Dd File Issue logo

Dd File Issue

OrganizationPopular
DataDog
dd-file-issue

File GitHub issues to the right repository (pup CLI or plugin)

Overview

PublisherDataDog
Repositorypup
Skill namedd-file-issue
Stars
1K
Forks
127
Bundled files
Instructions only
LicenseApache-2.0
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by DataDog on GitHub. Read the source before you install it.

Installation

Install the Dd File Issue AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/DataDog/pup/tree/main/skills/dd-file-issue
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/DataDog/pup.git /tmp/pup
mkdir -p .claude/skills
cp -r /tmp/pup/skills/dd-file-issue .claude/skills/dd-file-issue
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Dd File Issue in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Dd File Issue on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Dd File Issue is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

DD File Issue Skill

This skill helps you file GitHub issues to the correct repository. All issues — whether related to the pup CLI, skills, agents, or documentation — are tracked in DataDog/pup.

Invocation: /dd-file-issue

Decision Logic: Which Repository?

File to DataDog/pup Repository

All issues are filed here, including:

  • API Functionality: API calls failing, incorrect responses, missing API endpoints
  • Authentication: OAuth2 issues, token refresh problems, API key handling
  • CLI Behavior: Command syntax, flags not working, output format issues
  • pup Installation: Binary installation, PATH issues, version problems
  • API Coverage: Missing Datadog API endpoints, incomplete command coverage
  • Performance: Slow API calls, timeout issues
  • Error Messages: Confusing pup error messages, API errors
  • Agent Behavior: Agent selection, agent prompts, agent responses
  • Documentation: README, CLAUDE.md, AGENTS.md, agent files inaccurate
  • Agent Prompts: Agent giving wrong guidance, unclear instructions
  • Skill Issues: Skills not working correctly
  • Agent Selection: Claude picking wrong agent for task
  • Examples/Guides: Missing or incorrect examples in agent files
  • User Experience: Confusing workflows, unclear instructions

Example Issues:

  • "pup logs search returns 404 error"
  • "OAuth token refresh not working"
  • "Need support for new Datadog API endpoint"
  • "pup --output=json returns invalid JSON"
  • "pup auth login fails on Linux"
  • "logs agent doesn't explain query syntax clearly"
  • "Agent tells me to use wrong pup command"
  • "Documentation says to use TypeScript but we use pup now"
  • "Need better examples for metrics queries"
  • "Agent selection guide is confusing"

Workflow

  1. Gather Information

    • What went wrong?
    • What were you trying to do?
    • What error message did you see (if any)?
    • What agent/command were you using?
  2. Determine Repository

    • All issues go to DataDog/pup
  3. Check for Existing Issues

    • Search the target repository for similar issues
    • If found, suggest commenting on existing issue instead
  4. Collect Issue Details

    • Title: Clear, concise summary (e.g., "logs agent: incorrect time format example")
    • Body: Detailed description with:
      • What happened
      • Expected behavior
      • Steps to reproduce
      • Environment details (OS, pup version, plugin version)
      • Relevant logs/errors
  5. Select Issue Type

    • Bug report
    • Feature request
    • Documentation improvement
    • Question
  6. Create Issue

    • Use gh issue create to file the issue
    • Add appropriate labels
    • Provide issue URL to user

Implementation

1. Determine Repository

All issues are filed against DataDog/pup. Ask clarifying questions to gather detail:

markdown
To file this issue correctly, I need to understand:

1. What were you trying to do?
2. What went wrong?
3. Was this a problem with:
   - A pup command not working?
   - An agent giving bad guidance?
   - Documentation being wrong/unclear?

2. Search for Existing Issues

bash
gh issue list \
  --repo DataDog/pup \
  --search "your search terms" \
  --limit 10

3. Create Issue

bash
gh issue create \
  --repo DataDog/pup \
  --title "Clear, concise title" \
  --body "Detailed description" \
  --label "bug" # or "enhancement", "documentation"

Issue Body Template

Use this template for comprehensive issue reports:

markdown
## Description
[Clear description of the issue]

## Expected Behavior
[What should happen]

## Actual Behavior
[What actually happened]

## Steps to Reproduce
1. [First step]
2. [Second step]
3. [...]

## Environment
- OS: [e.g., macOS 14.1, Ubuntu 22.04]
- Pup version: [run `pup --version`]
- Plugin version: [from plugin.json]
- Claude Code version: [if relevant]

## Error Messages / Logs

[Paste any error messages or relevant logs]


## Additional Context
[Any other relevant information]

## Suggested Fix (Optional)
[If you have ideas for how to fix this]

Best Practices

Good Issue Titles

✅ Good:

  • "logs agent: incorrect time format documentation"
  • "pup metrics query: --from flag ignores relative time"
  • "Agent selection guide: missing decision tree for security agents"

