Detect what dev tooling infrastructure a project has and flag gaps across linters, formatters, pre-commit hooks, test runners, and CI/CD pipelines. Returns structured findings without applying changes. Use when the user asks to \"review tooling\", \"check project tooling\", \"what tooling is missing\", \"review dev infrastructure\", or \"tooling audit\".
npx skills add https://github.com/tobihagemann/turbo --skill review-tooling
Detect dev tooling infrastructure and flag gaps. Analysis only. Does not install or configure tools.
Tooling review always operates at the project level since config files live at the project root. Scope parameters (diff commands, file lists) are accepted but ignored.
When called standalone, use the git repository root as the project root (fall back to the current working directory if not in a git repo).
Search for config files in the project root and subdirectories (exclude vendored directories like node_modules/, vendor/, .build/). Classify findings into five categories:
| Config file pattern | Tool |
|---|---|
| .eslintrc*, eslint.config.* | ESLint |
| biome.json, biome.jsonc | Biome (linter + formatter) |
| deno.json, deno.jsonc with lint config | Deno lint |
| .swiftlint.yml | SwiftLint |
| ruff.toml, [tool.ruff] in pyproject.toml | Ruff |
| .pylintrc, pylintrc | Pylint |
| .flake8, [flake8] in setup.cfg | Flake8 |
| .rubocop.yml | RuboCop |
| .golangci.yml, .golangci.yaml | golangci-lint |
| clippy.toml, .clippy.toml | Clippy |
| ktlint*, .editorconfig with ktlint | ktlint |
| Config file pattern | Tool |
|---|---|
| .prettierrc*, prettier.config.* | Prettier |
| biome.json, biome.jsonc | Biome (linter + formatter) |
| deno.json, deno.jsonc with fmt config | Deno fmt |
| .swift-format, .swiftformat | swift-format / SwiftFormat |
| [tool.black] in pyproject.toml, pyproject.toml with [tool.ruff.format] | Black / Ruff formatter |
| rustfmt.toml, .rustfmt.toml | rustfmt |
| gofmt / goimports (check CI config or Makefile for usage) | gofmt |
| .clang-format | ClangFormat |
| Config file pattern | Tool |
|---|---|
| .husky/ directory | Husky |
| .lintstagedrc*, lint-staged key in package.json | lint-staged |
| .pre-commit-config.yaml | pre-commit framework |
| .git/hooks/pre-commit (non-sample) | Custom git hook |
| .lefthook.yml, lefthook.yml | Lefthook |
| Config file pattern | Tool |
|---|---|
| jest.config.*, jest key in package.json | Jest |
| vitest.config.* | Vitest |
| pytest.ini, [tool.pytest] in pyproject.toml, conftest.py | pytest |
| Package.swift with test targets, *Tests/ directories | Swift Testing / XCTest |
| _test.go files | Go testing |
| Cargo.toml with [dev-dependencies], tests/ directory | Rust tests |
| .rspec, spec/ directory | RSpec |
| phpunit.xml* | PHPUnit |
| Config file pattern | Tool |
|---|---|
| .github/workflows/*.yml | GitHub Actions |
| .gitlab-ci.yml | GitLab CI |
| Jenkinsfile | Jenkins |
| .circleci/config.yml | CircleCI |
| bitbucket-pipelines.yml | Bitbucket Pipelines |
| .travis.yml | Travis CI |
| azure-pipelines.yml | Azure Pipelines |
These tables are not exhaustive. If the project uses a tool not listed here, detect it by recognizing its config files.
Determine the primary language(s) and ecosystem from config files and source code. This informs which tooling gaps are relevant. A Go project without Prettier is not a gap. A Node.js project without a linter is.
| Signal | Ecosystem |
|---|---|
| package.json | Node.js / JavaScript / TypeScript |
| Package.swift, *.xcodeproj | Swift / Apple |
| pyproject.toml, setup.py, requirements.txt | Python |
| go.mod | Go |
| Cargo.toml | Rust |
| Gemfile | Ruby |
| pom.xml, build.gradle* | Java / Kotlin |
| deno.json, deno.jsonc | Deno |
For each category, assess whether the project has adequate tooling for its ecosystem:
When assessing pre-commit hooks, also check whether detected linters and formatters are wired into the hooks. A project with ESLint and Prettier but no pre-commit hook means formatting issues can slip into commits.
