coding agents

Superpowers: How I Use Coding Agents in October 2025

Honghao Wang

— 3 min read
Superpowers: How I Use Coding Agents in October 2025

Superpowers: How I'm Using Coding Agents in October 2025

Read Jesse Vincent's October post

This is a follow-up to his September piece, but it’s substantial enough to stand on its own.

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Why Jesse’s Workflow Stands Out

Jesse Vincent is one of the most creative users of coding agents—especially Claude Code—I know. He has developed a refined, highly effective workflow that includes:

  • Test-driven development (TDD) with red/green cycling — ensuring a test fails before writing the fix.
  • Detailed step planning for clarity and reproducibility.
  • Self-updating memory notes for persistent context across sessions.
  • A feelings journal for agent self-reflection — e.g., “I feel engaged and curious about this project” (Claude).

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Claude Code Plugins & "Superpowers"

Claude Code recently introduced plugins.

To celebrate, Jesse bundled many of his techniques into a new plugin called Superpowers.

Installation

/plugin marketplace add obra/superpowers-marketplace
/plugin install superpowers@superpowers-marketplace

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Exploring the Repository

You can set aside time to browse the full Superpowers repo.

One fun example is the Root Cause Tracing skill:

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---
name: Root Cause Tracing
description: Systematically trace bugs backward through the call stack to identify the original trigger
when_to_use: Bug appears deep in the call stack but you need to locate its origin
version: 1.0.0
languages: all
---

**Overview**  
Bugs often appear deep within the call stack (e.g., running `git init` in the wrong directory, creating a file in an incorrect location, or opening a database with a wrong path). The instinct might be to fix the spot where the error is detected, but that only treats the symptom.  
**Core principle:** Trace backward through the call chain until you identify the original trigger, then fix the source.

**When to Use**

digraph when_to_use {

"Bug appears deep in stack?" [shape=diamond];

"Can trace backwards?" [shape=diamond];

"Fix at symptom point" [shape=box];

"Trace to original trigger" [shape=box];

"BETTER: Also add defense-in-depth" [shape=box];

"Bug appears deep in stack?" -> "Can trace backwards?" [label="yes"];

"Can trace backwards?" -> "Trace to original trigger" [label="yes"];

"Can trace backwards?" -> "Fix at symptom point" [label="no - dead end"];

"Trace to original trigger" -> "BETTER: Also add defense-in-depth";

}

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Why It’s Interesting

  • Uses a Graphviz DOT graph to visually represent the debugging workflow.
  • Claude can interpret DOT diagrams as instructions, enabling rich debugging visualizations.
  • Encourages backtracking through logic rather than treating superficial symptoms.

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DOT Graph Visualization

I quickly vibe-coded a URL-based DOT visualizer — see the rendered output here:

image

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Connecting Debugging Workflows to Multi-Platform Publishing

This kind of integrated skill documentation fits neatly into larger AI-assisted content ecosystems.

For example, AiToEarn enables:

  • Simultaneous publishing to Douyin, Kwai, WeChat, Bilibili, Rednote, Facebook, Instagram, LinkedIn, Threads, YouTube, Pinterest, X.
  • Automated AI content generation, analytics, and model ranking.
  • Monetization of both human-written and AI-generated methodology guides.

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Jesse’s Token-Efficient Architecture

> Core efficiency: Loads a single doc (<2k tokens).

> When a specific process step is needed, runs a shell script to retrieve it.

> An end-to-end chat sequence for building a to-do list app hit 100k tokens.

> Employs subagents for heavy task execution and token-hungry implementation work.

Jesse’s post also led me to Claude’s `/mnt/skills/public` directory — see my write-up here.

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Tools for AI Content Creators

For developers working on multi-agent systems or efficient token management, platforms like AiToEarn官网 can be invaluable:

  • Open-source AI content monetization.
  • Publish & earn from one AI-generated source across all major social platforms.
  • Integrates tools for generation, analytics, and AI model rankings.
  • Explore AiToEarn documentation or see the latest AI model leaderboard.

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Bottom line: Jesse’s Superpowers plugin and workflow are a masterclass in turning coding agents into truly personalized, multi-functional development companions. Anyone serious about leveraging Claude Code—or similar tools—should dive into his shared resources.

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Would you like me to also create a visual workflow map summarizing Jesse’s entire October “Superpowers” setup so it’s easier to replicate? That would make this even more actionable.

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