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Dev Dependency Management

Community
vasilyu1983
dev-dependency-management

Guides dependency management across languages and ecosystems. Use when choosing package managers, lockfiles, update policy, security scanning, SBOMs, or monorepo patterns.

Overview

Publishervasilyu1983
RepositoryAI-Agents-public
Skill namedev-dependency-management
Stars
87
Forks
19
Bundled files
28
LicenseMIT
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.

  • 28 bundled files

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

  • Open source

    Published by vasilyu1983 on GitHub. Read the source before you install it.

Installation

Install the Dev Dependency Management 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/vasilyu1983/AI-Agents-public.git /tmp/AI-Agents-public
mkdir -p .claude/skills
cp -r /tmp/AI-Agents-public/frameworks/shared-skills/skills/dev-dependency-management .claude/skills/dev-dependency-management
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Dev Dependency Management 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 Dev Dependency Management 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 Dev Dependency Management 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 Management

Use this skill for package-manager choice, lockfile policy, update strategy, supply-chain controls, and dependency hygiene across common ecosystems. It owns reproducibility and security defaults, not framework-specific app architecture.

Quick Reference

When to Use

  • Choose or migrate a package manager.
  • Define lockfile, wrapper, or toolchain pinning policy.
  • Add, remove, pin, or upgrade dependencies safely.
  • Resolve dependency conflicts or transitive-risk issues.
  • Set up audits, SBOM generation, provenance checks, or update automation.
  • Define dependency policy for monorepos or polyglot repos.

Route Elsewhere

  • Framework-specific frontend or backend implementation: use the relevant software skill.
  • AppSec architecture beyond the dependency layer: use software-security-appsec.
  • CI/CD platform design: use ops-devops-platform.

Defaults

  • Lockfile-first installs.
  • Pinned package manager or wrapper where the ecosystem supports it.
  • Smallest safe dependency change first.
  • Patch updates often, majors in isolation.
  • SBOM and audit workflow for release artifacts.
  • AI-suggested packages are untrusted until verified.

Workflow

  1. Identify ecosystem, package manager, lockfile, and wrapper conventions already present.
  2. Decide the smallest safe change: add, remove, pin, update, audit, or migrate.
  3. Load only the guidance needed for lockfiles, security, monorepo policy, or update strategy.
  4. Verify volatile tool behavior and security claims against official sources before recommending migrations.
  5. Finish with reproducibility checks, rollback notes, and any audit or SBOM follow-up.

ASCII Flow

text
dependency management request
  -> identify ecosystem, package manager, lockfile, wrapper, and workspace shape
  -> classify change: add, remove, pin, update, audit, migrate, or policy
  -> choose smallest safe dependency move
  -> check security, provenance, license, transitive risk, and AI package risk
  -> update lockfile or policy according to repo conventions
  -> run reproducibility, test, audit, and SBOM checks where available
  -> document rollback, owner, expiry, and follow-up

Core Decisions

Package-Manager Defaults

EcosystemDefault for new reposCurrent stableKey constraint
Nodepnpm unless compat pressure favors npmpnpm 12 (Rust-native, requires Node 22)pnpm 11+ is pure ESM, SQLite store; pnpm 12 is a native Rust port via pnpm self-update; verify CI Node version
Pythonuvuv 0.11.x (0.x but production-stable)Still on 0.x versioning; core APIs stable
RustCargostable toolchaincommit Cargo.lock for apps
Gogo modulescurrent go toolchaingo.mod + go.sum canonical
JavaMaven wrapper or Gradle wrappersee upstreamwrappers plus BOMs or version catalogs
.NETPackageReferencecurrent .NET SDKPackageReference over packages.config
PHPComposercurrent stablecommit composer.lock for apps

Keep repo-local consistency more important than theoretical ecosystem purity.

Lockfile and Toolchain Policy

Minimum rules:

  • commit application lockfiles
  • use exact lockfile installs in CI
  • avoid hand-editing lockfiles
  • do not carry multiple lockfiles for one package graph
  • prefer repo-local wrappers or toolchain files over global latest installs

Update Strategy

Default cadence:

  • patch: small and frequent
  • minor: batched and tested
  • major: isolated with release-note review and rollback plan
  • security: prioritize by exploitability and exposure, not CVSS alone

Supply-Chain Controls

Use:

  • official registries where possible
  • provenance or signature verification where supported
  • SBOM generation for release artifacts (CISA 2025 draft adds mandatory component hash, license, tool name, and generation context fields)
  • explicit review of install or build scripts
  • expiration on overrides, resolutions, and temporary pins
  • pnpm minimumReleaseAge or Renovate minimumReleaseAge to avoid consuming just-published packages (Shai-Hulud, Axios, and Miasma npm compromises 2025-2026 repeatedly show a fast-publish-to-attack window under three hours)
  • dependency confusion mitigations: scope all internal packages, audit all org-scoped packages before use
  • npm v12 (shipping July 2026) disables install scripts, git dependencies, and remote-URL dependencies by default; plan the CI migration before it lands, and treat any repo still relying on install scripts as a review item, not a blocker to skip

