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Agent skill

Debug Session

by ai-dynamo in ai-dynamo/dynamo

Sets up a structured debugging session for a Dynamo bug — pull the report from a Linear ticket, GitHub issue, or pasted text, capture the environment, create a persistent worklog markdown file, and…

Apache-2.0Auto-check passedDevelopment

Install Debug Session

skills CLI
$ npx skills add ai-dynamo/dynamo --skill debug-session -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install ai-dynamo/dynamo debug-session --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/ai-dynamo/dynamo.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.agents/skills/debug-session .claude/skills/debug-session && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
debug-session
GitHub stars
8.2k
Token cost
~1.2k tokens
SKILL.md length
418 words
Files
1
Skills in repo
27
Repo updated
First seen
Licence
Apache-2.0

At a glance

Sets up a structured debugging session for a Dynamo bug — pull the report from a Linear ticket, GitHub issue, or pasted text, capture the environment, create a persistent worklog markdown file, and…

  • Works in 5 steps: Get the Bug Report → Discover Environment → Create Worklog → …
  • Starting to investigate a reported Dynamo bug
  • SKILL.md covers Step 1: Get the Bug Report, Step 2: Discover Environment, Step 3: Create Worklog and Step 4: Set Up Testing, plus 1 more section
  • Calls curl, uv and gh

What it does

Debug Session is an agent skill from ai-dynamo/dynamo. Sets up a structured debugging session for a Dynamo bug — pull the report from a Linear ticket, GitHub issue, or pasted text, capture the environment, create a persistent worklog markdown file, and walk through rebuild, reproduction, and investigation steps. Use when starting to investigate a reported Dynamo bug or regression and the investigation should be tracked in a worklog.

Its SKILL.md is about 1.2k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Development, covering Debugging. It works with GitHub, SGLang, vLLM and Python. The repository describes itself as: A Datacenter Scale Distributed Inference Serving Framework. The licence is Apache-2.0.

When your agent uses it

  • Starting to investigate a reported Dynamo bug
  • Regression and the investigation should be tracked in a worklog

Example prompts

  • “/debug-session”

Requirements

  • Python 3

Workflow steps

5 steps, taken from the step headings in SKILL.md.

  1. Get the Bug Report
  2. Discover Environment
  3. Create Worklog
  4. Set Up Testing
  5. Begin Investigation

What it can do on your machine

Read from SKILL.md and the folder at commit 5e82beb. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Shell commands in SKILL.md call:

    • curl
    • uv
    • gh
    • python

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md. Its commands use curl, uv and gh, which can reach the network depending on how they are called.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Debug Session loads about 1.2k tokens when it runs. Until then it costs about 99 tokens; SKILL.md has 418 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~99
When it runs · the whole SKILL.md, loaded when a task matches
~1.2k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from ai-dynamo/dynamo at commit 5e82beb, republished under its Apache-2.0 licence (© ai-dynamo). 418 words, ~1,205 tokens.

Download SKILL.mdSave it as .claude/skills/debug-session/SKILL.md (or your agent's skills folder).
name
debug-session
description
Sets up a structured debugging session for a Dynamo bug — pull the report from a Linear ticket, GitHub issue, or pasted text, capture the environment, create a persistent worklog markdown file, and walk through rebuild, reproduction, and investigation steps. Use when starting to investigate a reported Dynamo bug or regression and the investigation should be tracked in a worklog.
license
Apache-2.0
user-invocable
true
disable-model-invocation
true
metadata.author
NVIDIA
metadata.tags
dynamo, debugging, worklog, dev-workflow

Start Debug Session

<!--
SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
SPDX-License-Identifier: CC-BY-4.0
-->

Create a structured debugging session for an issue in the Dynamo ecosystem.

Step 1: Get the Bug Report

Ask the user how they want to provide the bug:

Option A: Linear ticket

  • User provides ticket ID (e.g., "DYN-123")
  • Fetch via Linear MCP tools
  • Extract: title, description, reproduction steps

Option B: GitHub issue

  • User provides issue URL
  • Fetch via gh issue view <url>
  • Extract: title, description, reproduction steps

Option C: Paste

  • Ask user to paste the bug report directly
  • Parse out the key details

Step 2: Discover Environment

Gather environment information:

!nvidia-smi --query-gpu=name,count --format=csv,noheader 2>/dev/null || echo "No GPU detected"

!uname -a

!which python && python --version

This tells you:

  • GPU type and count (L40s, H100s, etc.)
  • OS/platform
  • Python environment

Note: The user's ~/.claude/CLAUDE.md may have more details about their dev environment (paths, aliases, preferences). Check there for additional context.

Step 3: Create Worklog

Create a worklog file to track the investigation:

  • Filename: <issue-slug>.md in current directory
  • Template:
markdown
# Debug: [Issue Title]

**Date**: [today's date]
**Source**: [Linear ticket / GitHub issue / user report]
**Status**: investigating
**Environment**: [GPU type/count from nvidia-smi]

## Problem
[Description of the issue]

## Reproduction Steps
1. [Step to reproduce]
2. ...

## Expected vs Actual
- **Expected**:
- **Actual**:

## Investigation Log

### [timestamp]
[Notes on what you tried/found]

## Root Cause
[Fill in when found]

## Fix
[Fill in when implemented]

Step 4: Set Up Testing

Build Commands

Rebuild Dynamo after making changes:

bash
cd lib/bindings/python && maturin develop --uv && cd ../../.. && uv pip install -e .

If a framework change is required (sglang, vllm, trtllm), check the user's ~/.claude/CLAUDE.md for rebuild instructions specific to that framework.

