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Cold Outbound Optimizer

CommunityPopular
ericosiu
cold-outbound-optimizer

Design, analyze, and optimize cold outbound email campaigns for Instantly. Handles end-to-end ICP definition, expert panel scoring (recursive to 90+), sequence copywriting, infrastructure audit, capacity planning, and implementation docs. Use when asked to build cold outbound sequences, optimize cold email, analyze outbound campaigns, build sales sequences, build Instantly sequences, create cold outbound strategies, or design email campaigns. Supports both "start from scratch" and "optimize existing" modes.

Overview

Publisherericosiu
Repositoryai-marketing-skills
Skill namecold-outbound-optimizer
Stars
3.5K
Forks
685
Bundled files
10
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 10 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by ericosiu on GitHub. Read the source before you install it.

Installation

Install the Cold Outbound Optimizer AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/ericosiu/ai-marketing-skills.git /tmp/ai-marketing-skills
mkdir -p .claude/skills
cp -r /tmp/ai-marketing-skills/outbound-engine .claude/skills/cold-outbound-optimizer
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Cold Outbound Optimizer in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Cold Outbound Optimizer on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Cold Outbound Optimizer is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Preamble (runs on skill start)

bash
# Version check (silent if up to date)
python3 telemetry/version_check.py 2>/dev/null || true

# Telemetry opt-in (first run only, then remembers your choice)
python3 telemetry/telemetry_init.py 2>/dev/null || true

Privacy: This skill logs usage locally to ~/.ai-marketing-skills/analytics/. Remote telemetry is opt-in only. No code, file paths, or repo content is ever collected. See telemetry/README.md.


Cold Outbound Optimizer


Startup: Determine Mode

Ask the user:

  1. Do you have an existing Instantly account with campaigns to audit, or are you starting from scratch?
  2. Do you have an Instantly API key? (Required for audit mode.)

If API key provided → run scripts/instantly-audit.py to pull campaigns, account inventory, and warmup scores before proceeding.


Phase 1: Discovery & Audit

1A — Infrastructure Check (if API key available)

Run python3 scripts/instantly-audit.py --api-key <KEY> and report:

  • Active campaigns (name, status, reply rate, open rate)
  • Sending accounts (count, warmup score, daily limit)
  • Domain inventory
  • Warmup gaps: any account with score <80 or <14 days warmup → flag as NOT ready

1B — Performance Data

  • Pull campaign analytics from Instantly
  • Ask: "Do you have a spreadsheet with historical outbound data?" If yes, request link.

1C — ICP Definition

If no ICP defined, collect:

  • Titles: Who are you targeting? (e.g., VP Marketing, Head of Growth)
  • Industries: Which verticals?
  • Company size: Employee count or revenue range?
  • Revenue floor: Minimum ARR/revenue to qualify?
  • Anti-ICP: Who to explicitly exclude?

Use references/icp-template.md as the collection template.

1D — Business Context

Collect:

  • What do you sell? (One sentence, no jargon)
  • What's the primary offer? (Free trial, audit, demo, consultation)
  • Real URLs to reference (pricing page, case studies, relevant content)
  • Any proof points? (Client results, stats, social proof)

1E — Expert Panel Config

Default: 10 experts (see references/expert-panel.md). Ask: "Any industry-specific experts to add, or panelists to swap?" Confirm roster before scoring.


Phase 2: Expert Panel Recursive Scoring

Target: 90/100. Non-negotiable. Iterate until reached.

Round Structure

Each round produces:

  1. Score table — all 10 panelists, individual score (0-100), one-line rationale
  2. Aggregate score — average of all 10
  3. Top weaknesses — ranked list of what's holding the copy back
  4. Changes made — specific edits addressing each weakness
  5. Updated copy — full revised sequence after changes

Scoring Criteria (per panelist's lens — see references/expert-panel.md)

  • Subject line curiosity / open rate potential
  • First sentence pattern interrupt
  • Body clarity and brevity
  • CTA softness and specificity
  • Sequence flow and follow-up logic
  • Deliverability risk signals (spam words, link density)
  • Personalization believability

Rules

  • Scores must be brutally honest. No padding to 90 without earning it.
  • If round score < 90: identify top 3 weaknesses, revise copy, run next round.
  • If round score ≥ 90: finalize copy and proceed to deliverables.
  • Show every round in the final doc — the iteration trail is part of the value.

