a5c.ai

Establish project governing principles including dev guidelines, code quality standards, testing policies, UX requirements, performance benchmarks, and security constraints.

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Convert technical plans into actionable development tasks with dependency graphs, effort estimates, and parallelization opportunities.

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Perform closed-book frontier reasoning — complex problem solving from internalized knowledge without external retrieval or tool use.

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Constitutional AI and safety guardrail prompts for aligned LLM behavior

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Content moderation API integration using OpenAI Moderation, Perspective API, and others

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Entity and fact extraction for user profiling and personalization

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Few-shot example generation and optimization for improved LLM performance

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LangFuse LLM observability integration for tracing, analytics, and cost tracking

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LangChain chain composition including SequentialChain, RouterChain, and LCEL patterns

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LangChain ReAct agent implementation with tool binding for reasoning and action loops

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Use when starting any conversation. Establishes how to find and use skills, requiring skill invocation before any response.

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Use when you have a spec or requirements for a multi-step task, before touching code. Creates bite-sized TDD implementation plans with dependency tracking.

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Use when creating new skills, editing existing skills, or verifying skills work before deployment.

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Use when about to claim work is complete, fixed, or passing, before committing or creating PRs. Evidence before assertions.

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Use when starting feature work that needs isolation from current workspace or before executing implementation plans.

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Use when completing tasks, implementing major features, or before merging to verify work meets requirements.

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LangGraph StateGraph builder with state schema design. Create stateful agent workflows with cycles, conditionals, and persistence.

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Use when receiving code review feedback, before implementing suggestions. Requires technical rigor and verification, not blind implementation.

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Use when facing 2+ independent tasks that can be worked on without shared state or sequential dependencies.

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Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes. Requires root cause investigation first.

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Use when executing implementation plans with independent tasks in the current session. Dispatches fresh subagent per task.

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Perform cross-artifact consistency and coverage analysis across constitution, specification, plan, and task artifacts to detect gaps, conflicts, and misalignments before implementation.

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Validate implementation quality through custom checklists, scoring against constitution standards, specification coverage, and producing remediation recommendations.

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LangChain retriever implementation with various retrieval strategies for RAG applications

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