Agent skill

Email Drafting

by gooseworks-ai in gooseworks-ai/goose-skills

Write high-converting cold emails using structured frameworks, personalization tiers, and patterns from real campaigns.

MITAuto-check passedSales & Support

Install Email Drafting

skills CLI
$ npx skills add gooseworks-ai/goose-skills --skill email-drafting -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install gooseworks-ai/goose-skills email-drafting --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/gooseworks-ai/goose-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/outreach/capabilities/email-drafting .claude/skills/email-drafting && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
email-drafting
GitHub stars
1.2k
Used in
1 other repo
Token cost
~3.8k tokens
SKILL.md length
1,999 words
Files
2
Skills in repo
273
Repo updated
First seen
Licence
MIT

At a glance

Write high-converting cold emails using structured frameworks, personalization tiers, and patterns from real campaigns.

  • Works in 3 steps: Intake → Draft Emails → Review & Refine
  • Tasks that involve Cold outreach
  • SKILL.md covers When to Use, Phase 0: Intake, Phase 1: Draft Emails and Phase 2: Review & Refine, plus 2 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Email Drafting is an agent skill from gooseworks-ai/goose-skills. Write high-converting cold emails using structured frameworks, personalization tiers, and patterns from real campaigns. Pure reasoning skill — no scripts. Auto-loads when any task requires outreach copy.

Its SKILL.md is about 3.8k tokens, which your agent loads only when the skill is triggered. The skill folder holds 1 other file (for example `skill.meta.json`).

It sits in Sales & Support, covering Cold outreach. The repository describes itself as: Library of Growth & GTM skills + data APIs for Claude Code, Codex, Cursor to run ads, social, content, lead gen, seo and data scraping. The licence is MIT.

When your agent uses it

  • Tasks that involve Cold outreach

Example prompts

  • “/email-drafting”

Workflow steps

3 steps, taken from the step headings in SKILL.md.

  1. Intake
  2. Draft Emails
  3. Review & Refine

What it can do on your machine

Read from SKILL.md and the folder at commit c650c6d. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md.

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Email Drafting loads about 3.8k tokens when it runs. Until then it costs about 55 tokens; SKILL.md has 1,999 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~55
When it runs · the whole SKILL.md, loaded when a task matches
~3.8k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from gooseworks-ai/goose-skills at commit c650c6d, republished under its MIT licence (© gooseworks-ai). 1,999 words, ~3,847 tokens.

Download SKILL.mdSave it as .claude/skills/email-drafting/SKILL.md (or your agent's skills folder). This skill also uses 1 other file; get the full folder from GitHub.
name
email-drafting
description
Write high-converting cold emails using structured frameworks, personalization tiers, and patterns from real campaigns. Pure reasoning skill — no scripts. Auto-loads when any task requires outreach copy.
tags
outreach

Email Drafting

Pure reasoning skill for writing cold emails. No scripts, no tools — just frameworks, patterns, and examples from real campaigns that consistently generate replies.

When to Use

Use this skill when:

  • User says "write a cold email", "draft outreach", "help me with email copy", "write a sequence"
  • Any task requires cold email copy — subject lines, full sequences, or individual emails

Phase 0: Intake

Collect campaign context before writing anything. Ask all questions at once, organized by category. Skip any the user has already answered.

Campaign Context
  1. What product/service are you selling?
  2. What problem does it solve? Who feels this pain most acutely?
  3. What's the campaign angle? (hiring signal, competitor displacement, pain-based, event-triggered, etc.)
  4. Is there a specific signal or trigger? (job posting, G2 review, LinkedIn engagement, funding round, etc.)
Audience
  1. Who is the recipient? (title, seniority, department)
  2. What keeps them up at night? (daily frustrations relevant to your product)
  3. What objections will they have? (budget, switching cost, "we already have X", timing)
Proof & Credibility
  1. What social proof do you have? (customer logos, case studies, metrics)
  2. Name 2-3 peer companies the recipient would recognize as similar to them
  3. Any hard metrics? (cost savings, speed improvement, % lift)
Tone & Style
  1. What tone fits? (casual-direct, professional-sharp, provocative, empathetic)
  2. Who is the sender? (founder, AE, SDR — this affects voice)
  3. Any brand guidelines or words to avoid?
Sequence
  1. How many touches? (default: 3)
  2. What's the desired CTA? (call, demo, reply, resource download)
  3. Email-only or multi-channel? (email + LinkedIn, email + phone)

