Awesome-omni-skill discover

Extracts semantic memory from project analysis. Scans codebase, docs, and configs to understand tech stack, constraints, and goals.

install

source · Clone the upstream repo

git clone https://github.com/diegosouzapw/awesome-omni-skill

Claude Code · Install into ~/.claude/skills/

T=$(mktemp -d) && git clone --depth=1 https://github.com/diegosouzapw/awesome-omni-skill "$T" && mkdir -p ~/.claude/skills && cp -r "$T/skills/development/discover" ~/.claude/skills/diegosouzapw-awesome-omni-skill-discover && rm -rf "$T"

manifest: skills/development/discover/SKILL.md

source content

Discover Skill (Stage 1)

This skill analyzes existing projects and generates Gastown-compatible semantic memory.

When to Use

Use this skill when:

Starting migration of an existing project
Need to understand a codebase's tech stack
Want to document project constraints and goals
Preparing for roadmap generation (Stage 2)

Output Structure

project/
├── .gt/
│   └── memory/
│       ├── semantic.json      # Permanent facts (tech stack, constraints)
│       ├── episodic.json      # Decisions with TTL (optional)
│       └── procedural.json    # Learned patterns (optional)
└── [existing project files]

Discovery Procedure

Scan these locations in priority order:

1. Package Files (Tech Stack Detection)

File	Detects
`package.json`	Node.js runtime, framework, dependencies
`Cargo.toml`	Rust projects
`go.mod`	Go projects
`requirements.txt`	Python dependencies
`pyproject.toml`	Python projects (modern)
`Gemfile`	Ruby projects
`pom.xml`	Java/Maven projects
`build.gradle`	Java/Gradle projects

2. Configuration Files (Service Detection)

File	Detects
`.firebaserc` , `firebase.json`	Firebase
`wrangler.toml`	Cloudflare Workers
`vercel.json`	Vercel deployment
`netlify.toml`	Netlify deployment
`docker-compose.yml`	Containerization
`Dockerfile`	Container build
`*.env.example`	Environment variables
`.github/workflows/`	CI/CD (GitHub Actions)

3. Documentation (Project Understanding)

File	Provides
`README.md`	Project description, setup
`docs/`	Architecture docs, ADRs, PRDs
`CONTRIBUTING.md`	Development workflow
`CHANGELOG.md`	Project history
`LICENSE`	License type

4. Source Structure (Codebase Understanding)

Directory	Indicates
`src/` , `lib/` , `app/`	Main code location
`tests/` , `__tests__/` , `spec/`	Test location
`schemas/` , `migrations/`	Database schemas
`components/`	UI component library
`api/` , `routes/`	API structure

Tech Stack Extraction

Framework Detection

Look for these patterns in dependencies:

Dependency	Framework
`next`	Next.js
`react`	React
`vue`	Vue.js
`@angular/core`	Angular
`express`	Express.js
`fastify`	Fastify
`django`	Django
`flask`	Flask
`fastapi`	FastAPI
`rails`	Ruby on Rails
`gin-gonic/gin`	Gin (Go)

Database Detection

Indicator	Database
`pg` , `postgres`	PostgreSQL
`mysql2`	MySQL
`mongodb` , `mongoose`	MongoDB
`redis`	Redis
`prisma`	Prisma ORM
`drizzle-orm`	Drizzle ORM
`typeorm`	TypeORM

Auth Detection

Indicator	Auth System
`firebase-admin`	Firebase Auth
`@auth0/`	Auth0
`next-auth`	NextAuth.js
`passport`	Passport.js
`clerk`	Clerk
`supabase`	Supabase Auth

Output: semantic.json

{
  "$schema": "semantic-memory-v1",
  "project": {
    "name": "my-app",
    "type": "web-application",
    "primary_language": "TypeScript",
    "description": "A task management app for teams"
  },
  "tech_stack": {
    "runtime": "Node.js 20",
    "framework": "Next.js 14",
    "database": "Neon PostgreSQL",
    "auth": "Firebase Auth",
    "deployment": "Vercel",
    "styling": "Tailwind CSS",
    "testing": "Vitest",
    "orm": "Drizzle"
  },
  "personas": [
    {"name": "Team Lead", "needs": ["assign tasks", "track progress"]},
    {"name": "Developer", "needs": ["see my tasks", "update status"]}
  ],
  "constraints": [
    "Must support offline mode",
    "GDPR compliant data handling"
  ],
  "non_goals": [
    "Mobile native app (web-only for MVP)",
    "Enterprise SSO (future phase)"
  ],
  "evidence": {
    "last_scan": "2026-01-27T10:00:00Z",
    "files_analyzed": ["package.json", "README.md", "docs/PRD.md"]
  }
}

Memory Types

Semantic Memory (Required)

Permanent facts that don't change:

Project name and type
Primary programming language
Tech stack components
Architectural constraints
Non-goals

Episodic Memory (Optional)

Decisions with time-to-live (~30 days):

Architecture decisions
Library choices with rationale
Trade-offs made

Procedural Memory (Optional)

Learned patterns:

Code conventions
Testing patterns
Deployment procedures

Quality Gates

Gate	Requirement
`semantic_valid`	semantic.json is valid JSON
`project_identified`	project.name is not null or empty
`tech_stack_detected`	At least 2 tech_stack fields populated
`evidence_recorded`	evidence.files_analyzed has 1+ entries

Validation

python plugins/lisa/hooks/validate.py --stage discover

Error Handling

If unable to detect something:

Set field to
```
null
```
rather than guessing
Add to
```
evidence.unresolved
```
list (if pattern exists)
Document what was searched and why it failed

Next Steps

After discover completes:

Proceed to Stage 2 (Plan) →
```
skills/plan/SKILL.md
```
Or proceed directly to Stage 3 (Structure) if roadmap exists