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
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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%