Phase 1: Draft Emails

Email Structure Formula

Every cold email follows this skeleton:

Hook (1 sentence) → Evidence (1-2 sentences) → Offer (1 sentence)

Word count targets:

  • Cold intro (Touch 1): 50-90 words
  • Follow-up (Touch 2-3): 30-50 words
  • Breakup (final touch): 20-40 words
Frameworks

Pick the framework that matches the campaign angle:

FrameworkStructureBest For
PASProblem → Agitate → SolvePain-based signals (complaint posts, operational friction)
BABBefore → After → BridgeAspirational buyers (growth-stage, scaling companies)
AIDAAttention → Interest → Desire → ActionCold database outreach (no specific signal)
Signal-Proof-AskSignal → Proof → Soft askSignal-based campaigns (hiring, engagement, events)
Personalization Tiers

Choose based on campaign size and expected ROI per lead:

TierWhat It MeansLead VolumeExpected Reply Rate
Tier 1 (Generic)Merge fields only ({first_name}, {company}). Same template for everyone.500+ leads1-3%
Tier 2 (Segment)Industry/role-specific pain points + proof swaps. One template per segment.50-500 leads3-7%
Tier 3 (Deep)Reference a specific signal (their post, comment, job posting, news). Unique per lead.1-50 leads8-20%
Subject Line Patterns

8 proven patterns from real campaigns:

#PatternExample
1Signal reference"Before you fill that [role] role"
2Peer framing"What TIA members are doing with AI workers"
3Question"Is [Company] still [doing thing product fixes]?"
4Replacement"Looking for a [competitor] replacement?"
5Data hook"$150K agency study → $8K. 48 hours."
6Empathy"When [event that affected them]"
7Direct"[Topic] for [Company]"
8Curiosity"How [peer company] did [interesting thing]"

Rules for subject lines:

  • Under 50 characters
  • No ALL CAPS, no exclamation marks, no emoji
  • No "quick question" or "touching base"
Tone Guidance
ToneWhen to UseVoice Example
Casual-DirectSDR sending to peers, startup-to-startup"Hey — saw your post. We work on the same problem."
Professional-SharpEnterprise outreach, VP+ recipients"I wanted to reach out because [specific reason]."
ProvocativeCompetitive displacement, challenger positioning"Your current tool is costing you more than you think."
EmpatheticOrphan capture, pain-based outreach"I know switching platforms mid-cycle is brutal."
Sequence Design Principles
TouchTimingPurposeLengthNotes
Touch 1Day 1Hook + proof + soft CTA50-90 wordsThe only email that can be longer
Touch 2Day 3-5New angle or asset30-50 wordsDifferent proof point, not a "bump"
Touch 3Day 7-10Different proof point or social20-40 wordsShorter = better this late
Touch 4Day 14-21Breakup (optional)20-30 wordsRemove pressure, leave door open

Sequence rules:

  • Never repeat the same CTA across touches
  • Each touch needs a new reason to reply
  • Later touches = shorter emails
  • Never send a "just checking in" or "bumping this" — add value or stop
Hard Rules

These are non-negotiable. Every email must pass all 10:

