Every skill here slid through 5 layers of ice. Real code, real metrics, real penguins.
Built by penguins who read the docs.
Complete skills. No truncation. Genuinely useful.
Branded types pattern — prevent mixing structurally identical types at compile time.
type Brand<T, TBrand> = T & { __brand: TBrand };
type UserId = Brand<number, "UserId">;
type OrderId = Brand<number, "OrderId">;
function createUserId(id: number): UserId {
return id as UserId;
}
const userId = createUserId(123);
const orderId = createOrderId(456);
getUser(userId); // OK
getUser(orderId); // Error — type safety!
Pydantic v2 migration — 4-17x performance gains with Rust-based validation.
from pydantic import BaseModel, ConfigDict
from pydantic import field_validator
from typing import Annotated
from pydantic import Field
class UserCreate(BaseModel):
model_config = ConfigDict(
from_attributes=True, # was: orm_mode
populate_by_name=True, # was: allow_population_by_field_name
)
name: str
age: Annotated[int, Field(ge=0, le=150)]
@field_validator("name") # was: @validator
@classmethod
def validate_name(cls, v: str) -> str:
return v.strip()
Production-ready multi-stage Dockerfile — non-root, health checks, optimized layers.
# Stage 1: builder
FROM python:3.12-slim-bookworm AS builder
WORKDIR /app
RUN apt-get update && apt-get install -y \
--no-install-recommends build-essential \
libpq-dev && rm -rf /var/lib/apt/lists/*
COPY requirements.txt .
RUN pip install --no-cache-dir --user \
-r requirements.txt
# Stage 2: runtime (clean, minimal)
FROM python:3.12-slim-bookworm
WORKDIR /app
RUN groupadd -r appuser && \
useradd -r -g appuser appuser
COPY --from=builder /root/.local \
/home/appuser/.local
COPY . .
USER appuser
HEALTHCHECK --interval=30s --timeout=10s \
CMD curl -f http://localhost:8000/health
CMD ["uvicorn", "main:app", "--host", "0.0.0.0"]
useActionState + Server Components — form handling with automatic pending states.
import { useActionState } from "react";
function SignupForm() {
const [state, submitAction, isPending] =
useActionState(
async (prev, formData) => {
const email = formData.get("email");
const res = await createAccount(email);
if (!res.ok) return { error: res.message };
return { success: true };
},
{ success: false }
);
return (
<form action={submitAction}>
<input name="email" type="email" />
<button disabled={isPending}>
{isPending ? "Creating..." : "Sign Up"}
</button>
{state.error && <p>{state.error}</p>}
</form>
);
}
EUREKA-validated and SOTA-promoted. Partially revealed.
ASCII Decision Tree — strategic & tactical patterns for bounded contexts.
DDD DECISION TREE 1. Domain Analysis |- Identify core domain |- Define subdomains (core, supporting, generic) '- Map business capabilities 2. Strategic Design (Problem Space) |- Define bounded contexts |- Develop ubiquitous language |- Create context maps '- Map team organization 3. Tactical Design (Solution Space) |- Model aggregates & roots |- Implement entities & value objects |- Define domain events '- Design repositories & services 4. Integration |- Choose context mapping pattern |- Implement ACL (anti-corruption layer) |- Event-driven communication ...
BM25 + Dense + RRF fusion — +18.5% MRR, validated across 30+ sources.
HYBRID SEARCH ARCHITECTURE
==========================
Query -> [BM25 Sparse] ----+
-> [Dense Vector] ---+--> RRF Fusion (k=60)
| |
v v
Jina Reranker v3
(61.94 nDCG@10)
|
v
Quality-filtered results
KEY FINDINGS:
- BM25+Dense+RRF = +18.5% MRR vs single method
- RRF k=60 optimal (Azure AI, OpenSearch, Pinecone)
- Jina Reranker v3 (0.6B) outperforms 1.5B models
- Native support: Weaviate, Elastic, Azure AI ...
mcp-scan deployment — 4 attack vectors covered, tool pinning, real-time proxy.
MCP SECURITY CHECKLIST
======================
ATTACK VECTORS:
[x] Tool Poisoning (hidden malicious instructions)
[x] Tool Shadowing (cross-server hijacking)
[x] Rug Pull Attacks (silent schema modification)
[x] Server Spoofing
MODES:
1. SCAN - Static analysis: prompt injection,
tool poisoning, malware, PII
2. PROXY - Real-time monitoring between
host and MCP server
3. TOOL PINNING - Hash-based rug pull prevention
5.5% of public MCP servers vulnerable.
Defense-in-depth: 5-layer recommendation ...
Skills+CLI vs MCP direct. Eliminates JSON-RPC overhead, capability negotiation, and tool description bloat. Measured in production.
S-001 · Token OptimizationHierarchical planner-worker + consensus. Uncoordinated agents amplify errors 17x; this architecture reduces it to 4.4x. 40+ sources confirm.
S-010 · Multi-Agent CoordinationSkills optimized for Claude Code CLI
3 skills: antifragile_protocol, formal_logic_verification, context_manager
Skills for professional Python development
4 skills: pragmatic_engineering, context_manager, memory_lifecycle, docker-containerization
Skills for modern full-stack development
5 skills: pragmatic_engineering, react, postgresql, docker-containerization, prisma_v7
Skills for architecture and system design
4 skills: sovereign_grand_strategy, antifragile_protocol, domain_driven_design, microservices_architecture
Skills for monitoring and observability
4 skills: health_check, context_manager, memory_lifecycle, semantic_topology
Skills optimized for Qwen Code CLI
3 skills: qwen_all, qwen_coder_delta, qwen_code_cli_delta
{
"mcpServers": {
"midos": {
"url": "https://mcp.midos.dev/mcp",
"env": {
"MIDOS_API_KEY": "your_key_here"
}
}
}
}
Works with Claude Code, Cursor, VS Code, Windsurf, and any MCP client.