ls-casestudy

name	ls-casestudy
description	Reverse-engineer any codebase, plugin, or repo into an evidence-first case study with file:line citations for every claim. Pattern extraction for future reference — not a bug hunt.

/casestudy — Evidence-First Architecture Case Study

Generate a detailed, evidence-backed case study of a codebase, plugin, repo, or system. Every claim must cite a file path and line number. This is pattern extraction for future reference — not a bug hunt, not a review.

Input

The user provides one or more of:

• A directory path to analyze
• A repo URL to clone and analyze
• A specific system/plugin/module to focus on
• An intent statement ("written as a pattern reference for X")

If no intent is stated, ask: "What are you building that this case study should inform?"

Phase 1: Evidence Collection

Dispatch 2-3 polymathic scouts (Haiku, read-only) in parallel to gather raw evidence:

Scout 1 — Structure Mapper:

Glob for all source files. Read build configs, manifests, plugin descriptors (.uplugin, package.json, pyproject.toml, Cargo.toml, etc.). Map the dependency graph. Report: file counts, module structure, entry points, dependency list. Every finding = file:line.

Scout 2 — API Surface Scanner:

Grep for public interfaces, exported functions, class declarations, route handlers, IPC handlers, event emitters. Report: public API surface with file:line for each entry point. Identify the core patterns (subsystem, singleton, event bus, async action, etc.).

Scout 3 — Flow Tracer (if the system has runtime behavior):

Trace the primary flow: startup → initialization → main loop → shutdown. Identify entry points, lifecycle hooks, configuration loading, error handling patterns. Report each step with file:line evidence.

Phase 2: Case Study Generation

Synthesize scout reports into a structured case study document. Follow this format exactly:

# Case Study: [System Name] Patterns for [Target Application]

Intent

- This is a case study of [system], written as a pattern reference for [future use].
- [Scope exclusions if any].

Scope and evidence sources

- [file:line] — [short note about what this line tells us]
- [file:line] — [short note]
- ... (every evidence source listed upfront)

Evidence glossary

- Each evidence line includes a short note about the referenced line and, when it starts a block, a brief description of what the following lines do.

---

## 1) [Category name] ([what it tells us])

- [Claim with plain language explanation]. Evidence: [file:line], [file:line]
- [Next claim]. Evidence: [file:line]

## 2) [Next category]

...

## N) [Target application] design notes (adaptation guidance)

These are recommendations based on the observed patterns above, not requirements.

- [Recommendation based on evidence]
- [Pattern takeaway for the target application]

Evidence Rules (HARD)

1. Every claim must have at least one file:line citation. No exceptions. If you can't cite it, don't claim it.
2. Evidence sources listed upfront. The reader should be able to verify every claim by reading the listed files.
3. File paths must be absolute. Use the actual on-disk path, not relative or shortened paths.
4. Line numbers must be accurate. Read the file and confirm the line number before citing.
5. Short notes per evidence line. One sentence max explaining what that line tells us.
6. No speculation. If behavior is unclear from the code, say "unclear from evidence" — don't guess.
7. Block descriptions. When an evidence line starts a block (function, class, config section), briefly describe what the following lines do.

Category Selection

Choose categories based on what the system actually does. Common categories:

Category	When to use
Plugin/module framing	Always — what is this thing?
UX entry points	If it has a UI surface
Core components	Always — the main classes/modules and their roles
Data flow / lifecycle	If it has runtime behavior
Configuration	If it has settings/config files
API surface	If it exposes APIs (REST, IPC, JS bridge, etc.)
Error handling	If it has a distinct error strategy
Credential/auth model	If it handles secrets or auth
Streaming/async patterns	If it does async work or streaming
Provider/adapter pattern	If it supports multiple backends
Persistence	If it stores data
Testing patterns	If it has tests worth studying

Don't force categories that don't apply. 4-8 sections is typical.

Phase 3: Adaptation Guidance

The final section maps observed patterns to the target application. These are recommendations, not requirements. Frame them as:

• "Pattern takeaway for [target]: [what to emulate and why]"
• Reference specific evidence that supports the recommendation

Output

Save the case study to one of:

• Docs/CaseStudies/[SystemName]_CaseStudy_[Target].md (if inside a project)
• <your-project-dir>/docs/case-studies/[slug].md (if saving to a specific project)
• User-specified path

Quality Checklist

Before delivering, verify:

• Every claim has file:line evidence
• Evidence sources section is complete (all cited files listed)
• No speculation — only code-verified claims
• Adaptation guidance references specific patterns from the study
• Intent statement is clear
• Categories match what the system actually does (no forced sections)