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Case study builder

Skill pinkpixel-dev/skills-collection-1/SKILLS/case-study-builder

Part 1 of a large AI and agent skills collection featuring 900+ reusable skill folders, prompt workflows, references, scripts, and assets across engineering, cloud, security, research, writing, design, and automation.

Install
npx -y skills add pinkpixel-dev/skills-collection-1 --skill case-study-builder

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Turn client wins into formatted case studies for proposals, social proof, and sales conversations. Use when someone needs to document results, build credibility, or create reusable proof assets.

SKILL.md

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Case Study Builder

Everyone wants social proof. Nobody makes time to create it. You finish a project, the client's happy, you move on — and six months later you're in a sales call with nothing to show.

This skill fixes that. Give me the raw details. I'll produce three formats you can use immediately.


Mode

Detect from context or ask: "Quick writeup, full case study, or full asset package?"

ModeWhat you getBest for
quick1 format: paragraph summary for proposalsFast social proof, proposal inserts
standard3 formats: proposal blurb, social proof pull-quote, sales storyActive pitching, LinkedIn, website
deep3 formats + blog-ready case study + FAQ variant + campaign assetsContent marketing, SEO, sales enablement

Default: standard — use quick if they say "just need something fast." Use deep if they want to turn the win into a marketing asset.


Context Loading Gates

Before generating anything, collect all 8 fields:

FieldWhat to Collect
1. ClientIndustry, company size/stage, named or anonymized?
2. BeforeWhat was broken or painful? Any numbers?
3. ActionsWhat did you specifically do? Scope, timeline, role
4. AfterREQUIRED: at least one specific number
5. TimelineHow long to achieve the result?
6. QuoteDirect client quote if available
7. NamingCan we name the client, or must we anonymize?
8. Use caseWhere will this be used? (proposals / website / LinkedIn)

Outcome Extraction Protocol — enforced on Field 4:

If the user says "results were good" or "things improved," stop and ask:

"I need at least one number to make this credible. Pick one:

  • Revenue change (e.g., 'closed 3 new clients worth $15K')
  • Time saved (e.g., 'cut from 10 hours to 2 hours/week')
  • Lead volume (e.g., 'went from 0 to 5 inbound leads/month')
  • Rough estimate is fine — it doesn't have to be exact."

Do not draft until Field 4 has at least one number.


Phase 1: Situation Analysis

Before drafting, reason through:

  1. Result strength: Is the outcome a Tier 1 (hard metric), Tier 2 (soft metric), or Tier 3 (proxy)? This determines how confident the language should be.
  2. Hero check: Is the story told from the client's perspective, or yours? Client = hero, you = guide.
  3. Tension check: What made this hard? Without a challenge, there's no story — just a list.
  4. Format match: Which use case did they specify? That determines which format to optimize.

Tier system for results language:

TierTypeExampleLanguage
1Hard metric"Revenue +40%"State directly
2Soft metric"Team finally aligned""For the first time in years..."
3Proxy metric"Enabled Series A close""Contributed to..."
4Directional"Noticeable improvement""Significant improvement in..."

Phase 2: The Hero Principle

The client is the hero. You are the guide.

Before writing, flip the framing:

❌ Wrong: "I built a content system that generated leads." ✅ Right: "Sarah went from scrambling to fill her pipeline to getting 3 inbound inquiries per week — all from a content system we built in 6 weeks."

Every format should be written from what the CLIENT experienced, not what YOU delivered.


Phase 3: Generate Three Formats

Format 1: Two-Liner (Proposals & Bios)

Formula: [What was done] + [scale/scope] + [for who] + [result or timeframe]

[Strong action verb] [what was delivered] for [specific client descriptor].
[Outcome metric] in [timeframe].

Example:

Built a full content system for a Series B SaaS founder with no marketing team. 0 to 3 inbound leads/week in 6 weeks.