AI-Generated Dependency Risk

Before accepting an AI-suggested package:

  • verify it exists
  • check for typosquatting risk
  • check maintenance and release cadence
  • prefer standard library or existing dependencies if feasible
  • run the same audit and review workflow as for any other new package

Output Modes

Default to one of these:

  • Dependency policy brief: manager choice, lockfile rules, update cadence, and security controls.
  • Upgrade plan: scope, batching, testing, rollback, and audit follow-up.
  • Dependency audit: health risks, unmaintained packages, graph complexity, and remediation order.
  • Monorepo dependency strategy: workspace model, shared policy, and update automation rules.

Known Traps

  • Upgrading transitive or security-sensitive packages without checking whether the fix actually lands in the production artifact path.
  • Mixing wrapper, lockfile, and package-manager upgrades in one move, which makes rollback and blame assignment much harder.
  • Assuming monorepo hoisting or workspace dedupe is harmless when postinstall scripts, peer deps, or native builds are involved.
  • Accepting temporary pins or overrides without a removal owner, expiry, and retest trigger.
  • Treating SBOM generation as complete supply-chain control while provenance, install scripts, and release process remain unreviewed.

Anti-Patterns

  • Mixing package managers or lockfiles casually.
  • Installing without the lockfile in CI.
  • Migrating ecosystems for novelty instead of clear value.
  • Treating AI-suggested packages as trusted by default.
  • Leaving temporary overrides in place without owners or expiry.
  • Chasing vulnerability counts without considering exploitability and production exposure.

References

FileWhat it covers
references/ecosystem-guides.mdPer-ecosystem package-manager defaults, CI install commands, and watchouts for Node, Python, Rust, Go, Java, .NET, PHP
references/lockfile-management.mdLockfile matrix, golden rules, per-ecosystem exact-install commands, CI rules, and drift recovery
references/security-scanning.mdNative audit commands, SBOM generation, provenance controls, Dependabot/Renovate usage, and triage workflow
references/monorepo-patterns.mdJS/TS workspace defaults, pnpm supply-chain settings, polyglot structure, and version governance
references/dependency-selection-guide.mdAdd-or-avoid decision criteria, graph inspection commands, AI-suggested package checklist
references/update-strategies.mdUpdate cadence table, batch-by-risk workflow, bot policy, and rollback rule
references/transitive-dependencies.mdTree inspection, override patterns, deduplication, and resolution decision tree
references/license-compliance.mdLicense risk table, GPL decision tree, automated tooling, CI integration, and SBOM generation commands
references/version-conflict-resolution.mdConflict types, per-manager diagnostic commands, forced resolution syntax, and pnpm catalogs
references/container-dependency-patterns.mdMulti-stage build patterns, layer caching, vulnerability scanning (Trivy/Grype), and reproducible base image pinning
references/semver-guide.mdSemVer constraint syntax for npm, Python, and Cargo with common pitfalls
references/anti-patterns.mdCritical and moderate anti-patterns with corrective examples

Navigation

Fact-Checking

  • Known bugs, regressions, framework/compiler/runtime footguns, and version-specific crash or workaround guidance must be verified against current primary web sources before being treated as current fact.
  • Curated source links live in data/sources.json.
  • Package-manager defaults, CLI flags, provenance behavior, and SBOM guidance are volatile and should be verified against current official docs before giving definitive recommendations.
  • Prefer official package-manager, standards, and security-advisory sources over secondary blogs.

Learnings Loop

Before applying this skill on a non-trivial task, read learnings.consolidated.md in this directory (and learnings.md if present).

After applying it, if you encountered a pattern worth remembering, a mistake worth preventing, or a domain fact that surprised you, append one dated bullet to learnings.md via agents-skills-feedback-loop/scripts/append_learning.py. Do not modify SKILL.md itself.

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 Dev Dependency Management AI skill do?

Guides dependency management across languages and ecosystems. Use when choosing package managers, lockfiles, update policy, security scanning, SBOMs, or monorepo patterns.

Why use Dev Dependency Management on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/vasilyu1983/AI-Agents-public/tree/main/frameworks/shared-skills/skills/dev-dependency-management. 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 Dev Dependency Management?

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 Dev Dependency Management?

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

Is the Dev Dependency Management AI skill free?

Yes. It is published on GitHub by vasilyu1983 under the MIT 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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