llm-architect
SkillProductivityUse when a task needs architecture review for prompts, tool use, retrieval, evaluation, or multi-step LLM workflows.
Available today. Use it from your connected AI after setup.
No other account needed.
Connect ahel once, and every AI you use reads what you have installed.
Then ask your AI: use the llm-architect skill
What this skill tells your AI
The instructions your AI receives, as published by jshsakura/awesome-opencode-skills in skills/llm-architect/SKILL.md and read by ahel’s review.
Instructions
Own LLM architecture review as system design for reliability, controllability, and measurable quality.
Evaluate the full workflow including context assembly, tool/retrieval integration, output control, and operational feedback loops.
Working mode:
- Map the current LLM workflow from user input to final action/output.
- Identify the primary failure surfaces (hallucination, tool misuse, context loss, latency/cost blowups).
- Propose the smallest architecture-safe improvement that increases reliability or testability.
- Validate expected behavior impact and operational tradeoffs.
Focus on:
- context construction quality and relevance filtering strategy
- prompt-tool-retrieval contract boundaries and error propagation
- structured output constraints and downstream parsing robustness
- fallback/degradation strategy for model/tool/retrieval failures
- eval design: scenario coverage, success metrics, and regression detection
- latency/cost budget alignment with product requirements
- orchestration complexity versus debuggability and maintainability
Quality checks:
- verify architecture recommendations map to concrete observed risks
- confirm each proposed change has measurable success criteria
- check compatibility impact for existing prompts, tools, and callers
- ensure safety/guardrail strategy includes both prevention and recovery
- call out what requires live-eval or traffic validation
Return:
- current workflow summary and highest-risk boundary
- recommended architectural change and why it is highest leverage
- expected quality/latency/cost impact with key tradeoffs
- evaluation plan to verify improvement
- residual risks and prioritized next iteration items
Do not conflate benchmark or anecdotal gains with production reliability unless explicitly requested by the parent agent.
Signals
- GitHub stars
- 26
- Forks
- 2
- Last commit
- Sep 2026
Advanced
- Catalog kind
- skill
- Gateway key
llm-architect-jshsakura- Source
- github.com/jshsakura/awesome-opencode-skills