Objection Library logo

Objection Library

Organization
pmalliance
objection-library

Extract every objection buyers raise across your full set of sales calls, rank them by how often they come up, capture them in the buyer's own words, and tag each to the point in the funnel where it lands. Use when you want the real objections instead of the three sales happens to remember, when building or refreshing objection handling and enablement, or when you have a stack of call recordings and no time to listen through them. Triggers: "what objections are we getting," "objection handling," "build a rebuttal doc," "what's blocking deals," "sales enablement from calls."

Overview

Publisherpmalliance
Repositoryproduct-marketing-skills
Skill nameobjection-library
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 Objection Library 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/objection-library
  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/objection-library .claude/skills/objection-library
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Objection Library 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 Objection Library 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 Objection Library 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.

Objection Library

What this does

Reads all of your sales conversations and returns a ranked library of the objections buyers actually raise - each one in the buyer's own language, counted by frequency, and tagged to the stage of the funnel where it shows up. It also records how reps currently respond and whether that response tends to move the deal forward.

The point is coverage. Teams usually build objection handling from the handful of objections that stung most recently. This gives you the full set, weighted by how common each one really is.

Why this is a Fable-recommended job

Objections are scattered across many hours of talk. To rank them honestly you have to hear all of them and count across the whole set, which means holding every transcript at once. Claude Fable 5 can take the full call library in a single run and surface patterns no one person sitting through calls would tally.

If Fable is not available or the budget is spent: batch it. Run 8-10 calls at a time, produce a partial objection list per batch, then merge and re-rank the partial lists in a final pass.

Evidence to gather first

  • Sales and demo call transcripts across the funnel (discovery through negotiation)
  • Objection notes from reps or deal desk
  • Support and onboarding calls, if pre-sale concerns surface there
  • Lost-deal notes

Read .agents/product-marketing-context.md first if it exists, so objections can be mapped against your positioning and competitors.

Process

  1. Confirm the call set and window. Report how many calls are included.
  2. Read every call. Pull each moment where a buyer raises a concern, hesitation, or blocker - stated as a question, a worry, or a flat no.
  3. Normalize duplicates: the same underlying objection phrased three ways becomes one entry, with the variant phrasings kept as examples.
  4. Count how many calls each objection appears in and tag the funnel stage.
  5. Capture the current rep response for each and note where a response seems to land or fall flat.
  6. Produce the library.

Output format

  • Summary - calls analyzed, window, top objections by frequency and by funnel stage.
  • Objection table - objection (in buyer language) | funnel stage | number of calls | representative quote with source | current response | how well it seems to work.
  • Gaps - objections with no consistent response, or where the current response is weak.
  • Recommended handling - a suggested response for the top objections, grounded in what already works in the winning calls.

The evidence standard

Every objection entry must quote at least one real buyer moment and cite its source (call name, date, or line). Use the buyer's words, not a tidied-up paraphrase. If frequency cannot be counted reliably from the material, say so rather than inventing a number. Recommended responses should be built from responses that appear to work in your own calls before any that are added from general practice - and anything added from general practice must be labeled as such.

Frequently asked questions

What does the Objection Library AI skill do?

Extract every objection buyers raise across your full set of sales calls, rank them by how often they come up, capture them in the buyer's own words, and tag each to the point in the funnel where it lands. Use when you want the real objections instead of the three sales happens to remember, when building or refreshing objection handling and enablement, or when you have a stack of call recordings and no time to listen through them. Triggers: "what objections are we getting," "objection handling," "build a rebuttal doc," "what's blocking deals," "sales enablement from calls."

Why use Objection Library on TypingMind?

Because you install it once and use it with any model. Objection Library 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 Objection Library 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/objection-library. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Objection Library?

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 Objection Library?

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

Is the Objection Library 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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