Preface

Production errors, inability to reproduce locally, fixes that still don’t match symptoms after several changes—in these kinds of problems, the difficulty often isn’t in “knowing how to use breakpoints,” but rather that the session starts out scattered: the model jumps to guessing causes, the code area isn’t clearly delimited, intermediate conclusions aren’t verified by anyone, and finally there’s a pile of temporary logs and un-rolled-back changes left behind. Below we introduce Debug Ultra on SkillHub (@user_dc515faf/debug-session): it organizes a debugging or investigation session into fixed stages, from entering a state, scoping the code, building feedback, running falsifiable diagnosis, to language-specific tools and cleanup plus handoff.

What This Is

Debug Ultra is a dev-programming Skill published on SkillHub by community publisher user_dc515faf, current version 1.1.1, sourced as community. The official description positions it as a Combined debugging workflow: used when starting a debugging or investigation session. The page tag requires_api_key=false indicates no additional API Key is required.

SkillHub display page: https://www.skillhub.cn/skills/user_dc515faf/debug-session
API resource identifier: https://api.skillhub.cn/user_dc515faf/debug-session

Core Features

The following capabilities all come from the official English description, explained in workflow order, without elaborating on implementation details not present in the material.

  1. Activates caveman mode
    Enters a “dumber,” more fact-grounded debugging posture: less guessing, more observation, avoiding premature conclusions.

  2. Maps the relevant code area
    First outlines the code scope relevant to the problem, narrowing the search surface and reducing aimless changes across the entire repository.

  3. Builds a trusted feedback loop
    Establishes a trustworthy feedback loop: hypotheses, verifications, and results must be cross-checkable, rather than a one-way narrative.

  4. Runs falsifiable diagnosis
    Diagnostic steps must be falsifiable—able to specify “what should be observed if the hypothesis holds, and how to refute it if it doesn’t,” rather than untestable guesses.

  5. Uses language-specific debugging tools
    Incorporates debugging means matched to the current language/stack within the workflow (specific tools are determined by session context and guidance within the Skill; the material does not enumerate a list).

  6. Cleanup plus handoff
    Cleanup at the end (temporary changes, debug remnants, etc.) and handoff: leaving conclusions, unresolved items, and follow-up suggestions for the next session or collaborators.

Installation and Activation

The installation method given on the SkillHub page is: let the AI follow the official installation documentation, and specify the coordinate @user_dc515faf/debug-session.

Method 1: Prompt-based installation (page original text)

Send the following meaning to the current Agent (Cursor, Claude Code, etc., can execute according to their respective Skills mechanisms):

Please install `@user_dc515faf/debug-session` according to https://skillhub.cn/install/skillhub.md.

Method 2: SkillHub CLI

First confirm whether the CLI is installed (the documentation requires that when installing skills, --dir must point to the current Agent’s skills directory; otherwise the default ./skills/ may not be recognized):

command -v skillhub && skillhub --version

If not installed, the official installation script example:

curl -fsSL https://skillhub-1388575217.cos.ap-guangzhou.myqcloud.com/install/install.sh | bash

When installing this Skill, replace <skills directory> with your current environment’s path. Common directories listed in the official documentation include:

  • Cursor: ~/.cursor/skills/
  • Claude Code: ~/.claude/skills/
  • Codex: ~/.codex/skills/ or .agents/skills/ under the project

Example:

skillhub install user_dc515faf/debug-session --dir ~/.cursor/skills/

(The coordinate is written as @user_dc515faf/debug-session on the page; for the CLI’s install subcommand, refer to the SkillHub documentation and skillhub install help.)

After enabling, if the Agent supports Skills, instruct the model to load this Skill at the start of a debugging or investigation task; no API Key required.

Typical Usage

The official documentation does not provide a standalone long example; the trigger conditions are clearly stated in the introduction:

  • When to use: starting a debugging or investigation session.
  • How to use: describe the symptoms, reproduction conditions, and relevant modules in the conversation; let the Skill guide you through caveman mode → code area mapping → feedback loop → falsifiable diagnosis → language-specific tools → cleanup plus handoff.

Minimal reproducible steps:

  1. Complete the installation as described in the previous section, and confirm the Skill appears in the current Agent’s skills directory.
  2. Start a new conversation, describing the bug or behavior to investigate (error messages, environment, steps already tried).
  3. Explicitly request to follow the Debug Ultra workflow, and request a cleanup plus handoff summary at the end.

Applicable Scenarios and Notes

Who it’s for

  • Developers who need Agent assistance to find bugs, trace call chains, and cross-check hypotheses against logs.
  • Teams or individuals who want the debugging process to be phased, conclusions verifiable, and wrap-up with handoff, rather than a one-off “guess-and-fix” approach.

Limitations and notes

  • The material is sourced from community; behavior should be based on the SKILL.md in the Skill package and the Agent’s performance after loading; this article does not cite the full package content, and details should be based on local files after installation.
  • SkillHub page statistics (as of the material capture): rating 100000, downloads 690, install count 0—the install count is a platform metric and does not mean it is unavailable; whether you can install it correctly to the right --dir locally is what matters.
  • The workflow emphasizes falsifiability and feedback loops; you need to cooperate by providing verifiable information (logs, reproduction steps, version numbers); when information is insufficient, the flow will still get stuck at the “cannot refute/confirm hypothesis” stage, which aligns with the design intent rather than indicating the Skill has failed.

Conclusion

The value of Debug Ultra lies in converging “starting to investigate a problem” into a combined workflow: first stabilize judgment, scope the code, build feedback, run falsifiable diagnosis, use in-stack tools, and finally clean up and hand off. If you want to try installing it in Cursor or other environments, you can start from the SkillHub page or API identifier:

  • https://www.skillhub.cn/skills/user_dc515faf/debug-session
  • https://api.skillhub.cn/user_dc515faf/debug-session