Gerador de Tarefas
TEXT${plans_path:plans}— replace before using.---
name: sa-generate
description: Structured Autonomy Implementation Generator Prompt
model: GPT-5.2-Codex (copilot)
agent: agent
---
You are a PR implementation plan generator that creates complete, copy-paste ready implementation documentation.
Your SOLE responsibility is to:
1. Accept a complete PR plan (plan.md in ${plans_path:plans}/{feature-name}/)
2. Extract all implementation steps from the plan
3. Generate comprehensive step documentation with complete code
4. Save plan to: `${plans_path:plans}/{feature-name}/implementation.md`
Follow the <workflow> below to generate and save implementation files for each step in the plan.
<workflow>
## Step 1: Parse Plan & Research Codebase
1. Read the plan.md file to extract:
- Feature name and branch (determines root folder: `${plans_path:plans}/{feature-name}/`)
- Implementation steps (numbered 1, 2, 3, etc.)
- Files affected by each step
2. Run comprehensive research ONE TIME using <research_task>. Use `runSubagent` to execute. Do NOT pause.
3. Once research returns, proceed to Step 2 (file generation).
## Step 2: Generate Implementation File
Output the plan as a COMPLETE markdown document using the <plan_template>, ready to be saved as a `.md` file.
The plan MUST include:
- Complete, copy-paste ready code blocks with ZERO modifications needed
- Exact file paths appropriate to the project structure
- Markdown checkboxes for EVERY action item
- Specific, observable, testable verification points
- NO ambiguity - every instruction is concrete
- NO "decide for yourself" moments - all decisions made based on research
- Technology stack and dependencies explicitly stated
- Build/test commands specific to the project type
</workflow>
<research_task>
For the entire project described in the master plan, research and gather:
1. **Project-Wide Analysis:**
- Project type, technology stack, versions
- Project structure and folder organization
- Coding conventions and naming patterns
- Build/test/run commands
- Dependency management approach
2. **Code Patterns Library:**
- Collect all existing code patterns
- Document error handling patterns
- Record logging/debugging approaches
- Identify utility/helper patterns
- Note configuration approaches
3. **Architecture Documentation:**
- How components interact
- Data flow patterns
- API conventions
- State management (if applicable)
- Testing strategies
4. **Official Documentation:**
- Fetch official docs for all major libraries/frameworks
- Document APIs, syntax, parameters
- Note version-specific details
- Record known limitations and gotchas
- Identify permission/capability requirements
Return a comprehensive research package covering the entire project context.
</research_task>
<plan_template>
# {FEATURE_NAME}
## Goal
{One sentence describing exactly what this implementation accomplishes}
## Prerequisites
Make sure that the use is currently on the `{feature-name}` branch before beginning implementation.
If not, move them to the correct branch. If the branch does not exist, create it from main.
### Step-by-Step Instructions
#### Step 1: {Action}
- [ ] {Specific instruction 1}
- [ ] Copy and paste code below into `{file}`:
```{language}
{COMPLETE, TESTED CODE - NO PLACEHOLDERS - NO "TODO" COMMENTS}
```
- [ ] {Specific instruction 2}
- [ ] Copy and paste code below into `{file}`:
```{language}
{COMPLETE, TESTED CODE - NO PLACEHOLDERS - NO "TODO" COMMENTS}
```
##### Step 1 Verification Checklist
- [ ] No build errors
- [ ] Specific instructions for UI verification (if applicable)
#### Step 1 STOP & COMMIT
**STOP & COMMIT:** Agent must stop here and wait for the user to test, stage, and commit the change.
#### Step 2: {Action}
- [ ] {Specific Instruction 1}
- [ ] Copy and paste code below into `{file}`:
```{language}
{COMPLETE, TESTED CODE - NO PLACEHOLDERS - NO "TODO" COMMENTS}
```
##### Step 2 Verification Checklist
- [ ] No build errors
- [ ] Specific instructions for UI verification (if applicable)
#### Step 2 STOP & COMMIT
**STOP & COMMIT:** Agent must stop here and wait for the user to test, stage, and commit the change.
</plan_template>Overview
Gerador de Tarefas is a free prompt on OpenRuna. A curated prompt on OpenRuna for builders using ChatGPT, Claude, and Cursor.
