update tutorial with 3. implementation phase add implementation commands
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1 Research Phase
1.0 Problem statement
Discuss
Discuss the problem and create in the docs/00_problem next files and folders:
problem_description.md: Our problem to solve with the end result we want to achieve.input_data: Put to this folder all the necessary input data and expected results for the further tests. Analyze very thoroughly input data and form system's restrictions and acceptance ctiteriarestrictions.md: Restrictions we have in real world in the -dashed list format.acceptance_criteria.md: Acceptance criteria for the solution in the -dashed list format. The most important part, determines how good the system should be.
Example:
problem_description.mdWe have wing type UAV (airplane). It should fly autonomously to predetermined GPS destination. During the flight it is relying on the signal form GPS module.
But when adversary jam or spoof GPS, then UAV either don't know where to fly, or fly to the wrong direction. So, we need to achieve that UAV can fly correctly to the destination without GPS or when GPS is spoofed. We can use the camera pointing downward and other sensor data like altitude, available form the flight controller. Airplane is running Ardupliot.input_data- orthophoto images from the UAV for the analysis
- list of expected GPS for the centers for each picture in csv format: picture name, lat, lon
- video from the UAV for the analysis
- list of expected GPS for the centers of video per timeframe in csv format: timestamp, lat, lon for each 1-2 seconds
- ...
restrictions.md- We're limiting our solution to airplane type UAVs.
- Additional weight it could take is under 1 kg.
- The whole system should cost under $2000.
- The flying range is restricted by eastern and southern part of Ukraine. And so on.
acceptance_criteria.md- UAV should fly without GPS for at least 30 km in the sunshine weather.
- UAV shoulf fly with maximum mistake no more than 40 meters from the real GPS
- UAV should fly correctly with little foggy weather with maximum mistake no more than 100 meters from the real GPS
- UAV should fly for minimum of 500 meters with missing inernal Satellite maps and the drifting error should be no more than 50 meters.
1.1 ✨AI Research: Restrictions and Acceptance Criteria assesment
Execute /1.research/1.1_research_assesment_acceptance_criteria
In case of external DeepResearch (Gemini, DeepSeek, or other), copypaste command's text and put to the research context:
- problem_description.md
- restrictions.md
- acceptance_criteria.md
- Samples of the input data
Revise
- Revise the result, discuss it
- Overwrite
acceptance_criteria.mdandrestrictions.md
1.2 ✨AI Research: Research the problem in great detail
Execute /1.research/1.2_research_problem
In case of external DeepResearch (Gemini, DeepSeek, or other), copypaste command's text and put to the research context:
- problem_description.md
- restrictions.md
- acceptance_criteria.md
- Samples of the input data
Revise
- Revise the result from AI.
- Research the problem as well
- Add/modify/remove some solution details in the draft. (Also with AI)
- Store it to the
docs/01_solution/solution_draft.md
1.3 ✨AI Research: Solution draft assessment
Execute /1.research/1.3_solution_draft_assessment
In case of external DeepResearch (Gemini, DeepSeek, or other), copypaste command's text and put to the research context:
- problem_description.md
- restrictions.md
- acceptance_criteria.md
- Samples of the input data
Revise
- Research by yourself as well - how to solve additional problems which AI figured out, and add them to the result.
Iterate
- Rename previous
solution_draft.mdto{xx}_solution_draft.md. Start {xx} from 01 - Store the new revised result draft to the
docs/01_solution/solution_draft.md - Repeat the process 1.3 from the beginning
When the next solution wouldn't differ much from the previous one, or become actually worse, store the last draft as docs/01_solution/solution.md
2. Planning phase
2.10 🤖📋AI plan: Generate components
Execute /2.planning/2.10_gen_components
Revise
- Revise the plan, answer questions, put detailed descriptions
- Make sure stored components are coherent and make sense
Store plan
- Save plan to
docs/02_components/00_decomposition_plan.md
2.15 🤖AI agent: Components assesment
Execute /2.planning/2.15_components_assesment
Revise
- Clarify the proposals and ask to fix found issues
2.20 🤖AI agent: Generate Jira Epics
Jira MCP
Add Jira MCP to the list in IDE:
"Jira-MCP-Server": {
"url": "https://mcp.atlassian.com/v1/sse"
}
Execute /2.planning/2.20_gen_epics use jira mcp
Revise
- Revise the epics, answer questions, put detailed descriptions
- Make sure epics are coherent and make sense
2.30 🤖AI agent: Generate tests
Execute /2.planning/2.30_gen_tests
Revise
- Revise the tests, answer questions, put detailed descriptions
- Make sure stored tests are coherent and make sense
2.40 🤖📋AI agent: Component Decomposition To Features
Execute
For each component in docs/02_components run
/2.planning/2.40_gen_features --component @xx__spec_[component_name].md
Revise
- Revise the features, answer questions, put detailed descriptions
- Make sure features are coherent and make sense
3. Development phase
3.05 🤖AI agent: Initial structure
Execute: /3.implementation/3.05_implement_initial_structure
Review
- Analyze the code, ask to do some adjustments if needed
3.10 🤖📋AI plan: Feature implementation
Execute
For each component in docs/02_components run
/3.implementation/3.10_implement_component @component_folder
Revise Plan
- Analyze the proposed development plan in a great detail, provide all necessary information
- Possibly reorganize plan if needed, think and add more input constraints if needed
- Improve plan as much as possible so it would be clear what exactly to do
Save Plan
- when plan is final and ready, save it as
[##]._plan_[component_name]to component's folder
Execute Plan
- Press build and let AI generate the code
Revise Code
- Read the code and check that everything is ok
3.20 🤖AI agent: Solution composition and integration tests
Read all the files here `docs/03_tests/` and for each file write down tests and run it.
Compose a final test results in a csv with the next format:
- Test filename
- Execution time
- Result
Fix all problems if tests failed until we got a successful result.
In case if one or more tests was failed due to missing data from user or API or other system, ask it from developer.
Repeat test cycle until no failed tests.