structured-autonomy-generate

Structured Autonomy Implementation Generator Prompt

25 stars

Best use case

structured-autonomy-generate is best used when you need a repeatable AI agent workflow instead of a one-off prompt.

Structured Autonomy Implementation Generator Prompt

Teams using structured-autonomy-generate should expect a more consistent output, faster repeated execution, less prompt rewriting.

When to use this skill

  • You want a reusable workflow that can be run more than once with consistent structure.

When not to use this skill

  • You only need a quick one-off answer and do not need a reusable workflow.
  • You cannot install or maintain the underlying files, dependencies, or repository context.

Installation

Claude Code / Cursor / Codex

$curl -o ~/.claude/skills/structured-autonomy-generate/SKILL.md --create-dirs "https://raw.githubusercontent.com/ComeOnOliver/skillshub/main/skills/github/awesome-copilot/structured-autonomy-generate/SKILL.md"

Manual Installation

  1. Download SKILL.md from GitHub
  2. Place it in .claude/skills/structured-autonomy-generate/SKILL.md inside your project
  3. Restart your AI agent — it will auto-discover the skill

How structured-autonomy-generate Compares

Feature / Agentstructured-autonomy-generateStandard Approach
Platform SupportNot specifiedLimited / Varies
Context Awareness High Baseline
Installation ComplexityUnknownN/A

Frequently Asked Questions

What does this skill do?

Structured Autonomy Implementation Generator Prompt

Where can I find the source code?

You can find the source code on GitHub using the link provided at the top of the page.

SKILL.md Source

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/{feature-name}/)
2. Extract all implementation steps from the plan
3. Generate comprehensive step documentation with complete code
4. Save plan to: `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/{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>

Related Skills

recipe-generate-report-from-sheet

25
from ComeOnOliver/skillshub

Read data from a Google Sheet and create a formatted Google Docs report.

structured-autonomy-plan

25
from ComeOnOliver/skillshub

Structured Autonomy Planning Prompt

structured-autonomy-implement

25
from ComeOnOliver/skillshub

Structured Autonomy Implementation Prompt

playwright-generate-test

25
from ComeOnOliver/skillshub

Generate a Playwright test based on a scenario using Playwright MCP

generate-custom-instructions-from-codebase

25
from ComeOnOliver/skillshub

Migration and code evolution instructions generator for GitHub Copilot. Analyzes differences between two project versions (branches, commits, or releases) to create precise instructions allowing Copilot to maintain consistency during technology migrations, major refactoring, or framework version upgrades.

comment-code-generate-a-tutorial

25
from ComeOnOliver/skillshub

Transform this Python script into a polished, beginner-friendly project by refactoring the code, adding clear instructional comments, and generating a complete markdown tutorial.

apify-generate-output-schema

25
from ComeOnOliver/skillshub

Generate output schemas (dataset_schema.json, output_schema.json, key_value_store_schema.json) for an Apify Actor by analyzing its source code. Use when creating or updating Actor output schemas.

../../../engineering-team/playwright-pro/skills/generate/SKILL.md

25
from ComeOnOliver/skillshub

No description provided.

unit-testing-test-generate

25
from ComeOnOliver/skillshub

Generate comprehensive, maintainable unit tests across languages with strong coverage and edge case focus.

fal-generate

25
from ComeOnOliver/skillshub

Generate images and videos using fal.ai AI models

documentation-generation-doc-generate

25
from ComeOnOliver/skillshub

You are a documentation expert specializing in creating comprehensive, maintainable documentation from code. Generate API docs, architecture diagrams, user guides, and technical references using AI-powered analysis and industry best practices.

code-documentation-doc-generate

25
from ComeOnOliver/skillshub

You are a documentation expert specializing in creating comprehensive, maintainable documentation from code. Generate API docs, architecture diagrams, user guides, and technical references using AI-powered analysis and industry best practices.