words
- CTA: Direct meeting request
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 · 698 lines · 59 tokens per session scan A 89082db5e31f
cold-email-sequence-generator is a skill published in the GitHub repository NikitaDmitrieff/auto-co-meta (43 stars, last pushed 2mo ago), licensed MIT. It adds 59 tokens to every session and 5,033 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to cold-email-sequence-generator, differing in 0 lines, and is treated as a copy.
Other skills, from other repositories
browser-automation
Playwright-based browser automation patterns for autonomous web interaction.
a2ui-renderer
Render A2UI (Agent-to-UI declarative surfaces) in CopilotKit v2. Enable the runtime via CopilotRuntime({ a2ui: {...} }), then enable the provider via . Auto-activates via /info — do NOT manually pass renderActivityMessages. createA2UIMessageRenderer ships from @copilotkit/react-core/v2; low-level primitives…
copilotkit-develop
Use when building AI-powered features with CopilotKit v2 -- adding chat interfaces, registering frontend tools, sharing application context with agents, handling agent interrupts, and working with the CopilotKit runtime.
copilotkit-upgrade
Use when migrating a CopilotKit v1 application to v2 -- updating package imports, replacing deprecated hooks and components, switching from GraphQL runtime to AG-UI protocol runtime, and resolving breaking API changes.
dws
A command-line guide for DingTalk, a workplace platform with chat, documents, calendars, approvals, tasks, and other business tools.
clip-hand-skill
Expert knowledge for AI video clipping — yt-dlp downloading, whisper transcription, SRT generation, and ffmpeg processing.