How Kiro Became My Coding Partner for Best Practices

For me, team retrospectives are tricky. I either don’t know what to say because I can’t remember everything that happened in the sprint, or I remember one detail and only focus on that. Then I realize important topics have been overlooked. Which means good discussions sometimes never even happen, simply because no one remembered to bring them up.

That’s the problem I wanted to solve. I needed a way to throw a net over all the places where work actually happens - starting with Linear, GitHub, and Slack - and pull that context into one place. With everything captured, retros could be based on the full story and not just fuzzy memories.

I decided to take on this project for the Kiro Hackathon as a way to see what it’s like to work with this tool from scratch. Very quickly I realized Kiro isn’t about jumping into code right away - it starts with structure.

Spec-Driven Development: Planning Before Coding

The first step with Kiro wasn’t just to start coding. Instead, I began by chatting about the project idea.

Kiro helped me shape a spec that laid out:

1. A requirements file with user stories and acceptance criteria.

2. A design document describing the overall architecture and app components.

3. A step-by-step implementation plan, breaking the project into clear tasks.

It was like having a roadmap before we even wrote a line of code. This time spent on planning really allowed me to execute with confidence.

Step by step process

Once the plan was ready, we started coding. What made Kiro special was that it didn’t just run off on its own. It showed me what it was doing, let me guide the process, and made sure I understood the code by highlighting and pointing back to every change.

It felt like having a real coding assistant who kept things on track and left a clear record of everything that was happening. Instead of vibe coding in the dark, I had a seatbelt that made sure we stayed on course.

Another thing I really valued was the ability to pause at any point and pick up right where we left off. No loss of context, no need to re-explain what we were doing - Kiro remembered the plan, the last changes, and the next steps. That made it feel less like starting from scratch each session and more like pair programming with a teammate.

Test-Driven by Default

Kiro also nudged the whole process into a test-driven mindset. For every major step or task, it created corresponding unit, integration and performance tests - even covering edge cases and adding fallbacks. That meant the project wasn’t just spec-driven; it was automatically test-driven too.

It felt like working with a partner who insists on good practices, making sure every feature was backed by tests from the start. Having that safety net made development feel more robust and aligned with best practices.

Hooks That Watch Your Back

Kiro also introduces hooks - small triggers that watch your codebase and run checks whenever something changes. I used them to automate the kind of maintenance work that usually eats up focus.

One example was a dead code detector. Whenever I replaced an old implementation, the hook scanned for functions and imports that were no longer used. It flagged them immediately so I could remove clutter before it piled up.

And all it took was a simple description, I explained in simple terms what I needed, and Kiro built the hook for me.

Integrations in One Go

One part that really impressed me was how Kiro handled integrations. I needed Retronet to connect with GitHub, Linear, and Slack, which usually means digging into three different API docs, figuring out authentication, and writing boilerplate client code — exactly the kind of repetitive work I like to offload.

Instead, I described the integrations in plain language, and Kiro went through the API documentation, generated the client code, and built the connections in one go. All three integrations were up and running without me having to piece them together manually.

It felt like having a teammate who happily handles the tedious setup, so I could stay focused on building the actual retro analysis logic.

Hitting a Blocker: Context Window Limits

One real challenge I hit was context window size when prompting the LLM to analyze the data. When I selected a big date range, the amount of data from GitHub, Linear, and Slack was far too large to pass directly into the model. The first runs blew past the window limits, and I’d lose important information as the calls failed.
After some back and forth with Kiro, I switched back into planning mode. We stepped through the problem together and came up with a better approach:

1. Fetch everything for the selected range
2. Split by date into chronological windows small enough to fit
3. Run inference on each chunk, extracting insights period by period
4. Aggregate the results into a single set of retro themes with evidence

Kiro generated a fresh plan for this chunk-and-merge workflow, and it worked. Every call stayed within safe limits, no matter how much data I threw at it.

// Step 1: Filter data for the selected date range
const filteredEvents = this._filterEventsByDateRange(
  sortedEvents,
  dateRange
);

// Step 2: Split into 24-hour chunks with 2-hour overlaps
const temporalData = this.temporalProcessor.processTeamData(
  teamData, 
  analysisContext.dateRange
);

// Step 3: Analyze each chunk individually
const chunkInsights = [];
for (const chunk of temporalData.chunks) {
  const chunkContext = {
    timeRange: chunk.summary.timeRange,
    eventCount: chunk.eventCount,
    patterns: chunk.patterns
  };

  // Run LLM inference on this chunk
  const insight = await this.provider.generateChunkSummary(
    chunk, 
    chunkContext
  );

  chunkInsights.push({ 
    timeRange: chunk.timeRange, 
    insight 
  });
}

// Step 4: Aggregate all chunks into final retrospective
const aggregatedData = this._buildTemporalAggregation(
  chunkInsights, 
  temporalData
);

const finalInsights = await this.provider.generateInsights(
  aggregatedData,
  { analysisType: 'temporal_aggregation' }
);

What Retronet Does

The final product, Retronet, automatically generates comprehensive sprint retrospectives by analyzing your team's actual work patterns. It pulls data from Linear, GitHub, and Slack to identify patterns, blockers, and achievements you might have missed, turning scattered information into data-driven insights for better retrospectives.

Key Takeaways

Building Retronet proved that AI assisted development doesn't have to mean sacrificing process for speed. The right AI partner can actually enforce better practices:

• Planning first creates better software than diving straight into implementation
• Understanding every line of code keeps you in control and helps you learn, rather than just shipping black box solutions
• Continuous testing catches problems before they become expensive to fix
• Automated maintenance keeps technical debt from accumulating

What made the difference was how Kiro acted like a real coding partner. It helped me plan first with specs, then guided me through design and step by step implementation. Along the way, hooks kept the codebase clean, tests gave me confidence, and when I hit blockers, like the context window issue, Kiro was right there in planning mode to figure out a path forward.

In the end, Retronet it’s proof that with the right tool, AI coding assistants doesn’t have to be pure vibes or chaos. Specs, structure, and a little help understanding the code can turn messy problems into something solid, useful, and production-ready.