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Predictable Revenue

OrganizationPopular
wondelai
predictable-revenue

Build a scalable outbound B2B sales machine with specialized roles (SDR, AE, CSM). Use when the user mentions "outbound sales", "Cold Calling 2.0", "cold email sequences", "sales pipeline", "SDR process", "sales development", "build an outbound sales team", or "fill my pipeline". Also trigger when setting up a B2B SaaS sales team from scratch or building a lead-qualification framework to improve close rates. Covers the three lead types (seeds/nets/spears), role specialization, the referral-email method, ANUM qualification, and pipeline math. For offer design, see hundred-million-offers. For persuasion science, see influence-psychology.

Overview

Publisherwondelai
Repositoryskills
Skill namepredictable-revenue
Stars
2.5K
Forks
249
Bundled files
8
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.

  • 8 bundled files

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

  • Open source

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

Installation

Install the Predictable Revenue 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.
    https://github.com/wondelai/skills/tree/main/plugins/sales-influence/skills/predictable-revenue
  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/wondelai/skills.git /tmp/skills
mkdir -p .claude/skills
cp -r /tmp/skills/plugins/sales-influence/skills/predictable-revenue .claude/skills/predictable-revenue
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Predictable Revenue 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 Predictable Revenue 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 Predictable Revenue 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.

Predictable Revenue Framework

A systematic approach to building a scalable, predictable B2B sales machine — the outbound prospecting system that helped Salesforce add $100M in recurring revenue.

Core Principle

Predictable lead generation drives predictable revenue. The biggest mistake in sales is having the same people prospect AND close — specialization creates a repeatable, scalable machine. Traditional cold calling is dead; Cold Calling 2.0 (mass, personalized cold emails that generate referrals to the right person) is the new outbound.

Scoring

Goal: 10/10. Score a sales process 0-10 by awarding 2 points for each of the five Quick Diagnostic rows it satisfies (prospecting/closing separated, defined outbound process, 3-month pipeline predictability, known lead-type mix, standardized SDR→AE handoff). Bands: 9-10 = role separation plus a repeatable process that predicts pipeline; 5-6 = some specialization but ad-hoc prospecting or unpredictable pipeline; ≤3 = one person prospects and closes, revenue depends on heroics. Always give the current score and the specific diagnostic rows blocking 10/10.

The Three Types of Leads

Not all leads are equal — treat them differently.

TypeSourceConversionCostExample
SeedsWord of mouth, referrals, organicHighestLowest (takes time)Customer referral, NPS-driven
NetsMarketing campaigns, inboundMediumMediumContent, SEO, webinars
SpearsOutbound prospectingLower but predictableHigher (people-intensive)Cold Calling 2.0

Key insight: Most companies over-invest in nets and under-invest in spears; seeds are the best but can't be manufactured quickly. Invest accordingly — customer success and referral programs (seeds), content and paid acquisition (nets), SDR team (spears).

See references/lead-types.md when deciding where to invest — it sizes the seeds/nets/spears budget split by company stage.

Sales Role Specialization

The #1 principle: separate prospecting from closing. When AEs prospect and close, they hate prospecting and pipeline becomes feast-or-famine.

RoleFocusMetrics
SDR (Sales Development Rep)Outbound prospecting → qualified opportunitiesQualified meetings/month
MDR (Market Development Rep)Inbound lead qualificationQualified leads/month
AE (Account Executive)Close dealsRevenue closed, win rate
CSM (Customer Success Manager)Retain and grow accountsRetention, expansion revenue

SDR (Sales Development Rep)

Generate qualified pipeline: research target accounts, send Cold Calling 2.0 emails, get referred to the right person, qualify with ANUM, pass to AEs. Not their job: closing, inbound leads, or existing customers. One SDR typically generates 10-20 qualified opportunities per month — measure opportunities, response rate, meetings booked, and pipeline value.

