name: feature-implement description: Systematic approach to implementing new features in Rust memory systems audience: developers workflow: feature-development

Feature Implementation

Systematic approach to implementing new features in the Rust memory system.

Purpose

Add new functionality following project conventions, maintaining code quality and test coverage.

Implementation Process

Phase 1: Planning

1. Understand Requirements

What is the feature?
Why is it needed?
Who will use it?
What are the acceptance criteria?

2. Design Approach

How does it fit into existing architecture?
What modules need changes?
What new modules are needed?
What are the data structures?
What are the API signatures?

3. Check Constraints

File size limit: ≤ 500 LOC per file
Async/Tokio patterns for I/O
Error handling with anyhow::Result
Storage: Turso (durable) + redb (cache)

Phase 2: Implementation

1. Create Module Structure

# For new module
touch src/new_feature/mod.rs
touch src/new_feature/core.rs
touch src/new_feature/storage.rs

# Update src/lib.rs
# pub mod new_feature;

Example structure:

src/
├── new_feature/
│   ├── mod.rs          # Public API exports
│   ├── core.rs         # Core logic (<500 LOC)
│   ├── storage.rs      # Storage operations (<500 LOC)
│   └── types.rs        # Data structures (<500 LOC)

2. Define Types

// src/new_feature/types.rs
use serde::{Deserialize, Serialize};

/// Feature data structure
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct FeatureData {
    pub id: String,
    pub created_at: i64,
    pub data: serde_json::Value,
}

/// Configuration for feature
#[derive(Debug, Clone)]
pub struct FeatureConfig {
    pub enabled: bool,
    pub max_items: usize,
}

3. Implement Core Logic

// src/new_feature/core.rs
use anyhow::Result;

/// Main feature implementation
pub struct Feature {
    config: FeatureConfig,
}

impl Feature {
    /// Create new feature instance
    pub fn new(config: FeatureConfig) -> Self {
        Self { config }
    }

    /// Core feature operation
    pub async fn process(&self, input: FeatureData) -> Result<FeatureData> {
        // Validate input
        self.validate(&input)?;

        // Process
        let processed = self.process_internal(input).await?;

        // Store result
        self.store(&processed).await?;

        Ok(processed)
    }

    fn validate(&self, data: &FeatureData) -> Result<()> {
        // Validation logic
        Ok(())
    }

    async fn process_internal(&self, data: FeatureData) -> Result<FeatureData> {
        // Processing logic
        Ok(data)
    }

    async fn store(&self, data: &FeatureData) -> Result<()> {
        // Storage logic
        Ok(())
    }
}

4. Add Storage Layer

// src/new_feature/storage.rs
use super::types::FeatureData;
use anyhow::Result;

/// Turso storage for feature
pub struct FeatureStorage {
    turso: TursoClient,
}

impl FeatureStorage {
    pub async fn save(&self, data: &FeatureData) -> Result<()> {
        let sql = "INSERT OR REPLACE INTO feature_table (id, data, created_at) VALUES (?, ?, ?)";
        self.turso
            .execute(sql)
            .bind(&data.id)
            .bind(serde_json::to_string(&data.data)?)
            .bind(data.created_at)
            .await?;
        Ok(())
    }

    pub async fn get(&self, id: &str) -> Result<Option<FeatureData>> {
        let sql = "SELECT id, data, created_at FROM feature_table WHERE id = ?";
        let row = self.turso.query(sql).bind(id).await?;

        // Parse and return
        Ok(None) // Placeholder
    }
}

5. Implement Public API

// src/new_feature/mod.rs
mod core;
mod storage;
mod types;

pub use types::{FeatureConfig, FeatureData};
pub use core::Feature;

/// Convenience function for common use case
pub async fn quick_process(data: FeatureData) -> anyhow::Result<FeatureData> {
    let feature = Feature::new(FeatureConfig::default());
    feature.process(data).await
}

Phase 3: Testing

1. Unit Tests

#[cfg(test)]
mod tests {
    use super::*;

    #[test]
    fn test_feature_creation() {
        let config = FeatureConfig {
            enabled: true,
            max_items: 100,
        };
        let feature = Feature::new(config);
        assert!(feature.config.enabled);
    }

    #[test]
    fn test_validation() {
        let feature = Feature::new(FeatureConfig::default());
        let data = FeatureData {
            id: "test".to_string(),
            created_at: 0,
            data: serde_json::json!({}),
        };
        assert!(feature.validate(&data).is_ok());
    }

    #[tokio::test]
    async fn test_process() {
        let feature = Feature::new(FeatureConfig::default());
        let input = create_test_data();
        let result = feature.process(input).await;
        assert!(result.is_ok());
    }
}

2. Integration Tests

// tests/integration/feature_test.rs
use memory_core::new_feature::*;

#[tokio::test]
async fn test_end_to_end_feature() {
    // Setup
    let memory = create_test_memory().await;

    // Execute feature
    let data = FeatureData { /* ... */ };
    let result = memory.feature_operation(data).await;

    // Verify
    assert!(result.is_ok());

    // Check storage
    let stored = memory.get_feature_data("id").await.unwrap();
    assert_eq!(stored.id, "id");
}

Phase 4: Integration

1. Wire into Main API

// src/lib.rs
pub mod new_feature;

use new_feature::Feature;

pub struct SelfLearningMemory {
    // Existing fields...
    feature: Feature,
}

impl SelfLearningMemory {
    pub async fn feature_operation(&self, data: FeatureData) -> Result<FeatureData> {
        self.feature.process(data).await
    }
}