❌ Bad:

  • "It doesn't work"
  • "Bug"
  • "Help"

When to Bundle Issues

  • Don't bundle unrelated issues - file separately
  • Do bundle if multiple issues stem from same root cause
  • Do bundle if documenting several examples of same problem

Labels to Use

For pup repository:

  • bug - Something isn't working
  • enhancement - New feature or request
  • documentation - Improvements or additions to documentation
  • agent - Related to specific agent behavior
  • skill - Related to skills
  • question - Further information is requested

Examples

Example 1: Filing a pup Bug

User: "The pup logs search command times out after 30 seconds"

Analysis: This is a pup CLI issue (command behavior)

Steps:

  1. Search existing issues: gh issue list --repo DataDog/pup --search "timeout"
  2. If not found, create issue:
bash
gh issue create \
  --repo DataDog/pup \
  --title "pup logs search: command times out after 30 seconds" \
  --body "## Description
The \`pup logs search\` command times out after 30 seconds when searching large time ranges.

## Expected Behavior
Should either complete the query or allow configurable timeout.

## Actual Behavior
Command fails with timeout error after 30 seconds.

## Steps to Reproduce
1. Run: \`pup logs search --query='*' --from='7d' --to='now'\`
2. Wait 30 seconds
3. See timeout error

## Environment
- OS: macOS 14.1
- Pup version: 1.2.3
- DD_SITE: datadoghq.com

## Error Message
\`\`\`
Error: request timeout after 30000ms
\`\`\`
" \
  --label "bug"

Example 2: Filing a Documentation Issue

User: "The logs agent documentation shows TypeScript examples instead of pup commands"

Analysis: This is a documentation issue

Steps:

  1. Search existing issues: gh issue list --repo DataDog/pup --search "TypeScript"
  2. Create issue:
bash
gh issue create \
  --repo DataDog/pup \
  --title "logs agent: outdated TypeScript examples in documentation" \
  --body "## Description
The logs agent documentation still shows TypeScript/Node.js examples instead of pup CLI commands.

## Location
\`agents/logs.md\` lines 150-200

## Expected
Should show pup CLI command examples like:
\`\`\`bash
pup logs search --query='*' --from='1h'
\`\`\`

## Actual
Shows TypeScript code with imports and API clients

## Impact
Confusing for users who expect pup CLI commands

## Files Affected
- agents/logs.md
- Possibly other agent files (need audit)
" \
  --label "documentation" \
  --label "agent"

Example 3: Feature Request

User: "Can we add an agent for Datadog SLO reporting?"

Analysis: This is a pup enhancement (new agent)

bash
gh issue create \
  --repo DataDog/pup \
  --title "enhancement: add SLO reporting agent" \
  --body "## Feature Request

### Description
Add a new agent focused on SLO reporting and analysis, separate from the SLO management agent.

### Use Cases
- Generate SLO reports for stakeholders
- Analyze SLO trends over time
- Compare SLOs across services

### Proposed Agent Capabilities
- Query SLO history
- Generate formatted reports
- Calculate SLO burn rates
- Identify at-risk SLOs

### Related Agents
- Current \`slos.md\` focuses on SLO CRUD operations
- New agent would focus on analytics and reporting

### Priority
Medium - would improve SLO workflow
" \
  --label "enhancement" \
  --label "agent"

Error Handling

If issue creation fails:

  1. Check network connectivity
  2. Verify gh CLI is authenticated: gh auth status
  3. Check repository permissions
  4. Provide user with issue details to file manually

Success Response

After successfully filing an issue:

markdown
✅ Issue created successfully!

**Repository**: DataDog/pup
**Issue**: #123
**URL**: https://github.com/DataDog/pup/issues/123
**Title**: [issue title]

The team will review your issue and respond soon. You can:
- Track the issue at the URL above
- Add more details by commenting on the issue
- Subscribe to notifications for updates

Tips for Users

  1. Be specific - Include exact commands, error messages, agent names
  2. Provide context - What were you trying to accomplish?
  3. Include environment details - OS, versions, configuration
  4. One issue per report - Don't bundle unrelated problems
  5. Check existing issues first - Avoid duplicates
  6. Be patient - Maintainers will respond when available

Frequently asked questions

What does the Dd File Issue AI skill do?

File GitHub issues to the right repository (pup CLI or plugin)

Why use Dd File Issue on TypingMind?

Because you install it once and use it with any model. Dd File Issue is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Dd File Issue in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/DataDog/pup/tree/main/skills/dd-file-issue. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Dd File Issue?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Dd File Issue?

As many as you like. As long as a model supports skills, you can use Dd File Issue with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Dd File Issue AI skill free?

Yes. It is published on GitHub by DataDog under the Apache-2.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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