AE (Account Executive)

Close deals from qualified pipeline: run discovery, demo, negotiate, close, hand off to CSM. Not their job: prospecting (SDR), inbound qualification (MDR), or post-sale management (CSM). Measure revenue closed, win rate, average deal size, and sales cycle length.

CSM (Customer Success Manager)

Retain and grow accounts: onboard, drive adoption, surface expansion opportunities, prevent churn. Measure net revenue retention, churn rate, expansion revenue, and NPS/CSAT.

The virtuous cycle: SDR generates pipeline → AE closes → CSM retains/grows → happy customer refers (Seeds).

See references/roles.md when defining a new role or org chart — it has full charters, career paths, and hiring profiles for each role.

Cold Calling 2.0

Outbound prospecting that replaces traditional cold calling, which fails on every front: 1-3% connection rate, gatekeepers, brand damage, no scalability.

1. Build list → 2. Send mass email → 3. Get referral → 4. Call the referral → 5. Qualify

Step 1: Build Target Account List

Define your Ideal Customer Profile (company size, industry, tech stack, geography, pain points), then build the list via LinkedIn Sales Navigator, ZoomInfo/Apollo/Clearbit, or industry directories. Target 200-500 accounts per SDR per quarter.

Step 2: The Referral Email

The core innovation: don't email the decision maker — email above them and ask for a referral down. Senior people forward emails, and referrals get 3-5x higher response because the introduction comes from inside the company. The email asks one thing — "who is the right person?" — under 100 words, no pitch, no attachments, no links, easy to forward. Response rate: 9-15% vs. 1-3% for traditional cold emails. For the verbatim template, subject-line open-rate table, and the full Day 1/3/7/14/30 sequence bodies, open references/cold-calling-2.md.

Step 3: Follow Up

DayAction
1Send referral email
3Follow up if no response
7Second follow-up (different angle)
14Break-up email ("Should I close your file?")
30Re-engage (new trigger event or content)

The break-up email on Day 14 often draws the highest response in the sequence — people respond to losing the opportunity (scarcity).

Step 4: Qualify with ANUM

CriteriaQuestionStrong SignalWeak Signal
AuthorityCan this person decide?Decision maker or strong influencerNo buying power
NeedDo they have the problem you solve?Active pain, seeking solutions"Nice to have"
UrgencyWhen must they solve it?This quarter, budget allocated"Someday"
MoneyCan they afford it?Budget exists, within rangeNo budget, too expensive

Call structure: rapport (2 min) → set agenda ("understand your situation, see if there's a fit") → discovery questions with ANUM built in (10-15 min) → next steps (if qualified, schedule AE demo).

Step 5: Hand Off to AE

Include account background and ICP match, contact details and role, pain points, ANUM notes, agreed next steps, and competitive intel. SDR introduces AE on a brief 3-way call or email, then drops off.

Ethical boundary: Comply with spam laws (CAN-SPAM, GDPR) and honor opt-outs immediately — including removing a hostile "stop emailing me" from the sequence on the spot, not at the next scheduled touch.

See references/qualification.md when running the ANUM discovery call — it has the full discovery question bank per criterion.

Pipeline Math

Work backward from the revenue goal:

Revenue Goal ÷ Average Deal Size = Deals Needed
Deals Needed ÷ Win Rate = Opportunities Needed
Opportunities Needed ÷ SDR Conversion = Prospects Needed
Prospects Needed ÷ Response Rate = Emails Needed

Example: $1M ARR ÷ $20K deals = 50 deals; ÷ 25% win rate = 200 opportunities; at 10% response rate and 10% response-to-qualified conversion = 20,000 emails ≈ 2-3 SDRs (each sends 300-500/month).

MetricBenchmark
Emails per SDR per day50-100
Response rate9-15%
Qualified opportunities per SDR per month10-20
AE demo-to-close rate20-30%
Average sales cycle30-90 days

See references/pipeline-math.md when sizing the SDR team to a revenue target — it has the full capacity-planning and revenue-modeling templates.

