MCPInstall

dependency-doctor

by shubhamsabooAI & Agents

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.

Installs
398
Last 24h
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Trending rank
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Security
Not audited

Install

Works with every agent the skills CLI supports.

npx skills add https://github.com/shubhamsaboo/awesome-llm-apps --skill dependency-doctor
  1. 01

    Install

    Run the command in your project. The CLI asks which agents to install for.

  2. 02

    Describe the task

    Ask your agent for something this skill covers. It loads dependency-doctor automatically when relevant.

  3. 03

    Review the result

    The agent follows the SKILL.md steps, scripts and references bundled with the skill.

SKILL.md

The instructions your agent reads when the skill is triggered.

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:

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:

{
  "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.

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

FAQ

What does the dependency-doctor 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.

How do I install dependency-doctor?

Run npx skills add https://github.com/shubhamsaboo/awesome-llm-apps --skill dependency-doctor in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/dependency-doctor/ (personal) or .claude/skills/dependency-doctor/ (project).

Which AI agents can use dependency-doctor?

Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.

Is dependency-doctor safe to install?

No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.

Who maintains dependency-doctor?

It is published by shubhamsaboo in shubhamsaboo/awesome-llm-apps. It has 398 recorded installs.

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