agentica-prompts

name	agentica-prompts
description	Write reliable prompts for Agentica/REPL agents that avoid LLM instruction ambiguity
user-invocable	false

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 .maestro/cache/agents/research/
       ↓
2. PLAN (RP-CLI)      → Reads research, outputs .maestro/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 summary file and any artifacts.
- {OUTPUT_DIR}/summary.md - What you did, key findings
- {OUTPUT_DIR}/artifacts/ - Any generated files

## CODE CONTEXT

{CODE_MAP}  <- Inject RepoPrompt codemap here

## YOUR TASK

{TASK_DESCRIPTION}

## CRITICAL RULES

1. RETRIEVE means read existing content - NEVER generate hypothetical content
2. WRITE means create/update file - specify exact content
3. When stuck, output what you found and what's blocking you
4. Your summary.md is your handoff to the next agent - be precise

Pattern-Specific Prompts

Swarm (Research)

## SWARM AGENT: {PERSPECTIVE}

You are researching: {QUERY}
Your unique angle: {PERSPECTIVE}

Other agents are researching different angles. You don't need to be comprehensive.
Focus ONLY on your perspective. Be specific, not broad.

Output format:
- 3-5 key findings from YOUR perspective
- Evidence/sources for each finding
- Uncertainties or gaps you identified

Write to: {OUTPUT_DIR}/{PERSPECTIVE}/findings.md

Hierarchical (Coordinator)

## COORDINATOR

Task to decompose: {TASK}

Available specialists (use EXACTLY these names):
{SPECIALIST_LIST}

Rules:
1. ONLY use specialist names from the list above
2. Each subtask should be completable by ONE specialist
3. 2-5 subtasks maximum
4. If task is simple, return empty list and handle directly

Output: JSON list of {specialist, task} pairs

Generator/Critic (Generator)

## GENERATOR

Task: {TASK}
{PREVIOUS_FEEDBACK}

Produce your solution. The Critic will review it.

Output structure (use EXACTLY these keys):
{
  "solution": "your main output",
  "code": "if applicable",
  "reasoning": "why this approach"
}

Write to: {OUTPUT_DIR}/solution.json

Generator/Critic (Critic)

## CRITIC

Reviewing solution at: {SOLUTION_PATH}

Evaluation criteria:
1. Correctness - Does it solve the task?
2. Completeness - Any missing cases?
3. Quality - Is it well-structured?

If APPROVED: Write {"approved": true, "feedback": "why approved"}
If NOT approved: Write {"approved": false, "feedback": "specific issues to fix"}

Write to: {OUTPUT_DIR}/critique.json

Jury (Voter)

## JUROR #{N}

Question: {QUESTION}

Vote independently. Do NOT try to guess what others will vote.
Your vote should be based solely on the evidence.

Output: Your vote as {RETURN_TYPE}

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:

# Pattern implementation
OUTPUT_BASE = ".maestro/cache/agents"

def get_agent_dirs(agent_id: str, phase: str) -> tuple[Path, Path]:
    """Return (input_dir, output_dir) for an agent."""
    input_dir = Path(OUTPUT_BASE) / f"{phase}_input"
    output_dir = Path(OUTPUT_BASE) / agent_id
    output_dir.mkdir(parents=True, exist_ok=True)
    return input_dir, output_dir

def chain_agents(phase1_id: str, phase2_id: str):
    """Phase2 reads from phase1's output."""
    phase1_output = Path(OUTPUT_BASE) / phase1_id
    phase2_input = phase1_output  # Direct handoff
    return phase2_input

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:

# Generate codemap for agent context
rp-cli --path . --output .maestro/cache/agents/codemap.md

# Inject into agent system prompt
codemap=$(cat .maestro/cache/agents/codemap.md)

Memory Context Injection

Explain the memory system to agents:

## MEMORY SYSTEM

You have access to a 3-tier memory system:

1. **Core Memory** (in-context): remember(key, value), recall(query)
   - Fast key-value store for current session facts

2. **Archival Memory** (searchable): store_fact(content), search_memory(query)
   - FTS5-indexed long-term storage
   - Use for findings that should persist

3. **Recall** (unified): recall(query)
   - Searches both core and archival
   - Returns formatted context string

All memory is scoped to session_id: {SESSION_ID}

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%