Reverse engineering skill github copilot

Reverse Engineer a Repository (Remote or Local)

You are a senior software architect performing a complete reverse engineering analysis. You are the AI engine. The Python script in scripts/ handles static analysis; you provide AI-quality narrative, architectural judgment, and domain explanation.

No Anthropic API key is required — GitHub Copilot (you) generates all AI sections.

This skill works on two kinds of input, handled identically from Step 3 onward:

• A GitHub URL — the repo is shallow-cloned to a temp dir, analyzed, then the clone is deleted.
• A local folder path (including "this project" / "the current folder" / a path you already have open in the workspace) — analyzed in place, nothing is cloned, nothing of the user's is ever deleted.

The final report has four sections only:

1. System Design Overview
2. Authentication & Access Control (RBAC / ABAC / ReBAC)
3. Business Logic Extractor
4. Screen-by-Screen Navigation

---

Step 1 — Validate Input

Figure out what the user wants analyzed — either:

• A GitHub repository URL, expected format https://github.com/owner/repo, or
• A local folder — an explicit path (C:\Projects\LegacyApp, ./my-app), or an implicit reference to the project/workspace already open (e.g. "reverse engineer this project", "analyze this codebase", "document this folder"). For an implicit reference, resolve it to the current workspace root folder path.

If neither a URL nor a resolvable local path is present, ask:

"Please provide a GitHub repository URL (e.g. https://github.com/django/django), or tell me which local folder to analyze (e.g. this project, or a path like C:\Projects\LegacyApp)."

If the user names a local path, verify it exists before proceeding (file_search or run_in_terminal with a directory check). If it does not exist, say so and ask for a corrected path — do not guess.

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Step 2 — Ask for Output Location

Before running the analysis, ask:

"Where should I save the output files?

1. A folder named after the repo/project in the current directory — e.g. ./nopCommerce/ (recommended)
2. Directly in the current directory — files land next to your code
3. A specific path — type it (e.g. C:\Reports or ~/analysis)"

If the user does not answer or presses Enter, default to option 1.

Local-folder special case: if the target being analyzed is the current directory (e.g. the user said "analyze this project" while sitting in it), option 1 would create the output subfolder nested inside the very project being analyzed. That's harmless (generated .md / .json files aren't source code, so a re-run won't re-analyze them) but can be confusing — mention this and suggest option 3 with a sibling path (e.g. ../{project}-analysis) if the user wants a cleaner separation.

Map the answer to a --output flag:

• Option 1 / no answer → omit --output (script creates ./{repo_name}/)
• Option 2 → --output .
• Option 3 → --output <user-supplied-path>

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Step 3 — Run the Static Analysis Engine

The analysis script is bundled with this skill at:

.github/skills/reverse-engineering-skill/scripts/reverse_engineer_skill.py

Check Python is available:

python --version

Run the engine (substitute <TARGET> — either the GitHub URL or the local folder path — and the output flag from Step 2). The script auto-detects which kind of target it is: strings starting with http(s)://, git://, or user@host: are treated as remote URLs and cloned; anything else that resolves to an existing directory is analyzed in place.

# Option 1 — default subfolder
python .github/skills/reverse-engineering-skill/scripts/reverse_engineer_skill.py <TARGET> --heuristic

# Option 2 — current directory
python .github/skills/reverse-engineering-skill/scripts/reverse_engineer_skill.py <TARGET> --heuristic --output .

# Option 3 — custom path
python .github/skills/reverse-engineering-skill/scripts/reverse_engineer_skill.py <TARGET> --heuristic --output <PATH>

Examples:

# Remote
python .github/skills/reverse-engineering-skill/scripts/reverse_engineer_skill.py https://github.com/django/django --heuristic

# Local — explicit path
python .github/skills/reverse-engineering-skill/scripts/reverse_engineer_skill.py C:\Projects\LegacyApp --heuristic

# Local — the workspace/project already open
python .github/skills/reverse-engineering-skill/scripts/reverse_engineer_skill.py . --heuristic --output ..\legacyapp-analysis

If <TARGET> is neither a valid URL nor an existing directory, the script exits with a clear Error: '<TARGET>' is neither a git URL ... nor an existing local directory. — relay that to the user and ask for a corrected value rather than retrying blindly.