Format 2: Story Version (LinkedIn & Sales Calls)

Structure — 4 paragraphs, 150–250 words:

**Set the scene:** [Their situation when you arrived — 2-3 sentences with stakes]

**Show the complexity:** [What made this hard — 2-3 sentences]

**What happened:** [Specific actions taken — no feature lists, just moves]

**What changed:** [Outcome — the number + the transformation]

Format 3: Full Case Study (Website & Portfolio)

# Case Study: [Client Name or Descriptor]

## The Challenge
[2-3 paragraphs: situation, stakes, what wasn't working]

## The Approach
[Phases or steps — what happened and in what order]

## The Results
[Metrics, before/after comparison, named outcomes]

## Key Details
- Client: [Named or "A [descriptor] company"]
- Industry: [Sector]
- Timeline: [Duration]
- Scope: [What was delivered]

## What Made This Different
[Unique angle, unexpected obstacle, or pivotal insight]

## Client Quote
> "[Testimonial — or placeholder if not yet collected]"
> — [Name], [Title]

Phase 4: Self-Critique Pass (REQUIRED)

After generating all three formats, evaluate:

Two-liner:

  • Does it have a specific number? (Not "improved results" — a real metric)
  • Is it 2 sentences or fewer?
  • Would someone scanning a proposal stop and read it?

Story version:

  • Is the client the hero (not the author)?
  • Is there genuine tension — something that made this hard?
  • Does the closing paragraph include the key metric?

Full case study:

  • Does the Results section lead with numbers?
  • Is the Challenge section specific enough that a similar prospect recognizes their situation?
  • Is the client quote (or placeholder) present?

Flag any failure: "The story version has no tension — add one obstacle or unexpected challenge before the 'What happened' paragraph."


Phase 5: Distribution Plan

FormatBest locationsWhen to use
Two-linerProposals, email bios, LinkedIn About sectionAny sales context
StoryLinkedIn post, podcast intros, sales call openerWeekly content
Full case studyWebsite portfolio page, PDF download, RFP responseLate-stage buyer research

Output Structure

## Case Study: [Client Descriptor] — [Date]

### Situation Summary
[2-sentence analysis from Phase 1]
Result tier: [1/2/3/4]

---

### Format 1: Two-Liner
[Final copy]

### Format 2: Story Version
[Final copy]

### Format 3: Full Case Study
[Full markdown]

---

### Self-Critique Notes
- Two-liner: [pass/issue]
- Story: [pass/issue]
- Full: [pass/issue]

### Distribution Plan
[Where to use each]

### Next Step
[If no quote collected → run testimonial-collector]
[If strong story → suggest LinkedIn post from story format]

Cross-reference: If a client quote was captured here, run testimonial-collector to properly format and score it for your testimonial library.


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

Gives 0 of the 12 instructions most learn study skills give in ~1.7k tokens

Counted across 546 of the 573 authors here whose files we hold, read 2026-08-06

  • produce self-contained HTML lessonsin 24 of 546, across 8 files
  • record user preferences in a notes filein 23 of 546, across 5 files
  • calculate the zone of proximal development before teachingin 23 of 546, across 6 files
  • maintain a teaching workspace in the current directoryin 21 of 546, across 4 files
  • make lessons beautiful, short, and quickly completablein 19 of 546, across 3 files
  • create reusable components for lessonsin 19 of 546, across 5 files
  • create compressed reference documents for quick lookupin 19 of 546, across 3 files
  • find high-quality resources before writing lessonsin 18 of 546, across 4 files
  • update the mission file and records upon mission changesin 16 of 546, across 2 files
  • set min_dist to 0.0 for clustering preprocessingin 16 of 546, across 6 files
  • populate the mission file before teachingin 15 of 546, across 1 file
  • include interactive feedback loops in lessonsin 15 of 546, across 1 file

Grouped from the skills themselves: near-identical wordings counted once, and counted by distinct author, so one author publishing three of these counts once. Length counted with cl100k_base; the agent that loads this file may tokenize it differently.

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