Building the Sales Development Team

See references/case-studies.md when you want a worked precedent for standing up or scaling the machine — it walks through Salesforce, HubSpot, and other implementations with starting state, results, and failure modes.

Hiring SDRs

Hire for coachability (the most important trait), curiosity, strong writing, resilience, and organization — experience is optional. Source recent graduates, career changers, and internal transfers. Career path: SDR (6-18 months) → Senior SDR → AE or SDR Manager.

SDR Ramp Time

PhaseTimelineExpectations
TrainingWeeks 1-2Product knowledge, tools, process
ShadowingWeeks 3-4Observe experienced SDRs, practice
RampingMonths 2-350% of quota
Full quotaMonth 4+100% of quota

Expect 3-4 months to full productivity.

SDR Compensation

Base + variable, typically 60/40 or 70/30. Pay variable per qualified opportunity generated, with bonuses for opportunities that close and for exceeding quota.

See references/team-building.md when hiring or building the comp plan — it has interview scorecards, the onboarding curriculum, and detailed compensation structures.

Metrics and Dashboards

Leading Indicators (Predictive)

Emails sent per SDR per day, response rate, meetings booked per week, qualified opportunities per month, pipeline value generated.

Lagging Indicators (Results)

Revenue closed, win rate, average deal size, sales cycle length, customer acquisition cost (CAC).

Efficiency Metrics

Cost per qualified opportunity, SDR:AE ratio (typically 2-3 SDRs per AE), LTV:CAC (target >3:1), payback period.

Cadence: daily activity metrics → weekly pipeline → monthly revenue → quarterly efficiency.

See references/metrics.md when building the sales dashboard — it has KPI definitions, formulas, and dashboard layouts by cadence.

Common Mistakes

MistakeWhy It FailsFix
AEs prospectingFeast-or-famine pipelineHire dedicated SDRs
Long, pitchy emailsLow response rateShort, referral-focused emails
No ICP definitionEffort wasted on wrong accountsDefine ICP before hiring SDRs
Too few SDRsNot enough pipelineWork backward from revenue goal
No hand-off processLeads fall through cracksStandardize SDR→AE handoff
Measuring activity, not resultsBusy but not productiveTrack qualified opportunities, not emails

Quick Diagnostic

Audit any B2B sales process:

QuestionIf NoAction
Are prospecting and closing separated?SDRs doing both = bottleneckCreate dedicated SDR role
Is there a defined outbound process?Ad-hoc prospectingImplement Cold Calling 2.0
Can you predict pipeline 3 months out?Revenue is unpredictableBuild pipeline math model
Do you know your lead type mix?Over-reliance on one sourceBalance seeds, nets, spears
Is SDR→AE handoff standardized?Leads lost in transitionCreate handoff checklist

Further Reading

For the complete system:

About the Author

Aaron Ross built the outbound sales process at Salesforce.com that added $100M+ in recurring revenue, and co-founded Predictable Revenue Inc. His book Predictable Revenue — known as "The Bible of Outbound Sales" — made Cold Calling 2.0 the standard for B2B outbound prospecting.

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 Predictable Revenue AI skill do?

Build a scalable outbound B2B sales machine with specialized roles (SDR, AE, CSM). Use when the user mentions "outbound sales", "Cold Calling 2.0", "cold email sequences", "sales pipeline", "SDR process", "sales development", "build an outbound sales team", or "fill my pipeline". Also trigger when setting up a B2B SaaS sales team from scratch or building a lead-qualification framework to improve close rates. Covers the three lead types (seeds/nets/spears), role specialization, the referral-email method, ANUM qualification, and pipeline math. For offer design, see hundred-million-offers. For...

Why use Predictable Revenue on TypingMind?

Because you install it once and use it with any model. Predictable Revenue 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 Predictable Revenue in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/wondelai/skills/tree/main/plugins/sales-influence/skills/predictable-revenue. 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 Predictable Revenue?

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 Predictable Revenue?

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

Is the Predictable Revenue AI skill free?

Yes. It is published on GitHub by wondelai 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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