benmatselby/dotfiles/dependency-evaluator
Claude Code and agent skill by benmatselby/dotfiles
- community
Checking that the SKILL.md file is still in the repository.
Evaluates whether a GitHub repository (tool, library, or framework) is worth adopting for the current project. Use when given a GitHub repo URL to assess as a potential dependency or tool.
Install
npx vskill@latest install benmatselby/dotfiles/dependency-evaluatorSKILL.md
Dependency Evaluator
Evaluate whether an external GitHub repository is worth adopting for the current project.
Instructions
1. Fetch the dependency details
Gather information about the dependency from two sources:
README and docs -- Use WebFetch to retrieve the repository's README. Read it thoroughly to understand what the tool does, how it works, and what problems it solves.
Repository metadata -- Use the gh CLI to fetch structured metadata:
From these sources, establish:
- What the tool/library does in concrete terms.
- Its primary language and tech stack.
- Its maturity: age, stars, release history, recent commit activity.
- Its maintenance status: is it actively developed, archived, or abandoned?
- Its license and any constraints that implies.
- Its dependencies and operational requirements (e.g. requires dnsmasq, needs root access, macOS-only).
2. Explore the current project
Use the Task tool with the explore agent to understand the current project. The exploration should focus on aspects relevant to the dependency being evaluated. At minimum, establish:
- The project's primary language(s) and framework(s).
- The dependency management approach (package.json, pyproject.toml, go.mod, etc.).
- The deployment model (Docker, Kubernetes, bare metal, serverless, etc.).
- The development workflow (dev servers, build tools, test frameworks).
- Any existing solutions in the codebase that overlap with what the dependency provides (e.g. if evaluating a proxy tool, look for existing proxy or networking configuration).
Tailor the exploration to the dependency. If the dependency is a testing library, focus on the project's test setup. If it is a deployment tool, focus on the project's deployment configuration.
3. Assess applicability
Compare the dependency against the current project across these dimensions:
- Problem fit -- Does this dependency solve a problem the project actually has? Is the problem significant enough to warrant a new dependency, or is it a minor inconvenience with a simpler fix?
- Tech stack compatibility -- Does it align with the project's language, framework, and runtime? Would adoption require introducing a new language or toolchain?
- Complexity trade-off -- What does adoption cost in terms of configuration, learning curve, and ongoing maintenance? What does it save? Is the trade-off favourable?
- Maturity and maintenance -- Is the project mature enough to rely on? Consider stars, commit frequency, number of contributors, issue response times, and whether it has reached a stable release.
- Operational fit -- Does it work with the project's deployment model? Are there platform restrictions (e.g. macOS-only, requires specific infrastructure)?
- Alternatives -- Are there simpler ways to achieve the same result? Could a small amount of custom code, a configuration change, or an already-installed dependency solve the problem?
4. Present the report
Structure the output as follows:
Dependency Summary -- What the tool/library is, what it does, its current status (maturity, maintenance, platform support). Keep this factual and concise.
Current Project Context -- The relevant aspects of the current project's architecture, tech stack, and existing patterns that relate to the dependency. Reference specific files using the file_path:line_number format.
Applicability Assessment -- A detailed evaluation covering each dimension from step 4. Be specific: reference concrete aspects of both the dependency and the current project. Present this as prose, not a checklist.
Recommendation -- A clear verdict: adopt, do not adopt, or conditionally adopt. State the reasoning plainly. If the recommendation is conditional, specify what conditions would need to be true (e.g. "adopt if you plan to run multiple Docker Compose stacks simultaneously").
Guidelines
- Be honest and direct. If it is not a good fit, say so clearly. A dependency that adds complexity without solving a real problem is a net negative.
- Ground the assessment in specifics. Reference files, configurations, and architecture decisions in the current project.
- Evaluate the dependency as it is today, not as it might be in the future. Roadmap promises do not count.
- Flag early-stage tools (few commits, no releases, single contributor) as a risk factor.
- Consider the project's scale. A tool for large teams may be overkill for a small personal project.
- If you cannot make a confident assessment, say so and list what additional information would help.
Security
- Security scanPassed, 100 of 100Pattern checks on the skill's files.
- Model reviewNot runA model review is scheduled for the next rescan.
Quality evaluation
- Task evaluationNot runNo evaluation yet. Run one in Skill Studio.
Source
- SKILL.md in the repositoryCheckingagents/skills/dependency-evaluator/SKILL.md
Popularity
- Repository stars
- 68
- Repository forks
- 14
- 7-day trend score
- 21
Stars and forks are for the whole GitHub repository, not this skill alone. Refreshed 18 May 2026, 5 months ago.
Works with
Any agent vskill installs to: 56 targets in all. These 39 have a page on this site.
- Claude Code
- Cursor
- GitHub Copilot
- Windsurf
- Codex
- Gemini CLI
- Cline
- Amp
- Roo Code
- Goose
- Continue
- Kilo Code
- Junie
- Trae
- OpenHands
- Qwen Code
- AI Agent
- Tabnine Chat
- Sourcegraph Cody
- Amazon Q
- Firebase Genkit
- Sweep
- Devin
- Phind
- Mentat
- Claude Desktop
- Aider
- GPT Pilot
- Plandex
- bolt.new
- v0
- Replit Agent
- Anse
- TypingMind
- BetterChatGPT
- LibreChat
- Warp AI
- Pieces
- Fig AI
Embed badge
[](https://verified-skill.com/skills/benmatselby/dotfiles/dependency-evaluator)