2. Update Database Schema

-- migrations/003_add_feature_table.sql
CREATE TABLE IF NOT EXISTS feature_table (
    id TEXT PRIMARY KEY,
    data TEXT NOT NULL,
    created_at INTEGER NOT NULL
);

CREATE INDEX idx_feature_created ON feature_table(created_at DESC);

3. Add Configuration

// src/config.rs
pub struct Config {
    // Existing config...
    pub feature_config: FeatureConfig,
}

Phase 5: Documentation

1. Code Documentation

/// Process feature data through the memory system.
///
/// This function takes raw feature data, validates it, processes it according
/// to learned patterns, and stores the result in both Turso and redb.
///
/// # Arguments
///
/// * `data` - The feature data to process
///
/// # Returns
///
/// Processed feature data with enriched information
///
/// # Errors
///
/// Returns error if:
/// - Data validation fails
/// - Storage operations fail
/// - Processing encounters unrecoverable error
///
/// # Example
///
/// ```no_run
/// use memory_core::new_feature::*;
///
/// #[tokio::main]
/// async fn main() -> anyhow::Result<()> {
///     let data = FeatureData {
///         id: "example".to_string(),
///         created_at: 1234567890,
///         data: serde_json::json!({"key": "value"}),
///     };
///
///     let result = quick_process(data).await?;
///     println!("Processed: {:?}", result);
///     Ok(())
/// }
/// ```
pub async fn feature_operation(&self, data: FeatureData) -> Result<FeatureData>

2. Update README

Add feature to main README.md:

## Features

- Episodic memory storage
- Pattern extraction and learning
- Context retrieval
- **NEW: Feature name** - Brief description

Phase 6: Quality Checks

# Format
cargo fmt

# Lint
cargo clippy --all -- -D warnings

# Build
cargo build --all

# Test
cargo test --all

# Documentation
cargo doc --no-deps

Phase 7: Commit

# Stage changes
git add src/new_feature/ tests/integration/feature_test.rs

# Commit with clear message
git commit -m "[feature] add new_feature module

- Implemented core Feature struct with process logic
- Added Turso storage layer with save/get operations
- Created comprehensive unit and integration tests
- Updated main API to expose feature_operation
- Added database migration for feature_table

Closes: #123
"

Best Practices

Code Organization

One feature per module
Split large modules (< 500 LOC per file)
Clear separation: types, core logic, storage, API

Error Handling

// Good: Propagate errors
pub async fn operation(&self) -> Result<Data> {
    let data = self.fetch().await?;
    self.process(data).await?;
    Ok(data)
}

// Bad: Swallow errors
pub async fn operation(&self) -> Option<Data> {
    let data = self.fetch().await.ok()?;  // Error info lost
    Some(data)
}

Async Patterns

// Good: Concurrent operations
let (result1, result2) = tokio::join!(
    operation1(),
    operation2(),
);

// Bad: Sequential when could be concurrent
let result1 = operation1().await;
let result2 = operation2().await;

Testing

Unit tests for each function
Integration tests for workflows
Test error cases
Test edge cases (empty, max, invalid)

Documentation

Public APIs must be documented
Include examples for complex APIs
Document errors and edge cases
Keep docs up to date with code

Feature Checklist

Common Pitfalls

1. Forgetting `.await`

// Wrong
let data = async_function();  // Returns Future, not Data

// Right
let data = async_function().await?;

2. Blocking in Async Context

// Wrong
async fn process() {
    std::thread::sleep(Duration::from_secs(1));  // Blocks executor!
}

// Right
async fn process() {
    tokio::time::sleep(Duration::from_secs(1)).await;
}

3. Not Testing Error Cases

#[tokio::test]
async fn test_error_handling() {
    let result = operation_with_invalid_input().await;
    assert!(result.is_err());
    assert!(matches!(result.unwrap_err(), Error::InvalidInput(_)));
}

4. Ignoring Performance

// Consider batch operations
for item in items {
    storage.save(item).await?;  // N database calls
}

// Better
storage.save_batch(items).await?;  // 1 database call

Integration with GitHub Release Best Practices

This skill integrates with the GitHub release best practices workflow for feature development and release management:

Release Preparation Integration

When implementing features for release:

Version Impact Assessment: Consult github-release-best-practices skill for semantic versioning implications
Changelog Planning: Consider Keep a Changelog format for feature documentation
Quality Gates: Ensure implementation meets release standards (>90% coverage, zero warnings)
Breaking Changes: Identify and document any breaking changes for migration guides

Coordination Workflows

Pre-Implementation: Use github-release-best-practices for version planning
Implementation: Follow feature-implement patterns while considering release requirements
Testing: Coordinate with testing-qa for release validation
Release Creation: Leverage github-release-best-practices agent for final release orchestration

Example Integration Pattern

## Feature Implementation → Release Workflow

1. **Assessment Phase**
   - github-release-best-practices: Analyze feature scope and version impact
   - feature-implement: Plan implementation with release considerations

2. **Implementation Phase**
   - feature-implement: Execute feature development
   - testing-qa: Ensure quality gates met
   - code-reviewer: Validate implementation standards

3. **Release Phase**
   - github-release-best-practices: Update changelog, create release notes
   - goap-agent: Coordinate final release creation

This integration ensures features are implemented with release readiness in mind, following 2025 GitHub release best practices for Rust workspaces.

feature-implement

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