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Grok Bot+18 agentsArchitecture Decision Record Writer
Converts informal notes, tickets, and PR discussions into structured Architecture Decision Records (ADRs).
$9.99
Architecture Decision Record Writer
Example session with this skill installed
We decided to use AWS Lambda for the image processing service instead of a dedicated EC2 cluster. We need to save costs on idle time and scale automatically during peaks. We considered EC2 but the ops overhead is too high.
- Read your context and instructions
- Compiled the architecture decision record
I have drafted the ADR based on your preference for serverless image processing.
Architecture Decision Record: Use AWS Lambda for Image Processing
ADR Metadata
- Status: Proposed
- Date: 2023-11-22
- Owner: Platform Engineering
- Related systems: Image Processing Service
Context
The current image processing workload is highly bursty, with long periods of inactivity followed by sharp spikes. Maintaining a dedicated EC2 cluster results in high costs for idle resources and requires significant manual effort for scaling and patching.
Decision Drivers
- Cost efficiency (pay-per-use).
- Operational simplicity (managed infrastructure).
- Automatic scaling to handle unpredictable traffic spikes.
Options Considered
Option 1 - AWS Lambda (Chosen)
- Summary: Serverless functions triggered by S3 uploads.
- Benefits: Zero cost for idle time; scales automatically; no server management.
- Drawbacks: Cold start latency; execution time limits (15 mins).
- Risks: Potential for high costs if traffic becomes constant and high-volume.
Option 2 - Dedicated EC2 Cluster
- Summary: Auto-scaling group of EC2 instances.
- Benefits: No execution time limits; predictable performance for long-running jobs.
- Drawbacks: High operational overhead for patching and scaling logic; cost incurred during idle periods.
Decision
Adopt AWS Lambda for the image processing service.
Rationale
Lambda aligns with the primary drivers of cost reduction and minimized operational overhead. The bursty nature of the workload makes serverless more economical than maintaining persistent virtual machines.
Consequences
- Positive: Reduced infrastructure bill and near-zero server maintenance.
- Negative: Image processing must be optimized to stay within Lambda timeout limits.
- Neutral: Monitoring must transition to CloudWatch Logs and X-Ray for distributed tracing.
Implementation Notes
- Implement S3 trigger for Lambda functions.
- Configure memory settings to balance processing speed and cost.
- Set up dead-letter queues (DLQ) for failed processing attempts.
Review Triggers
- Review if average monthly execution costs exceed the cost of a reserved EC2 instance.
- Review if image sizes grow to require >15 minutes of processing time.
Next steps
- Validate the maximum image size against the Lambda 10GB ephemeral storage limit.
- Define the retry policy for the S3 trigger.
- Review the draft with the security team for IAM role scoping.
Connects securely to your tools. The creator never sees your data.
What you get
About this skill
The problem
Engineering teams often make critical technical choices in meetings or Slack threads without documenting the "why." This leads to tribal knowledge, repeated arguments over settled topics, and future maintainers who are afraid to change code because they don't understand the original constraints.
What it does
- Converts informal notes, tickets, and PR discussions into structured Architecture Decision Records (ADRs).
- Extracts concrete decision drivers, trade-offs, and consequences from raw technical reasoning.
- Forces the documentation of rejected alternatives to ensure full decision traceability.
- Audits existing ADRs against a quality rubric to find weak logic or missing risk assessments.
- Generates implementation notes, review triggers, and follow-up actions for every decision.
Why this beats prompting it yourself
General LLMs often write vague design essays or skip the "negative consequences" section entirely. This skill enforces a strict architectural rubric that prioritizes technical debt visibility and future maintainability over marketing-speak. It ensures every "why" is backed by a specific driver rather than just calling a tool "scalable" or "fast."
Use cases
- Documenting a database migration or framework choice for a new service.
- Creating a durable record of why a specific third-party API was selected over building in-house.
- Auditing a team's documentation to ensure architectural decisions are actually traceable.
- Onboarding new lead engineers by providing a clear history of system constraints.
Known limitations
The skill does not verify technical claims, benchmarks, or security facts. It relies entirely on the technical context provided by the user.
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