# Agentica Prompts

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

- Repository: https://github.com/parcadei/Continuous-Claude-v3 (3947 stars)
- Category: AI and agents
- License: MIT
- Last updated: 2026-01-26
- Runs scripts: no, instructions only

## Install

Claude Code: `npx skills add parcadei/Continuous-Claude-v3 --skill agentica-prompts -g -a claude-code -y`
Codex: `npx skills add parcadei/Continuous-Claude-v3 --skill agentica-prompts -g -a codex -y`
Cursor: `npx skills add parcadei/Continuous-Claude-v3 --skill agentica-prompts -g -a cursor -y`
Gemini CLI: `npx skills add parcadei/Continuous-Claude-v3 --skill agentica-prompts -g -a gemini-cli -y`

## Description

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

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

# 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 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

The rest: https://github.com/parcadei/Continuous-Claude-v3/blob/HEAD/.claude/skills/agentica-prompts/SKILL.md