  1. No filler openers. Never "I hope this finds you well", "I hope you're having a great week", "just reaching out"
  2. No "just checking in" follow-ups. Every touch adds a new reason to reply
  3. Max 4 paragraphs per email. Most should be 2-3
  4. Every email references something specific to the recipient. Title, company, signal, industry — never fully generic
  5. Exactly one CTA per email. Always low-friction (15-min call, "worth a look?", "open to chatting?")
  6. Never lie about how you found them. If it was a database search, don't say "I came across your profile"
  7. No filler words. Ban: synergy, leverage, circle back, loop in, touch base, align, ping
  8. Subject lines under 50 chars. No caps, no exclamation marks, no emoji
  9. No selling in the first sentence. Lead with them, not you
  10. Sign off simply. Name only, or Name + one-line title. No "Best regards", no "Looking forward to hearing from you"

Phase 2: Review & Refine

  1. Present 3-5 draft variants for Touch 1 (different angles/frameworks)
  2. Ask user to pick a direction or combine elements
  3. Generate the full sequence based on chosen direction
  4. Iterate max 3 rounds — after that, ship it

Example Library

Real emails from real campaigns. Use these as structural templates — swap product, proof, and signal for the current campaign.

Type 1: Signal-Based (Hiring)

Signal source: LinkedIn Jobs — company posting for role that product replaces Framework: Signal-Proof-Ask

Subject: Before you fill that {role_title} role

Hi {first_name} — I noticed you're hiring for a {role_title} at {company}. Before you finalize that hire, worth a quick look at what companies like DHL, Werner, and MODE Global are doing instead — deploying AI workers for exactly this function.

4x cheaper than a BPO equivalent. 10x the call capacity. Runs 24/7. Happy to send over a quick overview or jump on a 15-minute call if the timing is right.

Why it works: Opens with their specific hiring signal (proves relevance). Names peer companies (social proof). Quantifies the alternative (4x, 10x). Low-friction CTA.


Type 2: Signal-Based (Peer/Association)

Signal source: Industry association membership directory Framework: BAB (Before → After → Bridge)

Subject: What TIA members are doing with AI workers

Hi {first_name} — as a TIA member running a serious freight brokerage, you're probably seeing the same thing we are: the best operators are rethinking how they staff carrier-facing functions.

Companies like Werner, MODE Global, and Circle Logistics — TIA members you'd recognize — have already deployed AI workers for carrier calls, check calls, and load coordination. I wanted to reach out because HappyRobot works specifically with freight brokers at this scale.

Worth a 15-minute call to see if it's relevant for {company}?

Why it works: Peer framing ("TIA members you'd recognize") creates belonging pressure. Names real members using the product. Positions as industry-specific, not generic.


Show full SKILL.md (845 more words)Show less
Type 3a: Signal-Based (Competitor Engagement — Liker)

Signal source: LinkedIn Sales Navigator — engaged with competitor company page content Framework: Signal-Proof-Ask

Subject: The other side of freight AI

Hi {first_name} — I noticed you've been following the freight AI space and wanted to reach out. HappyRobot is the other side of that conversation — we're the platform 8 of the top 10 US freight brokers including DHL and Werner run for AI carrier calls.

If you're evaluating options in this space, I'd love to show you what enterprise-grade looks like. 15 minutes?

Why it works: Acknowledges their research without calling out the competitor by name in email (saves that for LinkedIn). "Other side of that conversation" is intriguing. Proof point (8 of top 10) is specific and strong.


Type 3b: Signal-Based (Competitor Engagement — Commenter)

Signal source: Same as 3a but lead left a comment (higher intent) Framework: Signal-Proof-Ask with comment reference

Subject: Re: your comment on freight AI

Hi {first_name} — I saw your comment on {competitor}'s post about {topic}. That's exactly the problem we work on. HappyRobot powers AI carrier calls for DHL, Werner, and 8 of the top 10 US freight brokers.

If you're actively looking at this, worth 15 minutes to see the other option?

Why it works: References their exact comment (Tier 3 personalization). "Actively looking at this" matches their behavior. Even shorter than 3a because higher-intent leads need less convincing.


Type 4: Pain-Based (Commenter)

Signal source: LinkedIn post search for pain-language keywords Framework: PAS (Problem → Agitate → Solve)

For commenters (wrote about the pain):

Hi {first_name}, saw your comment on the {source} post about {pain_topic}. "{comment_snippet}..." — that resonated.