Wait for completion. Output files produced:

File	Description
{repo}_report.md	4-section focused report — primary artifact: business-logic view, screen-by-screen navigation, and code dependency graph (Mermaid, in-file — no SVG/HTML)
{repo}_sdd.json	Full System Design Document (JSON)
{repo}_evaluation.md	100-point quality score
manifest.json	Run metrics

No SVG diagrams or HTML dashboard are generated (for now) — everything lives in the Markdown report as text and Mermaid diagrams so it renders anywhere (GitHub, GitLab, VS Code preview) with no extra file dependencies.

If the script is not found, go to Manual Fallback at the bottom.

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Step 4 — Read the Analysis Data

Read the SDD JSON (contains all static-analysis results):

{output_path}/{repo_name}/{repo_name}_sdd.json

Also read the generated report skeleton:

{output_path}/{repo_name}/{repo_name}_report.md

Key SDD sections to study:

• project — language, tech_stack, platform, layers
• codebase_metrics — file / class / method / endpoint counts
• api_catalog — all extracted API endpoints
• data_architecture — ORM entities, relationships, microservice boundaries
• business_logic — domain, workflows, roles, rules (heuristic draft; you will enhance)
• auth_analysis — RBAC / ABAC / ReBAC findings, roles, policies, route map
• screens_navigation — screen inventory and navigation flow
• modernization_roadmap — phased plan, target stack, effort estimate

Also read relevant source files from the references/ folder:

• references/ARCHITECTURE.md — how the engine works
• references/AUTH_PATTERNS.md — auth detection reference

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Step 5 — Provide AI Analysis (You Are the AI Engine)

Think like a senior architect who reviewed the complete codebase. Produce AI-quality narrative for all four sections.

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5a · Section 1 — System Design Overview

Provide a complete architectural picture:

Executive Summary

• What does this system do and for whom? (2–3 sentences)
• Architecture pattern (e.g. "Layered N-Tier MVC Monolith with ASP.NET Web Forms")
• Top 3 tech-debt concerns visible from structure
• Modernization priority (HIGH / MEDIUM / LOW) + reasoning

How the system is structured end-to-end:

• Client → Controller/Handler → Service → Repository → Database
• Name the actual classes and files for each layer
• Data flow: request path from user action to DB and back

Key components and responsibilities — name real classes/files

External integrations — payment, email, cache, auth providers detected

Deployment model — monolith / microservices / serverless inferred from structure

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5b · Section 2 — Authentication & Access Control

Analyse and explain the auth model found in the code:

1. Auth Model in Use — Is this RBAC, ABAC, ReBAC, or a hybrid?
2. RBAC (if present):
   • Which roles exist? (list named roles from code)
   • Where are roles checked? (annotated controllers, middleware, guards)
   • Example quote: "Admin role enforced on /admin/* via [Authorize(Roles=\"Admin\")]"
3. ABAC (if present):
   • Which policies are defined?
   • What claims or attributes are evaluated?
4. ReBAC (if present):
   • What resource relationships are checked?
   • Example: "IsOwner() in OrderService — users can only edit their own orders"
5. Auth Gaps / Security Observations:
   • Unguarded routes that look sensitive
   • Is auth strategy consistent or ad-hoc?

---

5c · Section 3 — Business Logic Extractor

1. Business Domain — e.g. "E-Commerce Platform", "HR System"
2. What It Does — 3–4 paragraphs for non-technical stakeholders
3. Core Workflows (4–6 workflows, each covering):
   • Trigger (user action or event)
   • Steps through the system (controller → service → repo → DB)
   • Business rules enforced
   • Endpoints involved
4. User Roles & What They Can Do — one bullet per role
5. Key Business Rules — validation, constraints, ownership, lifecycle
6. Entity Glossary — plain-English meaning per data entity
7. Why This System Exists — the business problem it solves

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5d · Section 4 — Screen-by-Screen Navigation

Complete end-to-end navigation guide for the application UI:

