agentica-prompts

Write reliable prompts for Agentica/REPL agents that avoid LLM instruction ambiguity

By parcadei · 474 installs

npx skills add parcadei/continuous-claude-v3 --skill agentica-prompts

Source repository · Upstream listing

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: 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: Pattern Specific Prompts Swarm (Research) Hierarchical (Coordinator) Generator/Critic (Generator) Generator/Critic (Critic) Jury (Voter) Verb Mappings Action Bad (ambiguous) Good (explicit) Read "Read the file at X" "RETRIEVE contents of: X" Write "Put this in the file" "WRITE to X: {content}" Check "See if file has X" "RETRIEVE contents of: X. Contains Y? YES/NO." Edit "Change X to Y" "EDIT file X: replace 'old' with 'new'" Directory Handoff Mechanism Agents communicate via filesystem, not TaskOutput: Anti Patterns Pattern Problem Fix "Tell me what X contains" May summarize or hallucinate "Return the exact text" "Check the file" Ambiguous action Specify RETRIEVE or VERIFY Question form Invites generation Use imperative "RETRIEVE" "Read and confirm" May just say "confirmed" "Return the exact text" TaskOutput for handoff Floods context with transcript Directory based handoff "Be thorough" Subjective, inconsistent Specify exact output format Expected Improvement Without fixes: ~60% success rate With RETRIEVE + explicit return: ~95% success rate With structured tool schemas: ~98% success rate With directory handoff: Context preserved, no transcript pollution Code Map Injection Use RepoPrompt to generate code map for agent context: Memory Context Injection Explain the memory system to agents: References ToolBench (2023): Models fail ~35% retrieval tasks with ambiguous descriptions Gorilla (2023): Structured schemas improve reliability by 3x ReAct (2022): Explicit reasoning before action reduces errors by ~25%