What kind of data do I need to provide for the engine to work?

The skill requires implementation facts such as deployment logs, project files, or CRM exports. It uses these to reconstruct performance baselines and calculate quantified ROI, ensuring your case studies are based on hard data rather than memory.

Which AI agents or development environments is this skill compatible with?

This skill is designed for filesystem-based AI environments like Claude Code or Cursor. It includes a built-in consent gate to securely interface with tools like GitHub, Jira, and HubSpot when permissions are granted.

How does this differ from just asking a standard LLM to write a case study?

Unlike generic prompts that often hallucinate metrics, this skill enforces a 'credibility layer' that strictly separates verified facts from assumptions, preventing the overly optimistic language that often triggers buyer skepticism.

Can I use this for clients with strict NDAs or sensitive data?

The skill includes an anonymization guidance module and generates NDA-safe testimonial variations. It helps you draft evidence-led assets that respect client privacy while still proving the technical impact of your work.

What exactly is included in the purchase?

You receive the core synthesis engine, the ROI modeling framework, and a suite of templates for technical case studies and permission checklists. It also includes the 'credibility layer' logic used to verify implementation facts.

Does the skill automatically send testimonial requests to my clients?

No, the skill generates the content and the permission plan, but it does not perform automated outreach. You maintain full control over when and how you contact your clients for testimonials or approvals.

deployment to proof case study engine

Example session with this skill installed

Use @deployment-to-proof-case-study-engine on these migration logs. We moved the client from a legacy ERP to a cloud-native API. Calculate the throughput change and draft a case study.

Read your context and instructions
Compiled the deployment to proof

I have analyzed the migration logs and implementation notes for the ERP-to-API transition. The analysis focuses on throughput improvements and latency reduction.

Project Proof Asset Pack

Baseline vs. Post-Deployment Performance

Metric	Baseline (Legacy ERP)	Post-Deployment (Cloud API)	Delta	Verification
Request Latency	1,250ms (avg)	185ms (avg)	-85.2%	Verified via Logs
Peak Throughput	45 req/sec	650 req/sec	+1,344%	Load Test Data
Sync Error Rate	4.2%	0.08%	-98.1%	Error Logs

ROI Model (Annualized)

Direct Infrastructure Savings: $14,200 (Reduction in on-prem server maintenance).
Productivity Gains: 420 engineering hours/year (Reduction in manual sync troubleshooting).
Capacity Impact: The system now supports 14x higher transaction volume without additional headcount.
Assumption: Engineering hour costed at $85/hr; 99.9% API uptime sustained.

Case Study Draft: High-Throughput API Migration

Headline: Scalable Cloud API achieves 1,300% throughput increase for legacy ERP workflow.
Problem: The client's legacy ERP created a bottleneck, capping transaction volume and causing 4%+ data sync failures during peak hours.
Intervention: Replaced monolithic sync scripts with a cloud-native, event-driven API layer using managed message queues.
Outcome: Reduced latency by 85% and virtually eliminated sync errors, allowing the client to scale without infrastructure upgrades.

Testimonial Request (NDA-Safe)

"We've documented an 85% improvement in system response time following the API migration. Would you be open to a brief quote regarding how this latency reduction has impacted your team's daily operations? We are happy to keep the company name anonymous and refer to you as 'Leading Logistics Provider' if preferred."

Next steps

Verify the $85/hr cost assumption in the ROI model.
Select whether to use the branded or anonymous version of the case study.
Send the testimonial request to the Lead Engineer for approval.

Connects securely to your tools. The creator never sees your data.

About this skill

Vague marketing claims and unverified "success stories" kill sales credibility. This skill solves the post-delivery documentation gap by converting raw implementation data, logs, and exports into high-fidelity ROI models and case studies. It stops you from relying on memory or generic testimonials after a project ends.

What it does

Reconstructs Baselines even if you forgot to capture performance data before the build started.
Models Quantified ROI using conservative formulas to separate direct savings from soft capacity gains.
Drafts Evidence-Led Case Studies focused on the technical intervention and measured outcomes rather than fluff.
Generates Testimonial Requests including NDA-safe variations and permission checklists for sensitive clients.
Manages Data Privacy by providing specific anonymization guidance for identity-sensitive deployments.

How it works

Intake & Source Mapping: You provide implementation facts via files, logs, or CRM exports.
Analysis & Attribution: The skill identifies what was verified versus inferred and calculates impact metrics.
Drafting: It generates a baseline table, ROI model, and a conversion-ready case study draft.
Permission Planning: It builds a request for the client and a plan for asset reuse.

Frameworks & tools

This skill is industry-agnostic and functions in filesystem-based environments like Claude Code or Cursor. It includes a strict connector consent gate for tools like HubSpot, GitHub, and Jira.

Why this beats prompting it yourself

Generic prompts often hallucinate metrics or use overly optimistic language that triggers buyer skepticism. This skill enforces a "credibility layer" that explicitly separates observed facts from assumptions, ensuring your proof assets stand up to scrutiny.

Use cases

Converting a completed GitHub repo and deployment log into a technical case study.
Calculating the specific time-savings and error-reduction ROI for a custom automation.
Drafting a testimonial request for a client who is happy but has strict NDA constraints.
Reconstructing a "before" state from old tickets to prove the value of a recent migration.

Known limitations

Does not perform automated outreach. Requires manual verification of financial assumptions. Cannot bypass NDAs or legal restrictions on data sharing.

How to use

Skills on Agensi run inside your AI agent. Copy the prompt, paste it into your agent, and it connects to Agensi and installs this skill for you. If a payment is needed, your agent shows it to you first.

Reviews

No reviews yet

Be one of the first to try it. Every listed skill passes our trust checks below.

Security scanned

Passed our 8-point scan before listing

Fresh listing

Recently published to Agensi

Money-back guarantee

Doesn't do what its page promises? You get your money back.

deployment to proof case study engine