Phase 3: Deliverables

Strategy Doc

Create a document (Google Doc, Notion, or markdown) with:

  1. Pre-Analysis / Brutal Truth — what the existing campaigns are doing wrong (or baseline if starting from scratch)
  2. ICP Summary — confirmed targeting parameters
  3. Infrastructure Status — account inventory, warmup readiness, capacity math
  4. Scoring Rounds — full panel vote tables for every round
  5. Final Email Copy — all steps for all campaigns, Instantly-ready format
  6. Implementation Plan — step-by-step setup instructions
  7. Capacity Math — accounts × daily send rate = pipeline projections
  8. Weekly Metrics Targets — open rate, reply rate, positive reply rate, meetings booked
  9. STOP List — what to kill immediately
  10. START List — what to launch first

Format Rules for Final Copy

Follow all rules in references/instantly-rules.md and references/copy-rules.md.

Human Review Gate

Do NOT push anything to Instantly automatically. The doc is for human review. Get explicit approval before any API writes.

Iteration

After review, collect feedback and re-run scoring on revised copy if needed.


Capacity Math Formula

Accounts ready (score ≥80, ≥14 days warmup) × 30 emails/day = conservative daily volume
Accounts ready × 50 emails/day = aggressive daily volume
Daily volume × 22 working days = monthly send capacity
Monthly sends × expected reply rate = expected replies
Expected replies × qualification rate = pipeline opportunities

Weekly Metrics Targets (Baselines)

MetricGoodGreat
Open rate40%+60%+
Reply rate3%+7%+
Positive reply rate1%+3%+
Meeting rate0.5%+1.5%+

Adjust targets based on niche and offer. Cold traffic to a free audit converts differently than a paid trial.


Add-On Recommendations (mention but don't build)

  • LinkedIn automation: HeyReach or similar for multi-channel sequences. Separate workflow.
  • Lead enrichment: Clay or Apollo for personalization data before upload.
  • Lead pipeline: Use scripts/lead-pipeline.py for Apollo → LeadMagic → Instantly automation.

Reference Files

FilePurpose
references/instantly-rules.mdVariable syntax, sequence structure, deliverability rules
references/expert-panel.mdDefault 10-expert roster with scoring lenses
references/copy-rules.mdEmail copy rules (first sentence, CTA, stats framing)
references/icp-template.mdICP data collection template
scripts/instantly-audit.pyPulls campaigns, accounts, warmup scores via Instantly v2 API
scripts/lead-pipeline.pyEnd-to-end lead sourcing pipeline
scripts/competitive-monitor.pyCompetitor tracking and intelligence
scripts/cross-signal-detector.pyMulti-source signal detection
scripts/cold-outbound-sender.pySend approved outbound emails

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Cold Outbound Optimizer AI skill do?

Design, analyze, and optimize cold outbound email campaigns for Instantly. Handles end-to-end ICP definition, expert panel scoring (recursive to 90+), sequence copywriting, infrastructure audit, capacity planning, and implementation docs. Use when asked to build cold outbound sequences, optimize cold email, analyze outbound campaigns, build sales sequences, build Instantly sequences, create cold outbound strategies, or design email campaigns. Supports both "start from scratch" and "optimize existing" modes.

Why use Cold Outbound Optimizer on TypingMind?

Because you install it once and use it with any model. Cold Outbound Optimizer is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Cold Outbound Optimizer in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/ericosiu/ai-marketing-skills/tree/main/outbound-engine. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Cold Outbound Optimizer?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Cold Outbound Optimizer?

As many as you like. As long as a model supports skills, you can use Cold Outbound Optimizer with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Cold Outbound Optimizer AI skill free?

Yes. It is published on GitHub by ericosiu under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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