We've been working with brokers who had the same issue. HappyRobot handles {relevant_task} so your team doesn't have to.

Worth a quick look?

For reactors (liked/reacted to pain content):

Hi {first_name}, noticed you've been following the conversation around {pain_topic} on LinkedIn.

If {company} is dealing with {specific_pain}, we might be able to help. HappyRobot automates {relevant_task} for freight brokers — no extra headcount needed.

Open to a quick chat?

Why it works: Quotes their own words back to them (strongest personalization). Empathetic tone — no hard sell. Short. The reactor version is lighter-touch because the signal is weaker.


Type 5: Competitive Displacement

Signal source: G2 reviews (1-star), public complaints, known competitor customers Framework: PAS

Subject: UserTesting alternatives in 2026

{first_name} — I'll keep this short. If you've run into participant quality issues with UserTesting, you're not alone. HubSpot and Glassdoor both moved to Outset after hitting the same wall.

We use Prolific-verified participants instead of a general panel. Different quality tier entirely. Worth 20 minutes to compare?

Why it works: Validates their frustration without bashing the competitor. Names companies they'd respect. Explains the "why" behind the switch (Prolific-verified participants). CTA is specific (20 minutes, comparison framing).


Type 6: ABM / Enterprise

Signal source: Target account list, vertical-specific research Framework: AIDA

Touch 1:

{first_name} — WeightWatchers cut their per-study research cost from $150K to $8K using Outset's AI-moderated interviews. They run 3x the studies they used to, in half the time.

I put together a quick breakdown of what this could look like for {company}'s {vertical} research. Worth 20 minutes?

Touch 3 (Day 7):

Following up with something concrete — built a rough ROI model using {industry} benchmarks. Even conservative numbers show a 6-8x reduction in per-study cost.

Happy to walk through the math. 15 minutes?

Touch 5 (Day 14):

Last note from me. If research cost or speed isn't a priority right now, totally understand. But if it is — the WeightWatchers and HubSpot numbers are worth seeing.

Open to a quick benchmark call? No demo, just data.

Why it works: Leads with a specific customer story and hard numbers. Each touch adds new value (case study → ROI model → benchmark offer). Breakup touch removes pressure. CTA evolves: overview → walk-through → benchmark.


Type 7: Follow-Up Patterns (Touch 2+)

New angle (not a bump):

{first_name} — different angle from my last email. {Peer company} just published results from their first quarter using {product}: {specific metric}. Thought it might be relevant given {company}'s {situation}.

Asset-led:

Put together a one-page breakdown of {topic relevant to them}. No pitch — just data. Want me to send it over?

Social proof drop:

Quick update — {new customer} just went live with us last week. Similar setup to {company}. Happy to share what their onboarding looked like.

Breakup:

I'll keep this short — if the timing isn't right, no worries at all. But if {problem} comes back up, I'm an easy call away. Cheers.


Type 8: Re-engagement (90-Day)

Signal source: Outreach log — leads contacted 90+ days ago with no reply, or leads who filled the role your product replaces Framework: Signal-Proof-Ask

Hi {first_name} — we chatted about 3 months ago when you were hiring for {role}. Curious how it's going. If you're still feeling the pain on {problem}, a few things have changed on our end worth seeing.

15 minutes to catch up?

Why it works: References the original conversation and signal. Acknowledges time has passed. "A few things have changed" creates curiosity without overselling.