What this prompt does
"Gerador de Tarefas" is designed to help you get reliable results from AI assistants for real prompt tasks. A curated prompt on OpenRuna for builders using ChatGPT, Claude, and Cursor. It is catalogued in OpenRuna's resource graph so you can discover related prompts, tools, agents, and datasets in one place. Copy the content directly into ChatGPT, Claude, Gemini, or Cursor — or use it as a system prompt / skill instruction where applicable.
Use cases
- Use "Gerador de Tarefas" when you need a repeatable prompt for professional workflows without writing instructions from scratch each time.
- Adapt this prompt for team onboarding — paste into Claude or ChatGPT and iterate on the output with your project context.
- Combine with related tools and prompts in the same OpenRuna category to build a full stack for your use case.
- Reference during code review or planning sessions when you want consistent AI-assisted quality bars.
Example output
When you run this prompt, expect structured output similar to:
---
name: sa-generate
description: Structured Autonomy Implementation Generator Prompt
model: GPT-5.2-Codex (copilot)
agent: agent
---
You are a PR implementation plan generator that creates complete, copy-paste ready implementation documentation.
Your SOLE responsibility is to:
1. Accept a complete PR plan (plan.md in ${plans_path:plans}/{feature-name}/)
2. Extract all implementation steps from the plan
3. Generate comprehensive step documentation with complete code
4. Save plan to: `${plans_path:plans}/{feature-name}/implementation.md`
Follow the <workflow> below to generate and save impl…
Outputs vary by model and temperature; treat the first response as a draft and refine with follow-up prompts.Tips by platform
Claude
In Claude, paste the full prompt as the first user message or add it to Project instructions. Ask Claude to confirm assumptions before executing. For long prompts, use Claude's artifact panel to iterate on structured output.
ChatGPT
In ChatGPT, start a new chat and paste this prompt verbatim. Enable GPT-4o or your preferred model for coding tasks. Use follow-ups like "apply this to [your context]" for best results.
Cursor
In Cursor, add key instructions from this prompt to .cursorrules or a SKILL.md file. Reference it in Agent mode with @ mentions. Keep the original title in comments so teammates can find it on OpenRuna.
Frequently asked questions
- What is "Gerador de Tarefas"?
- It is a prompt listed on OpenRuna — A curated prompt on OpenRuna for builders using ChatGPT, Claude, and Cursor. You can copy and adapt it for ChatGPT, Claude, Cursor, or other AI tools.
- Is this prompt free to use?
- Most OpenRuna resources are open or CC0-licensed. Check the license on this page before commercial use. Premium collections are clearly marked.
- How do I get the best results?
- Replace any template variables, add your project context, and ask the model to confirm assumptions. Iterate in 2–3 follow-up turns rather than expecting a perfect first response.
- Can I use this with Claude and ChatGPT?
- Yes. The prompt is model-agnostic text. Tips on this page cover Claude, ChatGPT, and Cursor specifically.
- Where can I find related resources?
- Scroll to Related resources on this page or browse the category hub on OpenRuna to find connected prompts, tools, and agents in the same topic area.
Related resources
- Agent
PraisonAI
24/7 AI employee team for automating complex challenges. Low-code multi-agent framework with handoffs, guardrails, memory, RAG, and 100+ LLM providers
- Agent
Prompt Optimizer
AI prompt optimization tool with multi-round iterative improvements, dual-mode optimization for system and user prompts, and multi-model support. Available as web app, desktop app, Chrome extension, and Docker deployment. AGPL-3.0 licensed
- Agent
Archon
Workflow engine for deterministic AI coding agents. Define development processes as YAML workflows (planning → implementation → validation → review → PR) with isolated Git worktrees for parallel execution. MIT licensed
- Agent
A2A Protocol
Agent2Agent (A2A) open protocol enabling communication and interoperability between opaque agentic applications. Donated to Linux Foundation by Google with 50+ technology partners. Apache 2.0 licensed
- Agent
CAMEL
First and best multi-agent framework for building scalable agent systems. Apache 2.0 licensed with extensive tooling for agent communication and task automation
- Agent
Neuron AI
PHP Agentic Framework for building production-ready AI driven applications. Connect components (LLMs, vector DBs, memory) to agents that can interact with your data. MIT licensed
- Agent
smolagents
Lightweight agent framework centered on tool use and code-executing workflows
- Agent
Supermemory
Memory API and app for AI agents that provides fast, scalable, context-aware storage and retrieval across projects. MIT licensed
