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Shayan Rais
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# Weather Karachi System Flow
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This document describes the complete flow of the weather data fetching and transformation system.
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## System Overview
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The weather system consists of skills and specialized subagents that work together to fetch and transform temperature data for Karachi, Pakistan.
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## Flow Diagram
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```
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┌─────────────────────────────────────────────────────────────────┐
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│ User Interaction │
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└─────────────────────────────────────────────────────────────────┘
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│
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▼
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┌──────────────────┐
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│ /weather │
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│ Command │
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└──────────────────┘
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│
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│ invokes via Skill tool
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▼
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┌──────────────────┐
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│ /weather-karachi │
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│ Skill │
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└──────────────────┘
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│
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│ Step 1 (Sequential via Task tool)
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▼
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┌────────────────────────┐
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│ weather-fetcher │
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│ Subagent │
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│ (subagent_type) │
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└────────────────────────┘
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│
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▼
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┌────────────────────────┐
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│ wttr.in API │
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│ Fetch Temperature │
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│ for Karachi │
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└────────────────────────┘
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│
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│ Returns: 26°C
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▼
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│
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│ Step 2 (Sequential via Task tool)
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▼
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┌─────────────────────────┐
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│ weather-transformer │
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│ Subagent │
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│ (subagent_type) │
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└─────────────────────────┘
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│
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▼
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┌─────────────────────────┐
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│ input/input.md │
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│ Read Transform Rules │
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└─────────────────────────┘
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│
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│ Reads: "add +10"
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▼
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┌────────────────────────┐
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│ Apply Transform │
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│ 26 + 10 = 36°C │
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└────────────────────────┘
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│
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▼
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┌────────────────────────┐
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│ output/output.md │
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│ Write Results │
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└────────────────────────┘
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│
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▼
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┌────────────────────────┐
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│ Display Summary │
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│ to User │
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└────────────────────────┘
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```
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## Component Details
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### 1. Skills and Commands
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#### `/weather` (Command)
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- **Location**: `.claude/commands/weather.md`
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- **Purpose**: Entry point for weather operations
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- **Action**: Invokes `weather-karachi` skill via Skill tool
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- **Model**: haiku
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#### `/weather-karachi` (Skill)
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- **Location**: `.claude/skills/weather-karachi/SKILL.md`
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- **Purpose**: Orchestrates the weather fetching and transformation workflow
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- **Action**: Launches two specialized subagents sequentially via Task tool
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- **Model**: haiku
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### 2. Specialized Subagents
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#### `weather-fetcher`
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- **Location**: `.claude/agents/weather-fetcher.md`
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- **Purpose**: Fetch real-time temperature data
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- **Data Source**: wttr.in API for Karachi, Pakistan
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- **Output**: Temperature in Celsius (numeric value)
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- **Tools Available**: WebFetch
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#### `weather-transformer`
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- **Location**: `.claude/agents/weather-transformer.md`
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- **Purpose**: Apply mathematical transformations to temperature data
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- **Input Source**: `input/input.md` (transformation rules)
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- **Output Destination**: `output/output.md` (formatted results)
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- **Tools Available**: Read, Write
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### 3. Data Files
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#### `input/input.md`
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- **Purpose**: Stores transformation rules
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- **Format**: Natural language instructions (e.g., "add +10 in the result")
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- **Access**: Read by weather-transformer subagent
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#### `output/output.md`
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- **Purpose**: Stores formatted transformation results
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- **Format**: Structured markdown with sections:
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- Original Temperature
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- Transformation Applied
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- Final Result
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- Calculation Details
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## Execution Flow
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1. **User Invocation**: User runs `/weather` command or `/weather-karachi` skill
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2. **Skill Invocation**: `/weather` invokes `weather-karachi` skill via Skill tool
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3. **Sequential Subagent Execution** (via Task tool):
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- **Step 1**: `weather-fetcher` subagent fetches current temperature from wttr.in
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- **Step 2**: `weather-transformer` subagent:
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- Reads transformation rules from `input/input.md`
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- Applies rules to the fetched temperature
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- Formats and writes results to `output/output.md`
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4. **Result Display**: Summary shown to user with:
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- Original temperature
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- Transformation rule applied
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- Final transformed result
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## Example Execution
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```
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Input: /weather
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├─ Invokes: weather-karachi skill (via Skill tool)
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│ ├─ Subagent: weather-fetcher (via Task tool)
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│ │ └─ Result: 26°C
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│ ├─ Subagent: weather-transformer (via Task tool)
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│ │ ├─ Reads: input/input.md ("add +10")
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│ │ ├─ Calculates: 26 + 10 = 36°C
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│ │ └─ Writes: output/output.md
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│ └─ Output:
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│ ├─ Original: 26°C
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│ ├─ Transform: Add +10
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│ └─ Result: 36°C
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```
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## Key Design Principles
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1. **Separation of Concerns**: Each component has a single, clear responsibility
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2. **Sequential Execution**: Subagents run in order to ensure data dependencies are met
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3. **Specialized Subagents**: Task-specific subagents with minimal tool access
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4. **Skill-Based Architecture**: Skills orchestrate workflows, subagents execute tasks
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5. **Configurable Transformations**: Rules stored externally in input files
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6. **Structured Output**: Results formatted consistently in output files
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