Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/tpavanipradeep/everything-claude-code/backend-patternsnpx skills add tpavanipradeep/everything-claude-code --skill backend-patternsgit clone --depth 1 https://github.com/tpavanipradeep/everything-claude-codeWhat it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00031 | $0.03331 |
| Opus 5 | $0.00015 | $0.01665 |
| Sonnet 5 | $0.00006 | $0.00666 |
| Haiku 4.5 | $0.00003 | $0.00333 |
Grade A, and why
backend-patterns scanned grade A with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
const requests = this.requests.get(identifier) || [] This is a copy
94% identical to backend-patterns — 15 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 599 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Backend Development Patterns
Backend architecture patterns and best practices for scalable server-side applications.
When to Activate
- Designing REST or GraphQL API endpoints
- Implementing repository, service, or controller layers
- Optimizing database queries (N+1, indexing, connection pooling)
- Adding caching (Redis, in-memory, HTTP cache headers)
- Setting up background jobs or async processing
- Structuring error handling and validation for APIs
- Building middleware (auth, logging, rate limiting)
API Design Patterns
RESTful API Structure
// ✅ Resource-based URLs
GET /api/markets # List resources
GET /api/markets/:id # Get single resource
POST /api/markets # Create resource
PUT /api/markets/:id # Replace resource
PATCH /api/markets/:id # Update resource
DELETE /api/markets/:id # Delete resource
// ✅ Query parameters for filtering, sorting, pagination
GET /api/markets?status=active&sort=volume&limit=20&offset=0
Repository Pattern
// Abstract data access logic
interface MarketRepository {
findAll(filters?: MarketFilters): Promise<Market[]>
findById(id: string): Promise<Market | null>
create(data: CreateMarketDto): Promise<Market>
update(id: string, data: UpdateMarketDto): Promise<Market>
delete(id: string): Promise<void>
}
class SupabaseMarketRepository implements MarketRepository {
async findAll(filters?: MarketFilters): Promise<Market[]> {
let query = supabase.from('markets').select('*')
if (filters?.status) {
query = query.eq('status', filters.status)
}
if (filters?.limit) {
query = query.limit(filters.limit)
}
const { data, error } = await query
if (error) throw new Error(error.message)
return data
}
// Other methods...
}
Service Layer Pattern
// Business logic separated from data access
class MarketService {
constructor(private marketRepo: MarketRepository) {}
async searchMarkets(query: string, limit: number = 10): Promise<Market[]> {
// Business logic
const embedding = await generateEmbedding(query)
const results = await this.vectorSearch(embedding, limit)
// Fetch full data
const markets = await this.marketRepo.findByIds(results.map(r => r.id))
// Sort by similarity
return markets.sort((a, b) => {
const scoreA = results.find(r => r.id === a.id)?.score || 0
const scoreB = results.find(r => r.id === b.id)?.score || 0
return scoreA - scoreB
})
}
private async vectorSearch(embedding: number[], limit: number) {
// Vector search implementation
}
}
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 2d ago First seen · 599 lines · 31 tokens per session scan A 681c33b4e125
backend-patterns is a skill published in the GitHub repository tpavanipradeep/everything-claude-code (104 stars, last pushed 5mo ago), licensed MIT. It adds 31 tokens to every session and 3,331 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). It is 94% identical to backend-patterns, differing in 15 lines, and is treated as a copy.
Other skills, from other repositories
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
brainstorming
You MUST use this before any creative work - creating features, building components, adding functionality, or modifying behavior. Explores user intent, requirements and design before implementation.
auto-perf-optimize
Run agent-driven VS Code performance or memory investigations. Use when asked to launch Code OSS, automate a VS Code scenario, run the Chat memory smoke runner, capture renderer heap snapshots, take workflow screenshots, compare run summaries, or drive a repeatable scenario before heap-snapshot analysis.
chat-perf
Run chat perf benchmarks and memory leak checks against the local dev build or any published VS Code version. Use when investigating chat rendering regressions, validating perf-sensitive changes to chat UI, or checking for memory leaks in the chat response pipeline.
chat-pet-sprite-creation
Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.
cpu-profile-analysis
Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…