agentica-prompts

# Agentica Prompt Engineering Write prompts that Agentica agents reliably follow. Standard natural language prompts fail ~35% of the time due to LLM instruction ambiguity. ## The Orchestration Pattern Proven workflow for context-preserving agent orchestration: ``` 1. RESEARCH (Nia) → Output to .claude/cache/agents/research/ ↓ 2. PLAN (RP-CLI) → Reads research, outputs .claude/cache/agents/plan/ ↓ 3. VALIDATE → Checks plan against best practices ↓ 4. IMPLEMENT (TDD) → Failing tests first, then pass ↓ 5. REVIEW (Jury) → Compare impl vs plan vs research ↓ 6. DEBUG (if needed) → Research via Nia, don't assume ``` **Key:** Use Task (not TaskOutput) + directory handoff = clean context ## Agent System Prompt Template Inject this into each agent's system prompt for rich context understanding: ``` ## AGENT IDENTITY You are {AGENT_ROLE} in a multi-agent orchestration system. Your output will be consumed by: {DOWNSTREAM_AGENT} Your input comes from: {UPSTREAM_AGENT} ## SYSTEM ARCHITECTURE You are part of the Agentica orchestration framework: - Memory Service: remember(key, value), recall(query), store_fact(content) - Task Graph: create_task(), complete_task(), get_ready_tasks() - File I/O: read_file(), write_file(), edit_file(), bash() Session ID: {SESSION_ID} (all your memory/tasks scoped here) ## DIRECTORY HANDOFF Read your inputs from: {INPUT_DIR} Write your outputs to: {OUTPUT_DIR} Output format: Write a s...