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Kailash cheatsheets — patterns, nodes, workflows, cycles, perf, security, saga.
Installer avec Codex ou Claude Copiez ce prompt, collez-le dans Codex, Claude ou un autre assistant, puis laissez-le vérifier la page du skill et l'installer pour vous.
Basé sur la classification professionnelle SOC
Claude Code architecture — artifact design, context, agentic patterns. For CC audit/build.
Conformance Walk — freeze-then-judge on the source→delivered→live axis: one cw_core + Source/Delivered/Live adapter families, coverage vs pass-rate, discrete verdicts. Use for any testable surface.
Kailash security (Python) — validation, secrets, injection, authn/z. Hardcoded secrets BLOCKED.
/onboard procedure: read roster + team-memory + posture + claims + codify lease + rules-changed for a new operator joining a multi-operator COC repo.
/certify procedure: brief → probe → gate at 100%; loops failed questions until pass. NO Claude-assistance during gate phase. Curated bank, not LLM-generated.
/ecosystem-init procedure — write the D6 ecosystem-config, run the disclosure scan before write, establish genesis via runEnrollmentCeremony, scaffold non-Kailash STACK.md.
| name | cheatsheets |
| description | Kailash cheatsheets — patterns, nodes, workflows, cycles, perf, security, saga. |
Comprehensive collection of quick reference guides, common patterns, and best practices for Kailash SDK development.
Use when asking about quick tips, cheat sheet, quick reference, common mistakes, node selection, workflow patterns library, cycle patterns, production patterns, performance optimization, monitoring, security config, multi-tenancy, distributed transactions, saga pattern, custom nodes, PythonCode data science, ollama integration, directoryreader patterns, or environment variables.
This skill provides quick access to:
from kailash.workflow.builder import WorkflowBuilder
from kailash.runtime import LocalRuntime
workflow = WorkflowBuilder()
workflow.add_node("NodeType", "node_id", {"param": "value"})
runtime = LocalRuntime()
results, run_id = runtime.execute(workflow.build())
# Data processing
workflow.add_node("PythonCode", "transform", {"code": "..."})
# API calls
workflow.add_node("HTTPRequest", "api", {"url": "...", "method": "GET"})
# AI/LLM
workflow.add_node("LLMNode", "chat", {"model": os.environ["LLM_MODEL"], "prompt": "..."})
workflow.add_node("LoopNode", "loop", {"max_iterations": 5})
workflow.add_node("ProcessNode", "process", {})
workflow.add_connection("loop", "item", "process", "input")
workflow.add_connection("process", "output", "loop", "feedback")
| Rule | Why |
|---|---|
| ❌ NEVER use raw SQL | Use DataFlow instead |
✅ ALWAYS call .build() | Before runtime.execute() |
| ❌ NEVER use relative imports | Use absolute imports |
| ❌ NEVER mock in Tier 2-3 | Use real infrastructure |
| ❌ NEVER train sklearn/torch directly | Use km.train(...) (skill 34-kailash-ml) |
| ✅ Cap parallel worktrees at 3 concurrent Opus agents | Beyond 3, budget exhaustion & merge conflicts dominate |
Use this skill when you need:
For cheatsheet-related questions, invoke:
pattern-expert - Pattern selection and usagedecide-framework skill - Choose appropriate patterns for your use case