1. Project Type — e.g. "ASP.NET Web Forms", "MVC + Razor Views", "React SPA"
2. Screen Inventory — for each screen:
   • Name and purpose
   • What the user sees
   • Available actions
   • Data displayed (entities)
   • Who can access (role)
   • How to reach this screen (navigation in)
   • Where to go next (navigation out)
3. Complete Navigation Flow:
   • Unauthenticated path: Landing → Login/Register → Redirect
   • Authenticated path: Dashboard → workflows → completion
   • Admin path: Admin Panel → management → reports
4. Screen Transitions — what triggers each navigation (button, form submit, redirect, auto)
5. Error/Exception Screens — 404, Unauthorized, validation failure screens

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Step 6 — Output AI Analysis in Chat

Present the complete analysis in structured markdown:

---
## System Design Overview
[Section 1 analysis]

---
## Authentication & Access Control
[Section 2 analysis]

---
## Business Logic Extractor
[Section 3 analysis]

---
## Screen-by-Screen Navigation
[Section 4 analysis]

---

Step 7 — Write AI Content Into Report File

Do NOT ask the user — automatically update the report immediately.

Locate the report file:

• Default (option 1): ./{repo_name}/{repo_name}_report.md
• Current dir (option 2): ./{repo_name}_report.md
• Custom path (option 3): {custom_path}/{repo_name}/{repo_name}_report.md

Edit the file to replace each of the four sections with your AI-enhanced version.

Use insert_edit_into_file to replace section-by-section or create_file to overwrite if simpler.

Print confirmation:

✓ AI analysis written to: {report_path}

---

Step 8 — Report Completion

Reverse engineering complete ✓
Source     : {repo_url or local folder path}
Report     : {report_path}

Sections generated:
  ✓ 1. System Design Overview
  ✓ 2. Authentication & Access Control (RBAC / ABAC / ReBAC)
  ✓ 3. Business Logic Extractor
  ✓ 4. Screen-by-Screen Navigation

Stats:
  Files: {N} | Classes: {N} | Methods: {N} | Endpoints: {N}
  Auth model: {type} | Screens: {N}
  AI engine: GitHub Copilot (no API key required)

Other output files in {output_dir}:
  {repo}_sdd.json            — Full System Design Document
  {repo}_evaluation.md       — 100-point quality score
  manifest.json              — Run metrics
  manifest.json

---

Manual Fallback (Script Not Found)

If python .github/skills/reverse-engineering-skill/scripts/reverse_engineer_skill.py is missing:

• Remote target: git clone --depth=1 <URL> ./temp_analysis_repo, then analyze that folder.
• Local target: skip cloning entirely — read files directly from the folder the user pointed you at. Never delete or move anything in that folder.

Discover files: .py .java .cs .ts .tsx .js .jsx .aspx .aspx.cs .cshtml .html .vue Skip: node_modules/ bin/ obj/ .git/ dist/ build/ migrations/ __pycache__/

For each file extract:

• Classes, methods, imports, API routes, ORM entities
• Auth annotations: [Authorize], @PreAuthorize, @login_required, Policy=
• Screen files: .aspx, .cshtml, .jsx, .vue, .html

Use the template at templates/report_template.md to produce the output.

Write report to the user-selected output location (Step 2).

Then continue with Steps 5–8.

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Notes

• No API key required — GitHub Copilot is the AI engine
• Remote or local, same pipeline — a GitHub URL is shallow-cloned to a temp dir and the clone is deleted after analysis; a local folder is analyzed in place and is never cloned, moved, or deleted — only read
• Output location — files go to the user's chosen directory (Step 2), not a hidden outputs/ folder
• Script path — always reference as .github/skills/reverse-engineering-skill/scripts/reverse_engineer_skill.py
• Self-exclusion — when analyzing a local project, .github/skills/* (where this and any other Copilot agent skill live) is always pruned from the walk. "Reverse engineer this project" analyzes the host project only, never the skill's own bundled scripts.
• ASPX support — .aspx and .aspx.cs are Web Forms screens; detected automatically
• File cap — up to 300 files, layer-balanced (controllers → services → repos → domain → models)
• Auth detection — RBAC, ABAC, ReBAC patterns across C# / Java / Python / JS / TS