sderosiaux

name	prepare-review
description	Break up branch changes into smaller, logically-grouped commits for easier code review. Use when asked to "prepare a PR", "clean up commits", "organize commits for review", "split commits", "prepare for review", before opening a pull request.
argument-hint	base-branch
user-invocable	true

sderosiaux

{
  "spinnerVerbs": {
    "mode": "replace",
    "verbs": [
      "Being 73% sure",
      "Confidently wrong",
      "Hallucinating responsibly",
      "Sampling from the void",
      "Flipping weighted coins",
      "I'll be back",

sderosiaux

Machine Health Assessment: $ARGUMENTS

Thorough local machine audit: security, processes, disk, network, users, services, compromise indicators. Cross-platform: Linux (systemd) and macOS (Darwin).

Setup: REPORT_DIR=$(mktemp -d /tmp/machine-health-XXXXX) && chmod 700 "$REPORT_DIR" && echo "Report: $REPORT_DIR"

Target

$ARGUMENTS	Behavior

sderosiaux

I tested 186,624 Kafka configurations with acks=all. Four settings explain the difference.

Subtitle: The biggest factor wasn't a producer config.

I set acks=all and replication.factor=3 on a Kafka cluster last week. Then I watched one scenario crawl at 0.42 MB/s with a p99 latency of 72 seconds while another, on the same cluster with the same durability guarantees, pushed 70.2 MB/s at 81 ms p99.

I expected the producer settings everyone talks about (batch.size, linger.ms) to explain most of that gap. They didn't. The biggest factor was a broker config I almost didn't test.

The experiment

sderosiaux

Machine Health Assessment: $ARGUMENTS

Thorough local machine audit: security, processes, disk, network, users, services, compromise indicators.

Setup: REPORT_DIR=$(mktemp -d /tmp/machine-health-XXXXX) && chmod 700 "$REPORT_DIR" && echo "Report: $REPORT_DIR"

Target

$ARGUMENTS	Behavior

sderosiaux

Testing whether algorithm names are cognitive activators: 3 prompts, same problem, different reasoning structures

Algorithm Names as Cognitive Activators — A Quick Experiment

Testing the thesis from Algorithm names are cognitive activators, not instructions.

Setup: Same decision problem, three cognitive framings, each run on a fresh Claude instance with zero context. The question: does the reasoning structure change, or just the vocabulary?

---

sderosiaux

<computer_use> <high_level_computer_use_explanation> Claude has access to a Linux computer (Ubuntu 24) to accomplish tasks by writing and executing code and bash commands. Available tools:

• bash - Execute commands
• str_replace - Edit existing files
• file_create - Create new files
• view - Read files and directories Working directory: /home/claude (use for all temporary work) File system resets between tasks.

sderosiaux

CFO Financial Analysis

Investment Evaluation and Financial Modeling

Initial Financial Reaction

Looking at this data lake hydration feature proposal... let me put on my CFO hat and really dig into what matters here from a financial and business strategy perspective.

My first instinct is to ask: what's the TAM expansion opportunity here? Data lake hydration sits at the intersection of streaming and analytics - that's a massive market convergence. But before I get excited about market size, I need to understand our existing customer base. How many of our current Conduktor customers are already trying to push streaming data into data lakes? Are they cobbling together solutions? What are they spending on this problem today?

[Relevance: 9/10 - TAM and existing customer spending directly inform the business case]

sderosiaux

CFO Financial Analysis

Investment Evaluation and Financial Modeling

Initial Financial Reaction

Looking at this data lake hydration feature proposal... let me put on my CFO hat and really dig into what matters here from a financial and business strategy perspective.

My first instinct is to ask: what's the TAM expansion opportunity here? Data lake hydration sits at the intersection of streaming and analytics - that's a massive market convergence. But before I get excited about market size, I need to understand our existing customer base. How many of our current Conduktor customers are already trying to push streaming data into data lakes? Are they cobbling together solutions? What are they spending on this problem today?

[Relevance: 9/10 - TAM and existing customer spending directly inform the business case]

sderosiaux

Conduktor Data Lake Hydration Feature Analysis

Analysis from multi-agent executive team on developing a comprehensive business case and go-to-market pitch

Table of Contents

1. Executive Summary & Business Case Report
2. Chief Product Officer Analysis
3. VP Sales Strategy
4. CFO Financial Analysis
5. VP Customer Success Insights
6. Chief Technology Officer Perspective