Adopt Best-Practice Technologies

Scan the current project for missing best-practice areas and implement the top recommendation for each gap — or, in recommend mode, stop after producing recommendations without installing anything.

Modes and Invocation

$ARGUMENTS may contain a topic name, the word recommend, or both:

• (empty) — adopt every gap topic in the table below.
• <topic> — adopt that one topic only.
• recommend [<topic>] — recommend-only mode: perform the same detection and analysis, write the recommendation as a status: recommended project skill (see step 5R), and implement nothing. This replaces the old standalone do-recommend skill.

Do not prompt the user for which topics to process.

Topics

Topic	Best-Practice Skill
linting	mav-bp-linting
unit-testing	mav-bp-testing

Detection hints for each topic come from the shared registry — ${CLAUDE_PLUGIN_ROOT}/skills/do-upskill/topics.json (scanHints per topic). Do not maintain a separate detection list here.

Process

For each topic:

1. Check Whether the Practice Already Exists

Apply the topic's scanHints (dependencies, grep patterns, file globs) plus a check for CI steps that run the tool. If the practice is already implemented (configs exist, dependencies present, actively used), skip the topic. Print: <topic>: already implemented, skipping.

2. Identify the Project's Tech Stack

Determine the primary language(s) and framework(s) by reading:

• package.json (Node/JS/TS, look at framework deps like react, vue, express, etc.)
• pyproject.toml / requirements.txt (Python)
• go.mod (Go)
• Cargo.toml (Rust)
• build.gradle.kts / pom.xml (JVM)
• Source file extensions and directory structure

3. Read the Best-Practice Skill

Read the corresponding best-practice skill (${CLAUDE_PLUGIN_ROOT}/skills/<skill-name>/SKILL.md) to understand the recommended tools and configuration standards for the detected stack.

4. Check for an Existing Recommendation

If docs/maverick/skills/<topic>/SKILL.md exists with status: recommended frontmatter (written by do-upskill or a prior recommend-mode run), read it and use its recommended stack — do not re-analyse. There is exactly one recommendation artifact format: the project-skill file. Never write a separate docs/maverick/recommendations/ document.

5R. Recommend-Only Mode — Write the Recommendation and Stop

Pick the best-fit tool for the stack (1 option when there is a clear winner — e.g. Ruff for Python, ESLint for TypeScript; name up to 2 genuinely competitive alternatives and their trade-offs in the Patterns section). Write it as a status: recommended project skill at docs/maverick/skills/<topic>/SKILL.md, using do-upskill's mandatory output structure (Stack / Configuration / Patterns / File Locations, prose only, under 100 lines). Then stop — no installation, no commits.

5. Implement the Top Recommendation

Install and configure the recommended tool. Follow the best-practice skill's standards precisely.

For linting — implement all of:

1. Install packages — add the linter and formatter as dev dependencies using the project's package manager
2. Create config file — write the linter config at the project root using the recommended preset (e.g., eslint.config.js with flat config for TS/JS, ruff.toml for Python). Follow the error-only policy from the best-practice skill.
3. Create formatter config — if the formatter is a separate tool (e.g., Prettier for JS/TS), write its config. If the linter handles formatting (e.g., Ruff), configure it in the same file.
4. Add ignore patterns — exclude build output, generated code, and vendor directories
5. Add package scripts — add lint and format (or equivalent) scripts to the project's script runner (package.json scripts, Makefile, pyproject.toml scripts)
6. Verify — run the lint command and fix any initial errors in project source code. The goal is a clean lint pass, not a config that ignores existing issues.

For unit testing — implement all of:

1. Install packages — add the test framework and assertion library as dev dependencies
2. Create config file — write the test runner config at the project root with sensible defaults (e.g., vitest.config.ts for Vite projects, jest.config.js for other JS/TS, pyproject.toml [tool.pytest] for Python)
3. Create a sample test — write one example test file demonstrating the project's test conventions (file location, import pattern, describe/it structure). Place it alongside or mirroring an existing source file. The test should verify real behaviour, not be a trivial placeholder.
4. Add package scripts — add a test script (and test:coverage if the framework supports it)
5. Verify — run the test suite and confirm the sample test passes

6. Update the Project Skill

Rewrite docs/maverick/skills/<topic>/SKILL.md as a factual project skill describing what is now implemented (drop the status: recommended field), per do-upskill's output structure. This keeps the single artifact current.

7. Commit the Changes

Create a conventional commit for each topic adopted:

chore: adopt <tool-name> for <topic> (<topic>)

Follow the mav-git-workflow skill for commit conventions. Do not push — the user will review and push.

Rules

• Best-practice compliant — every config choice must follow the corresponding best-practice skill. Do not deviate from the standards (error-only policy, separate linter/formatter, etc.).
• Stack-specific — install and configure tools that match the project's actual language and framework
• Minimal and correct — write the minimum config needed for a clean, working setup. Do not add rules or plugins beyond the recommended preset.
• Verify before committing — run the tool after installation. If it fails, fix the issue. Do not commit a broken setup.
• Skip covered topics — if the practice already exists, do not overwrite or reconfigure it
• No branch creation — commit directly to the current branch. The user is expected to be on an appropriate branch.
• One commit per topic — separate commits make it easy to review or revert individual adoptions
• One recommendation format — the status: recommended project skill; never a parallel recommendations document

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