| title | description | sidebar_position | author | ms.date | ms.topic | keywords | estimated_reading_time | |||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
Stage 6: Implementation |
Build features, write code, and create content with the full suite of AI-assisted development tools |
7 |
Microsoft |
2026-02-18 |
how-to |
|
8 |
Implementation is the highest-density stage in the project lifecycle, with 30 assets spanning agents, prompts, instructions, and skills. This stage covers coding, content creation, prompt engineering, data analysis, and infrastructure work. The RPI (Research, Plan, Implement) methodology provides structured execution guidance for complex tasks.
You enter Implementation after completing Stage 5: Sprint Planning with assigned work items. You also re-enter this stage from Stage 7: Review when rework is needed, from Stage 8: Delivery at the start of each new sprint, or from Stage 9: Operations for hotfixes.
Note
Prerequisites: Sprint planned with assigned work items. Development environment configured from Stage 1: Setup.
| Tool | Type | How to Invoke | Purpose |
|---|---|---|---|
| rpi-agent | Agent | Select rpi-agent agent | Orchestrate the full research-plan-implement workflow |
| task-researcher | Agent | Select task-researcher agent | Research requirements and gather codebase evidence |
| task-planner | Agent | Select task-planner agent | Create implementation plans from research findings |
| task-implementor | Agent | Select task-implementor agent | Build components following plans |
| task-reviewer | Agent | Select task-reviewer agent | Validate implementation against plan and research |
| gen-jupyter-notebook | Agent | Select gen-jupyter-notebook agent | Create data analysis notebooks |
| gen-streamlit-dashboard | Agent | Select gen-streamlit-dashboard agent | Generate Streamlit dashboards |
| prompt-builder | Agent | Select prompt-builder agent | Create and refine prompt engineering artifacts |
| Tool | Type | How to Invoke | Purpose |
|---|---|---|---|
| phase-implementor | Agent | Select phase-implementor agent | Execute individual implementation phases |
| prompt-updater | Agent | Select prompt-updater agent | Update existing prompts and instructions |
| researcher-subagent | Agent | Select researcher-subagent agent | Conduct focused research within tasks |
| Tool | Type | How to Invoke | Purpose |
|---|---|---|---|
| rpi | Prompt | /rpi |
Start the full RPI workflow |
| task-research | Prompt | /task-research |
Research requirements for a task |
| task-plan | Prompt | /task-plan |
Create an implementation plan from research |
| task-implement | Prompt | /task-implement |
Begin implementation of a specific task |
| task-review | Prompt | /task-review |
Review implementation against the plan |
| prompt-build | Prompt | /prompt-build |
Create a new prompt engineering artifact |
| prompt-analyze | Prompt | /prompt-analyze |
Analyze prompt quality and effectiveness |
| prompt-refactor | Prompt | /prompt-refactor |
Refactor and improve existing prompts |
| git-commit | Prompt | /git-commit |
Stage and commit changes |
| git-commit-message | Prompt | /git-commit-message |
Generate a commit message for staged changes |
All coding standard instructions activate automatically based on file type:
| Instruction | Activates On | Purpose |
|---|---|---|
| csharp | **/*.cs |
C# coding standards |
| python-script | **/*.py |
Python scripting standards |
| bash | **/*.sh |
Bash script standards |
| bicep | **/bicep/** |
Bicep infrastructure standards |
| terraform | **/*.tf |
Terraform infrastructure standards |
| workflows | .github/workflows/*.yml |
GitHub Actions workflow standards |
| markdown | **/*.md |
Markdown formatting rules |
| writing-style | **/*.md |
Voice and tone conventions |
| prompt-builder | AI artifacts | Prompt engineering authoring standards |
| hve-core-location | ** |
Reference resolution for hve-core |
| Tool | Type | How to Invoke | Purpose |
|---|---|---|---|
| video-to-gif | Skill | Referenced in chat | Convert video to optimized GIFs |
Engineers are the primary users of Implementation, spending the majority of their engagement time here. Tech Leads contribute architecture-sensitive implementations. Data Scientists use notebook and dashboard generators. SREs handle infrastructure code. New Contributors start with guided tasks.
/rpi Implement the pagination logic for the /api/v2/search endpoint.
Add cursor-based pagination with a default page size of 50 and a maximum
of 200 results per request. Follow the existing pagination pattern in
src/api/handlers/list-resources.py.
Use individual task agents when you want more control over each phase.
/task-research Investigate how the existing list-resources handler in
src/api/handlers/list-resources.py implements pagination. Identify the
cursor encoding strategy, default and maximum page sizes, and response
envelope structure.
After research completes, plan the implementation:
/task-plan Create an implementation plan for adding cursor-based pagination
to the /api/v2/search endpoint following the patterns documented in the
research output.
Execute the plan:
Select task-implementor agent:
Build the webhook delivery system following the plan in
.copilot-tracking/plans/webhook-delivery-plan.md. Start with the event
dispatcher component and implement the retry queue second.
Select gen-jupyter-notebook agent:
Create a data analysis notebook for the Q4 sales transactions dataset in
data/sales-q4-2025.parquet. Include data quality assessment, revenue trend
analysis by product category and region, and customer cohort segmentation
using RFM scoring with matplotlib visualizations.
After implementation, validate the changes:
/task-review Validate the pagination implementation against the plan.
Check cursor encoding, page size limits, response envelope consistency,
and error handling for invalid cursor values.
Implementation produces source code, documentation, notebooks, dashboards, prompt artifacts, and infrastructure definitions. Transition to Stage 7: Review when implementation is complete. Use /clear to reset context before starting the review cycle.
🤖 Crafted with precision by ✨Copilot following brilliant human instruction, then carefully refined by our team of discerning human reviewers.