Output Format

When delivering email drafts, use this structure:

Single Email
**Subject:** [subject line]
**Personalization tier:** [1/2/3]
**Framework:** [PAS/BAB/AIDA/Signal-Proof-Ask]
**Word count:** [X words]

---

[Email body with merge fields in {curly_braces}]

---

**Merge fields used:** {first_name}, {company}, {role_title}, ...
Full Sequence
## Sequence: [Campaign Name]

**Touches:** [N]
**Personalization tier:** [1/2/3]
**Tone:** [casual-direct / professional-sharp / provocative / empathetic]

### Touch 1 — Day 1
**Subject:** [subject]
**Framework:** [framework]

[body]

### Touch 2 — Day [N]
**Subject:** Re: [original subject] OR [new subject]
**Framework:** [framework]

[body]

### Touch 3 — Day [N]
**Subject:** [subject]

[body]

---

**Merge fields:** {first_name}, {company}, ...
**Notes:** [any special instructions for the outreach tool]

© gooseworks-ai, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 1 other file in skills/outreach/capabilities/email-drafting of gooseworks-ai/goose-skills.

  • SKILL.md
  • skill.meta.json

Open the folder on GitHubat commit c650c6d

Used in 1 other repository

We found 1 copy of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in gooseworks-ai/goose-skills, which our catalogue first saw on October 7, 2026.

Compare with similar skills

Email Drafting next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Email Drafting compared with similar skills
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Email Drafting this skillgooseworks-ai/goose-skills1.2k1 repos~3.8kAutomated safety check: PassMIT
Cold Outbound Optimizerericosiu/ai-marketing-skills3.6k1 repos~1.7kAutomated safety check: PassMIT
Prospectingcoreyhaines31/marketingskills54k—~5kAutomated safety check: PassMIT
Sales OsromangojiberryAI/gojiberryai-sales-os139—~2kAutomated safety check: PassMIT
ProspectingCesarjoquin/Marketing-Skills2021 repos~3.8kAutomated safety check: PassMIT
Cold Outreach Personalizeraiskilloftheweek/claude-ai-skill-of-the-week149—~2.6kAutomated safety check: PassNone

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Categories

Questions about Email Drafting

What does Email Drafting do?

Write high-converting cold emails using structured frameworks, personalization tiers, and patterns from real campaigns. Email Drafting is an agent skill from gooseworks-ai/goose-skills. Write high-converting cold emails using structured frameworks, personalization tiers, and patterns from real campaigns.

When should I use Email Drafting?

Email Drafting fits situations like: tasks that involve Cold outreach.

How do I install Email Drafting in Claude Code?

Run `npx skills add gooseworks-ai/goose-skills --skill email-drafting -a claude-code`. Or copy the skill folder (skills/outreach/capabilities/email-drafting in gooseworks-ai/goose-skills) into .claude/skills/email-drafting in your project. Claude Code loads it when a task matches its description.

How do I install Email Drafting in Codex?

Run `npx skills add gooseworks-ai/goose-skills --skill email-drafting -a codex`. Or copy the skill folder (skills/outreach/capabilities/email-drafting in gooseworks-ai/goose-skills) into .agents/skills/email-drafting in your project. Codex loads it when a task matches its description.

Can I use Email Drafting in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add gooseworks-ai/goose-skills --skill email-drafting -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/email-drafting, .gemini/skills/email-drafting, .github/skills/email-drafting and .opencode/skills/email-drafting in your project.

What does Email Drafting need to run?

SKILL.md names no scripts, command-line tools or credentials: Email Drafting is instructions for the agent only.

Does Email Drafting access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Email Drafting safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Email Drafting use?

Email Drafting is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Email Drafting use?

About 3.8k tokens (SKILL.md is roughly 15k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Email Drafting?

Skills that share tags, products or a category with Email Drafting: Cold Outbound Optimizer (ericosiu/ai-marketing-skills, 3.6k stars), Prospecting (coreyhaines31/marketingskills, 54k stars), Sales Os (romangojiberryAI/gojiberryai-sales-os, 139 stars) and Prospecting (Cesarjoquin/Marketing-Skills, 202 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Email Drafting?

gooseworks-ai (a GitHub organization) maintains it in gooseworks-ai/goose-skills, which has 1,240 GitHub stars. The repository holds 273 skills in this directory. The repository was last updated on October 8, 2026.

Source: gooseworks-ai/goose-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.