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Win Loss Reasons

Organization
pmalliance
win-loss-reasons

Synthesize why you win and lose deals from your full set of sales call transcripts, deal notes, and CRM close reasons at once. Separates what buyers said out loud from what the pattern of evidence actually shows, and ties every finding back to the calls behind it. Use when you have a pile of win/loss material nobody has had time to read across, when sales keeps losing to the same competitor and no one can explain why, or when you need win/loss findings you can defend in front of leadership. Triggers: "why are we losing deals," "win/loss analysis," "read all these call transcripts," "what's the real reason we lost," "win rate against [competitor]."

Overview

Publisherpmalliance
Repositoryproduct-marketing-skills
Skill namewin-loss-reasons
Stars
77
Forks
12
Bundled files
Instructions only
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.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

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

Installation

Install the Win Loss Reasons 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/pmalliance/product-marketing-skills/tree/main/pmm-signal-pack/skills/win-loss-reasons
  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/pmalliance/product-marketing-skills.git /tmp/product-marketing-skills
mkdir -p .claude/skills
cp -r /tmp/product-marketing-skills/pmm-signal-pack/skills/win-loss-reasons .claude/skills/win-loss-reasons
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Win Loss Reasons 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 Win Loss Reasons 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 Win Loss Reasons 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.

Win & Loss Reasons

What this does

Reads your entire body of deal evidence in one pass and reports the real reasons you win and lose, ranked by how often each reason actually shows up - not by how loud it was in the last deal anyone remembers.

The output separates two things that usually get blurred together:

  • What buyers said - the reason the buyer or rep named for the deal ("too expensive," "went with a competitor").
  • What the pattern shows - what many deals read together reveal was really going on (e.g. price was named, but the transcripts show the loss tracked to a missing integration raised in the second call).

What buyers said is where teams stop. What the pattern shows is where the useful work is. This skill is built to get you to the second one.

Why this is a Fable-recommended job

Real win/loss lives across dozens of long transcripts. The value only appears when you can hold all of them at once and compare across the set - a reason that shows up in one deal is an anecdote, the same reason across nine deals is a finding. Claude Fable 5 can hold that whole evidence base in a single run and cross-reference it, which is exactly what this needs.

If Fable is not available or the budget is spent: run on your everyday model in batches. Feed 8-10 transcripts per batch, produce a partial reason table for each batch using the format below, then paste those partial tables back in and ask for a merged, de-duplicated final table. You lose some cross-deal nuance but keep the method.

Evidence to gather first

Point the skill at as much of this as you have. More material makes the ranking more trustworthy.

  • Sales call and demo transcripts (Gong, Fireflies, Zoom, notes)
  • Closed-won and closed-lost deals with any recorded reason
  • CRM opportunity notes and stage history
  • Deal desk / forecast call notes
  • Any post-mortem write-ups

If you have a .agents/product-marketing-context.md file from the Product Marketing pack, read it first for ICP, competitors, and positioning so findings are framed against your actual market.

Process

  1. Confirm the evidence set and the time window (e.g. last two quarters). Note how many deals and transcripts are included - the report will state this.
  2. Read every source. For each deal, capture: outcome (won/lost/no-decision), the reason it was recorded as won or lost on, and the moments in the transcript that reveal what actually moved the deal.
  3. Cluster reasons across all deals. Merge duplicates that use different words for the same thing.
  4. For each reason, separate what buyers said from what the pattern shows, and count how many deals it appears in.
  5. Flag competitor-driven losses and group them by competitor.
  6. Produce the report.

Output format

A single document with:

  • Summary - deals analyzed, time window, headline win reasons and loss reasons.
  • Loss reasons table - reason | said or shown by pattern | number of deals | representative quote with source | so-what for PMM.
  • Win reasons table - same columns.
  • Competitor breakdown - for each competitor you lost to: pattern, the point in the deal it turned, and the counter that appears to work.
  • What this changes - the two or three positioning, messaging, or enablement moves the evidence supports.

The evidence standard

Every reason in the tables must cite at least one specific source - the deal name, call date, or transcript line it came from. If a claim cannot be traced to the material, it does not go in the report; it goes in a short "worth checking" list at the end. Do not infer reasons the evidence does not support, and do not smooth a messy finding into a clean one.

Frequently asked questions

What does the Win Loss Reasons AI skill do?

Synthesize why you win and lose deals from your full set of sales call transcripts, deal notes, and CRM close reasons at once. Separates what buyers said out loud from what the pattern of evidence actually shows, and ties every finding back to the calls behind it. Use when you have a pile of win/loss material nobody has had time to read across, when sales keeps losing to the same competitor and no one can explain why, or when you need win/loss findings you can defend in front of leadership. Triggers: "why are we losing deals," "win/loss analysis," "read all these call transcripts," "what's...

Why use Win Loss Reasons on TypingMind?

Because you install it once and use it with any model. Win Loss Reasons 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 Win Loss Reasons in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/pmalliance/product-marketing-skills/tree/main/pmm-signal-pack/skills/win-loss-reasons. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Win Loss Reasons?

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 Win Loss Reasons?

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

Is the Win Loss Reasons AI skill free?

Yes. It is published on GitHub by pmalliance 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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