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Dependency Doctor

CommunityPopular
Shubhamsaboo
dependency-doctor

Checks requirements.txt, pyproject.toml, and package.json dependency manifests for surface-level direct-dependency footguns: standard-library shadowing pins, abandoned backports, unpinned dependencies, and obvious intra-manifest conflicts, plus opt-in PyPI yanked releases. Use when the user asks to check a manifest for dependency problems, asks why dependencies won't install or whether anything is wrong with their dependencies, wants a dependency autopsy, or suspects dependency manifest rot. Runs offline by default as a local tool for the user's own project, not repository CI.

Overview

PublisherShubhamsaboo
Repositoryawesome-llm-apps
Skill namedependency-doctor
Stars
138.7K
Forks
20.4K
Bundled files
2
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.

  • 2 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

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

Installation

Install the Dependency Doctor 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.
  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/Shubhamsaboo/awesome-llm-apps.git /tmp/awesome-llm-apps
mkdir -p .claude/skills
cp -r /tmp/awesome-llm-apps/agent_skills/dependency-doctor .claude/skills/dependency-doctor
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Dependency Doctor 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 Dependency Doctor 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 Dependency Doctor 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.

Dependency Doctor

Inspect one dependency manifest on the user's machine for direct, surface-level footguns. Explain each finding in plain language, then offer a small, reviewable fix. This does not diagnose a failed pip or uv resolution.

This is a local developer tool for a project the user chooses. It is not a repository-wide lint rule, a CI gate, or a proposal to enforce dependency policy across unrelated apps.

When to use

  • The user asks to check, audit, diagnose, or autopsy a dependency manifest
  • The user wants to rule out direct-manifest issues before deeper install debugging
  • The user suspects stale pins, backports, duplicate entries, or dependency rot
  • The user asks whether anything looks wrong with their dependencies

When not to use

  • Installing the current dependencies without diagnosing them
  • Upgrading every package or adding a new package
  • A full vulnerability audit. Use pip-audit, npm audit, or the project's approved security scanner for CVE coverage
  • Creating a repo-wide CI check. This skill is user-invoked and local

Choose the manifest

Use the path the user names. If no path is given and several manifests exist, ask which one to inspect. Do not sweep the repository or edit anything merely because the skill was triggered.

Supported inputs:

  • requirements.txt
  • pyproject.toml using PEP 621 or common Poetry dependency tables
  • package.json dependency sections

Run the offline diagnosis

From this skill directory:

bash
python3 scripts/dep_doctor.py /path/to/requirements.txt --json

The default path is fully offline. It reads only the selected manifest. The report shape is:

json
{
  "file": "/path/to/requirements.txt",
  "findings": [
    {
      "severity": "high",
      "kind": "stdlib-shadowing",
      "package": "pathlib",
      "line": 4,
      "why": "...",
      "fix": "..."
    }
  ],
  "summary": {
    "total": 1,
    "by_severity": {"high": 1},
    "by_kind": {"stdlib-shadowing": 1},
    "online": false
  }
}

The offline checks cover:

  • Python standard-library names published as packages
  • Known backports that should not be installed on supported Python versions
  • Dependencies without a usable version constraint
  • Repeated package entries
  • Conflicting exact pins for the same package

For package.json, Python-specific standard-library and backport checks do not apply. The doctor still checks unpinned values and repeated dependency entries.

Explain the diagnosis

Read references/dependency-pitfalls.md before presenting findings. Lead with high severity items, then medium and low. For each finding, include:

  1. Package and source line
  2. What can break
  3. The suggested fix

Do not call every range a conflict. The deterministic core reports conflicting constraints only when exact pins disagree. Compatible constraints split across multiple lines are duplicate entries that should be combined.

If there are no findings, say what was checked and note the limits. A clean report is not a CVE audit or a full dependency resolver.

Optional PyPI yank check

The online check sends package names and exact pinned versions to pypi.org. Ask for permission before enabling it, even if the user previously requested an offline diagnosis.

bash
python3 scripts/dep_doctor.py /path/to/requirements.txt --json --online

It reports an exact Python release only when every file for that release is marked yanked. Network failures become low-severity findings instead of hiding the offline diagnosis.

Offer fixes, do not apply them silently

After explaining the report, offer a focused edit. Wait for approval before changing the manifest.

  • Remove standard-library packages from supported Python projects
  • Remove obsolete backports, or add a Python-version marker when an old runtime genuinely needs one
  • For an unpinned dependency, inspect the working environment's installed version, confirm it is intended, and propose an exact reviewed pin
  • Keep one entry for duplicates and combine compatible constraints
  • For conflicting exact pins, inspect dependents before choosing a version
  • Replace a yanked pin with a tested, non-yanked release

After any approved edit, rerun the offline diagnosis and the project's existing install or test command. Do not introduce a new CI gate.

Files

  • scripts/dep_doctor.py: stdlib-only manifest parser and diagnosis engine
  • references/dependency-pitfalls.md: reasoning guide for the reported risks

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Dependency Doctor AI skill do?

Checks requirements.txt, pyproject.toml, and package.json dependency manifests for surface-level direct-dependency footguns: standard-library shadowing pins, abandoned backports, unpinned dependencies, and obvious intra-manifest conflicts, plus opt-in PyPI yanked releases. Use when the user asks to check a manifest for dependency problems, asks why dependencies won't install or whether anything is wrong with their dependencies, wants a dependency autopsy, or suspects dependency manifest rot. Runs offline by default as a local tool for the user's own project, not repository CI.

Why use Dependency Doctor on TypingMind?

Because you install it once and use it with any model. Dependency Doctor 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 Dependency Doctor in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Shubhamsaboo/awesome-llm-apps/tree/main/agent_skills/dependency-doctor. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Dependency Doctor?

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 Dependency Doctor?

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

Is the Dependency Doctor AI skill free?

Yes. It is published on GitHub by Shubhamsaboo 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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