avav25 Prompt Engineering
Comprehensive prompt engineering knowledge base. Provides actionable patterns, checklists, and guides for designing, securing, evaluating, and optimizing LLM prompts and agent systems.
When to Use
- Designing or reviewing prompt templates (system, developer, user prompts)
- Building tool calling schemas and structured output contracts
- Evaluating prompt quality — accuracy, safety, cost, latency
- Auditing LLM security against OWASP LLM Top 10
- Designing multi-agent orchestration and handoff protocols
- Optimizing prompt cost and latency
- Creating or reviewing Claude Code AI assets (rules, workflows, skills — all are prompts)
- Authoring or auditing a plugin skill — frontmatter spec, body length, progressive disclosure, scripts, eval (see
skill-authoring-spec.md) or a mis-triggering / fuzzy description (seeoptimizing-descriptions.md) - Setting up prompt versioning and observability
When NOT to Use
- Implementing backend/frontend code (use
Agent(software-engineer)+ stack-specific role) - Infrastructure and deployment (use
Agent(devops-engineer)) - Writing code tests (use
Agent(qa-engineer)+test-strategyskill) - Content writing (use
Agent(content-writer)) - Context pipeline design, memory engineering, agent harness, RAG architecture, multi-agent orchestration, production AI checklists → use
context-engineeringskill
Key Concepts
Prompt as System
A prompt is not a string — it is a system composed of:
- Instruction hierarchy: System prompt > Developer prompt > User prompt > Retrieved content
- Context assembly: What enters the context window, in what order, with what priority
- Output contract: Schema, format, constraints, error handling, fallback behavior
- Tool interface: Available tools, their schemas, permissions, composition patterns
- Guard rails: Safety filters, refusal policies, output validators
- Versioning: Immutable versions, deployment tags, audit trail
Core Principles
- Eval-first: Define how to measure before changing anything
- Simplest technique: Zero-shot → few-shot → CoT → chaining. Escalate only when simpler fails
- Explicit over implicit: Spell out constraints, output format, edge cases. Never assume the model "knows"
- Separation of concerns: Instructions vs data vs examples — always delimited
- Grounding: Prefer citations and verifiable data over unanchored claims
- Least privilege: Minimal tool permissions per agent. HITL for high-impact actions
- Cost awareness: Every token costs money and time. Compress, cache, route
Resource Files
| File | Contents |
|---|---|
technique-guide.md |
Full technique taxonomy with decision tree, examples, and anti-patterns |
prompt-template-patterns.md |
Delimiter conventions, system prompt structure, few-shot formatting, CoT triggers, output schema patterns |
security-checklist.md |
OWASP LLM Top 10 mapped to prompt-level mitigations with checklist |
eval-and-testing-guide.md |
Eval frameworks, grader types, dataset curation, A/B testing, regression gates |
prompt-versioning-and-providers.md |
Version-control patterns for prompts, prompt registry layout, provider differences (Anthropic / OpenAI / Google / open-source), portability tradeoffs |
prompt-deployment-and-monitoring.md |
Production rollout patterns — staged release, canary, rollback, observability, cost/latency monitoring, drift detection, on-call runbook hooks |
advanced-techniques-and-models.md |
Advanced techniques (self-consistency, tree-of-thought, ReAct, reflection) and model-specific patterns (Claude reasoning vs OpenAI, Gemini long context, Haiku/Sonnet/Opus selection) |
skill-authoring-spec.md |
Cached agentskills.io digest — skill specification (frontmatter/naming/dirs/progressive disclosure), best practices, scripts, and skill-output eval. Read when authoring or auditing a plugin skill |
optimizing-descriptions.md |
Cached agentskills.io digest — writing skill description triggering surface: imperative phrasing, trigger eval queries, train/val split, optimization loop. Read when a skill is mis-triggering or its description needs tuning |
Integration
- Follows rules:
Agent(prompt-engineer)(prompt system architecture, security, eval-first quality) - Used by workflows:
/plugin-doctor(prompt-engineering checks during plugin self-diagnostic),/developand/feature-dev(AI features),/feature-design(Wave-2 review for AI/LLM systems),/code-review(prompt quality review),/plugin-skill-create(skill scaffolding follows prompt-engineering patterns) - Companion skills:
context-engineeringskill (context pipeline design, memory engineering, agent harness, RAG architecture, multi-agent orchestration, production checklists),code-reviewskill (review checklists) - Collaborates with roles:
Agent(software-engineer)(prompt integration),Agent(qa-engineer)(prompt regression tests),Agent(product-manager)(success metrics)