When assessing CI/CD, check whether the pipeline runs tests and linters. A CI config that only builds but never tests is a gap.
Format each finding as:
### [P<N>] <title (imperative, <=80 chars)>
**Category:** <Linters | Formatters | Pre-commit Hooks | Test Runners | CI/CD>
<one paragraph: what is missing or misconfigured and why it matters for this project>
After all findings, add:
## Tooling Summary
| Category | Status | Tool(s) |
|---|---|---|
| Linters | <Present/Partial/Missing> | <detected tools or "—"> |
| Formatters | <Present/Partial/Missing> | <detected tools or "—"> |
| Pre-commit Hooks | <Present/Partial/Missing> | <detected tools or "—"> |
| Test Runners | <Present/Partial/Missing> | <detected tools or "—"> |
| CI/CD | <Present/Partial/Missing> | <detected tools or "—"> |
## Overall Verdict
**Tooling:** <well-equipped | gaps found>
<1-3 sentence summary>
If all categories are adequately covered, report that and highlight what the project does well.
Assess Kubernetes workloads and cluster configuration for AKS Automatic compatibility. Identifies incompatibilities, generates fixes, and guides migration from AKS Standard to AKS Automatic. WHEN: migrate to AKS Automatic, check AKS Automatic readiness, validate manifests for Automatic, assess cluster for Automatic compatibility, fix deployment for Automatic compatibility, identify AKS Automatic migration blockers, is my cluster ready for AKS Automatic.
Discovers available Azure OpenAI model capacity across regions and projects. Analyzes quota limits, compares availability, and recommends optimal deployment locations based on capacity requirements. USE FOR: find capacity, check quota, where can I deploy, capacity discovery, best region for capacity, multi-project capacity search, quota analysis, model availability, region comparison, check TPM availability. DO NOT USE FOR: actual deployment (hand off to preset or customize after discovery), quota increase requests (direct user to Azure Portal), listing existing deployments.
Interactive guided deployment flow for Azure OpenAI models with full customization control. Step-by-step selection of model version, SKU (GlobalStandard/Standard/ProvisionedManaged), capacity, RAI policy (content filter), and advanced options (dynamic quota, priority processing, spillover). USE FOR: custom deployment, customize model deployment, choose version, select SKU, set capacity, configure content filter, RAI policy, deployment options, detailed deployment, advanced deployment, PTU deployment, provisioned throughput. DO NOT USE FOR: quick deployment to optimal region (use preset).
Unified Azure OpenAI model deployment skill with intelligent intent-based routing. Handles quick preset deployments, fully customized deployments (version/SKU/capacity/RAI policy), and capacity discovery across regions and projects. USE FOR: deploy model, deploy gpt, create deployment, model deployment, deploy openai model, set up model, provision model, find capacity, check model availability, where can I deploy, best region for model, capacity analysis. DO NOT USE FOR: listing existing deployments (use foundry_models_deployments_list MCP tool), deleting deployments, agent creation (use agent/create), project creation (use project/create).
Intelligently deploys Azure OpenAI models to optimal regions by analyzing capacity across all available regions. Automatically checks current region first and shows alternatives if needed. USE FOR: quick deployment, optimal region, best region, automatic region selection, fast setup, multi-region capacity check, high availability deployment, deploy to best location. DO NOT USE FOR: custom SKU selection (use customize), specific version selection (use customize), custom capacity configuration (use customize), PTU deployments (use customize).
This skill should be used when working with LaminDB, an open-source data framework for biology that makes data queryable, traceable, reproducible, and FAIR. Use when managing biological datasets (scRNA-seq, spatial, flow cytometry, etc.), tracking computational workflows, curating and validating data with biological ontologies, building data lakehouses, or ensuring data lineage and reproducibility in biological research. Covers data management, annotation, ontologies (genes, cell types, diseases, tissues), schema validation, integrations with workflow managers (Nextflow, Snakemake) and MLOps platforms (W&B, MLflow), and deployment strategies.
Latch platform for bioinformatics workflows. Build pipelines with Latch SDK, @workflow/@task decorators, deploy serverless workflows, LatchFile/LatchDir, Nextflow/Snakemake integration.
Run Python code in the cloud with serverless containers, GPUs, and autoscaling. Use when deploying ML models, running batch processing jobs, scheduling compute-intensive tasks, or serving APIs that require GPU acceleration or dynamic scaling.
Take tobihagemann/review-tooling from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.