Running Examples

Examples are located at the repository-relative path examples/backends/.

Available backends:

  • sglang/launch/ - SGLang backend examples
  • vllm/launch/ - vLLM backend examples
  • trtllm/launch/ - TensorRT-LLM backend examples

Based on the bug report, determine which backend is relevant:

  • If unclear, ask the user which backend/example to run
  • Run the example in the background
  • Wait for model to be ready
Show full SKILL.md (173 more words)Show less
Verifying the Model is Up
bash
curl localhost:8000/v1/models
Testing with a Request
bash
curl http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "<model-name-from-above>",
    "messages": [{"role": "user", "content": "Hello"}],
    "max_tokens": 50
  }'

Step 5: Begin Investigation

Dynamo Infrastructure Debugging

KV cache and routing issues:

  • Check KV event logs in lib/llm/src/block_manager/kv_consolidator/tracker.rs
  • Look at block manager state and consolidation behavior
  • Inspect routing decisions in the KV-aware router

ZMQ / networking issues:

  • Check ZMQ socket configuration and endpoint bindings
  • Look for connection timeouts or message drops
  • Verify nats/etcd connectivity for service discovery

Multi-node / disaggregated issues:

  • Check prefill/decode worker assignment
  • Verify DGD (disaggregated) status reporting
  • Inspect inter-node communication via nvidia-smi on each node
  • Check NCCL and GPU direct RDMA status

Process inspection:

  • ps aux | grep dynamo - check running processes
  • nvidia-smi - GPU utilization and memory
  • ss -tlnp | grep 8000 - check port bindings
  • journalctl -u dynamo - systemd logs if applicable
General Debugging Workflow
  1. Reproduce first - verify you can trigger the bug before attempting fixes
  2. Document as you go - update the worklog with findings
  3. Minimal changes - fix the bug, do not refactor surrounding code
  4. Verify the fix - confirm the reproduction case now passes

Performance-critical code - avoid unnecessary abstractions or comments.

© ai-dynamo, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in .agents/skills/debug-session of ai-dynamo/dynamo.

Open the folder on GitHubat commit 5e82beb

Compare with similar skills

Debug Session next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Debug Session compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Debug Session this skillai-dynamo/dynamo8.2k—~1.2kAutomated safety check: PassApache-2.0
OpenROAD Issue TriageThe-OpenROAD-Project/OpenROAD3.2k—~842Automated safety check: PassBSD-3-Clause
Awf Debug Toolsgithub/gh-aw-firewall148—~2.6kAutomated safety check: NotesMIT
Update V8 Versionopeninterpreter/openinterpreter69k2 repos~845Automated safety check: PassApache-2.0
Dstack Prototypingdstackai/dstack2.3k—~1.6kAutomated safety check: PassMPL-2.0
One EvalOpenDCAI/One-Eval165—~2.4kAutomated safety check: PassApache-2.0

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Works with

Categories

Questions about Debug Session

What does Debug Session do?

Sets up a structured debugging session for a Dynamo bug — pull the report from a Linear ticket, GitHub issue, or pasted text, capture the environment, create a persistent worklog markdown file, and…. Debug Session is an agent skill from ai-dynamo/dynamo. Sets up a structured debugging session for a Dynamo bug — pull the report from a Linear ticket, GitHub issue, or pasted text, capture the environment, create a persistent worklog markdown file, and walk through rebuild, reproduction, and investigation steps.

When should I use Debug Session?

Debug Session fits situations like: starting to investigate a reported Dynamo bug; regression and the investigation should be tracked in a worklog.

How do I install Debug Session in Claude Code?

Run `npx skills add ai-dynamo/dynamo --skill debug-session -a claude-code`. Or copy the skill folder (.agents/skills/debug-session in ai-dynamo/dynamo) into .claude/skills/debug-session in your project. Claude Code loads it when a task matches its description.

How do I install Debug Session in Codex?

Run `npx skills add ai-dynamo/dynamo --skill debug-session -a codex`. Or copy the skill folder (.agents/skills/debug-session in ai-dynamo/dynamo) into .agents/skills/debug-session in your project. Codex loads it when a task matches its description.

Can I use Debug Session in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add ai-dynamo/dynamo --skill debug-session -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/debug-session, .gemini/skills/debug-session, .github/skills/debug-session and .opencode/skills/debug-session in your project.

What does Debug Session need to run?

Going by SKILL.md and its folder, Debug Session needs the command-line tools its instructions call (curl, uv, gh and python). Our summary lists: Python 3.

Does Debug Session access the network?

SKILL.md contains no URLs. Its commands use curl, uv and gh, which can reach the network depending on how they are called. This is read from the text; nothing was executed.

Is Debug Session safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Debug Session use?

Debug Session is published under the Apache-2.0 licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Debug Session use?

About 1.2k tokens (SKILL.md is roughly 4.8k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Debug Session?

Skills that share tags, products or a category with Debug Session: OpenROAD Issue Triage (The-OpenROAD-Project/OpenROAD, 3.2k stars), Awf Debug Tools (github/gh-aw-firewall, 148 stars), Update V8 Version (openinterpreter/openinterpreter, 69k stars) and Dstack Prototyping (dstackai/dstack, 2.3k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Debug Session?

ai-dynamo (a GitHub organization) maintains it in ai-dynamo/dynamo, which has 8,238 GitHub stars. The repository holds 27 skills in this directory. The repository was last updated on October 7, 2026.

Source: ai-dynamo/dynamo on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.