Download .claude/skills/agentica-prompts/SKILL.md from the publisher, or copy the path and open the repository. PlugMCP lists the skill and does not host the file.
Where does agentica-prompts live?
The publisher keeps it at https://github.com/parcadei/Continuous-Claude-v3.
Which agents can use agentica-prompts?
Claude, Cursor, and Codex can read a SKILL.md you add from the publisher's repository.
Write reliable prompts for Agentica/REPL agents that avoid LLM instruction ambiguity
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 .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
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:
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 .claude/cache/agents/codemap.md
# Inject into agent system prompt
codemap=$(cat .claude/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%
How do I install agentica-prompts?
Download .claude/skills/agentica-prompts/SKILL.md from the publisher, or copy the path and open the repository. PlugMCP lists the skill and does not host the file.
Where does agentica-prompts live?
The publisher keeps it at https://github.com/parcadei/Continuous-Claude-v3.
Which agents can use agentica-prompts?
Claude, Cursor, and Codex can read a SKILL.md you add from the publisher's repository.