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case study example library builder
Transform raw project notes and interviews into a library of anonymized, high-integrity case studies.
$5
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case study example library builder
Example session with this skill installed
Here are my notes from the Q3 logistics software implementation for a mid-sized retailer. Turn these into a library of safe, varied case studies for our sales deck and internal training.
- Read your context and instructions
- Compiled the case study example
I have processed the Q3 logistics implementation notes. I extracted the core pattern: a conflict between legacy warehouse workflows and automated inventory tagging that was resolved by a tiered rollout.
Logistics Implementation Case Library
| Case ID | Context | Problem Type | Learning Point | Best Use |
|---|---|---|---|---|
| L-01 | Global Pharma | Process Friction | Incremental rollout prevents floor-staff revolt. | Sales Deck |
| L-02 | Boutique E-commerce | Resource Gap | Automation requires data cleanliness before speed. | Training |
| L-03 | Heavy Manufacturing | Cultural Resistance | Top-down mandates fail without bottom-up feedback. | Workshop |
Case L-01: The Phased Transition (Global Pharma)
Situation: A global pharmaceutical distributor (2,000+ employees) attempting to modernize 15-year-old inventory systems within a strict 90-day compliance window.
Actors: COO (Efficiency focus), Warehouse Manager (Risk-averse), IT Lead (Protocol-driven).
The Problem: The new automated tagging system conflicted with the legacy "manual check" habit, causing a 20% drop in throughput during week one.
Intervention: Halted full rollout. Implemented a "Shadow Week" where one line used the new system while others remained manual, creating a peer-led training loop.
Outcome Logic: ✅ Verified 15% increase in picking accuracy by week four. 🎨 Illustrated: The Warehouse Manager became the primary internal advocate.
Validation:
- Confidentiality: Passed. No specific company or location markers retained.
- Plausibility: Passed. Causal link between shadow-testing and user adoption is standard.
- Claim Integrity: High. Outcome is tied to specific throughput metrics.
Next steps
- Select the "Global Pharma" variant for the enterprise sales deck.
- Use the "Boutique E-commerce" variant for the junior consultant onboarding module.
- Review the Evidence vs Illustration tags to ensure compliance with the original project data.
Connects securely to your tools. The creator never sees your data.
About this skill
The problem
Raw project retrospectives, interview notes, and sales data often contain sensitive information or messy details that make them unusable for training or marketing. Manually anonymizing and reframing these into pedagogical case studies is time-consuming and risks losing the core logic that makes the case valuable.
What it does
- Extracts the underlying causal chain, actors, and outcome logic from raw source material.
- Generates 3-5 distinct, anonymized variations across different industries and scales to maximize utility.
- Explicitly tags content as observed evidence or narrative illustration to maintain integrity.
- Validates every case against confidentiality, plausibility, and differentiation checks.
- Maps cases to specific applications like sales enablement, workshops, or content marketing.
Why this beats prompting it yourself
Basic prompting often leads to generic, "perfect" stories that lack realistic friction or leak identifiable details. This skill enforces a disciplined extraction process that preserves the transferable principle while systematically varying the context to build a broader, safer library than a simple rewrite could achieve.
Use cases
- Transforming internal project retros into client-facing success stories.
- Converting expert interviews into anonymized training modules for new hires.
- Creating varied role-play scenarios for sales team workshops.
- Generating a library of "failure mode" examples to illustrate common industry pitfalls.
Known limitations
Requires specific source material like transcripts or retrospectives to function effectively. It cannot generate high-fidelity cases from extremely vague one-sentence descriptions.
Requires raw source data like transcripts, notes, or retrospectives. Will not generate high-fidelity results from vague, single-sentence prompts.
How to use
- 1
Connect your agent to the Agensi MCP
Copy the prompt and paste it into Claude, ChatGPT, Cursor or any agent that supports MCP. It sets up the connection for you. Manual setup
- 2
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- 3
Ask for what you need
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