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Testimonial Collector

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BrianRWagner
testimonial-collector

Systematically gather, score, and format client testimonials. Use when someone needs social proof, wants to collect feedback, needs to turn happy clients into public advocates, or asks for help requesting or drafting a testimonial.

Overview

PublisherBrianRWagner
Repositoryai-marketing-claude-code-skills
Skill nametestimonial-collector
Stars
449
Forks
103
Bundled files
1
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.

  • 1 bundled files

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

  • Open source

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

Installation

Install the Testimonial Collector 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/BrianRWagner/ai-marketing-claude-code-skills/tree/main/testimonial-collector
  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/BrianRWagner/ai-marketing-claude-code-skills.git /tmp/ai-marketing-claude-code-skills
mkdir -p .claude/skills
cp -r /tmp/ai-marketing-claude-code-skills/testimonial-collector .claude/skills/testimonial-collector
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Testimonial Collector 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 Testimonial Collector 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 Testimonial Collector 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.

Testimonial Collector

Mode

Detect from context or ask: "One script, full system, or full system with campaign?"

ModeWhat you getBest for
quick1 outreach script + 1 format templateAsking one client, right now
standardFull collection system: timing, scripts, formatting, display guidanceBuilding repeatable social proof
deepFull system + multi-channel strategy + case study pipelineSales enablement, proposal library

Default: standard — use quick if they have one client in mind. Use deep if they want a system they can hand to a VA.


Context Loading Gates

Before proceeding, gather:

  • Client name, company, and industry
  • Project type and specific deliverables
  • Key results — push for at least one number ("even a rough estimate")
  • Desired output format (short quote / medium paragraph / full narrative)
  • Urgency (this week vs. building a library)

If results are vague (e.g., "things improved"), stop and ask: "Can you name one specific number — even a rough estimate? That's what makes a testimonial credible and usable." Do not draft until you have this.

If the user wants to skip a field: note it and flag the quality impact in the output.


Phase 1: Situation Analysis

Before drafting anything, reason through:

  1. Client relationship stage: Was this a quick project or a deep engagement? Depth affects how much authentic language is available.
  2. Results clarity: Are the outcomes measurable (numbers, timelines, named outcomes) or soft (vibes, general satisfaction)?
  3. Format match: What placement does the user need this for? A homepage needs different length than a sales deck.
  4. Voice data: Does the user have existing communication from this client (emails, Slack, quotes) that can inform tone?

Output a brief situation summary:

"You have a [length] engagement with [client] in [industry], with [strong/weak] results data. I'll draft in [format] with [authentic/templated] voice. Main gap to address: [specific gap]."


Phase 2: Quality Scoring Framework

Score the raw testimonial content (or anticipated content) before drafting:

DimensionScore 1Score 3Score 5
SpecificityNo detailsVague referencesSpecific named result
Measurability"It was great""Noticeable improvement""40% increase in leads"
Authentic VoiceSounds like ad copySlightly stiltedReads exactly how a person talks
LengthToo short (no context)Decent but thinEnough for all 3 formats

Scoring rule:

  • 4+ on all 4 dimensions → ready to use
  • ≤2 on any dimension → apply iteration protocol before delivering

Phase 3: Draft Generation

The Ask Templates

Direct Ask:

Subject: Quick favor (30 seconds)

Hey [Name],

Loved working on [project] with you — especially seeing [specific result].

Would you be open to sharing a quick testimonial I could use on my site?

No pressure. If yes, I can either:
A) Send you 3 questions to answer
B) Write a draft for you to approve/edit

Whatever's easier.

Question Route:

3 quick questions:
1. What was the situation before we worked together?
2. What changed or improved?
3. Would you recommend this to others? Why?

Draft-on-Behalf Framework: Rules for writing in the client's voice:

  • Tone: Match their actual communication style (check emails/messages for vocabulary)
  • Structure: Situation Before → What Changed → Specific Result → Recommendation
  • Avoid: Superlatives without evidence ("amazing," "life-changing")
  • Avoid: Leading with praise — lead with the client's situation
  • Length: 50-75 words (short), 100-150 words (medium), 200+ (long/full)

Fill-in template:

"[Client situation in 1 sentence]. [What the engagement delivered — concrete]. 
[Specific result, ideally with a number]. [Recommendation statement in client's natural voice]."

Phase 4: Format Production

Short Format (2-liner)

"[One punchy outcome sentence — lead with the result]"
— [Name], [Title] at [Company]

Use for: Homepage, LinkedIn featured section, proposal proof points

Medium Format (2-3 sentences)

"[Problem or situation]. [What changed]. [Recommendation or result]."
— [Name], [Title] at [Company]

Use for: Services page, sales decks, email sequences

Long Format (Full narrative)

Structure:

  1. Context paragraph (2-3 sentences on the situation)
  2. Transformation paragraph (what happened during the engagement)
  3. Results paragraph (outcomes, numbers, named wins)
  4. Closing recommendation sentence

Use for: Case study pages, downloadable PDFs, high-trust sales assets


Phase 5: Self-Critique Pass (REQUIRED)

After generating all formats, evaluate:

Specificity check: Does the short version have at least one concrete outcome (not just "great results")? Voice check: Could the client have actually written this, or does it sound like a marketing headline? Placement check: Is the recommended format actually correct length for the stated use case? Ethics check: Does the draft contain any claims the client didn't make or numbers you added?

Flag any issues: "The short version lacks a specific metric — you'll need to get one number from the client before using this on a homepage."


Iteration Protocol

If the received testimonial scores ≤2 on any dimension, send this gentle follow-up:

"Thanks so much — this is great. One small ask: could you add one specific 
number or outcome? Even rough ('saved us about 5 hours a week') makes it 
much more compelling for other clients. Totally optional, but makes a real difference."

If a second request still yields nothing specific: use Tier 3 proxy language:

"noticeable improvement in [area]" or "process now runs without manual oversight"


Placement Recommendation

Always deliver a placement recommendation with the formatted testimonials:

FormatRecommended LocationsWhy
Short (2-liner)Homepage, proposals, LinkedInTrust at first glance
MediumServices page, email, sales decksOvercome late-stage objections
LongCase study page, PDF, portfolioDeep proof for serious buyers

Cross-reference: If this client has a strong story, suggest running case-study-builder to expand into a full case study.


Output Structure

markdown
## Testimonial: [Client Name] — [Date]

### Quality Assessment
- Specificity: [X/5]
- Measurability: [X/5]
- Authentic Voice: [X/5]
- Length: [X/5]
- **Total: [X/20] — [Ready to use / Needs iteration]**

### Short Format (2-liner)
"[Quote]"
— [Name], [Title], [Company]

### Medium Format
"[Quote]"
— [Name], [Title], [Company]

### Long Format
[Full narrative]

### Placement Recommendation
[Where to use each format]

### Next Step
[Iteration note OR cross-reference to case-study-builder]

Skill by Brian Wagner | AI Marketing Architect | brianrwagner.com

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 Testimonial Collector AI skill do?

Systematically gather, score, and format client testimonials. Use when someone needs social proof, wants to collect feedback, needs to turn happy clients into public advocates, or asks for help requesting or drafting a testimonial.

Why use Testimonial Collector on TypingMind?

Because you install it once and use it with any model. Testimonial Collector 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 Testimonial Collector in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/BrianRWagner/ai-marketing-claude-code-skills/tree/main/testimonial-collector. 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 Testimonial Collector?

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 Testimonial Collector?

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

Is the Testimonial Collector AI skill free?

It is published on GitHub by BrianRWagner. Check the repository for licensing terms. 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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