Install any skill in seconds. Free to start, no credit card required.
Get Started Free →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".
.claude/skills/tobihagemann-review-tooling/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-04 | ✗→✓ | ▲ Improved | 17% | 0% |
| case-06 | ✗→✓ | ▲ Improved | 42% | 0% |
| case-08 | ✗→✓ | ▲ Improved | 47% | 0% |
| case-17 | ✗→✓ | ▲ Improved | 55% | 0% |
| case-18 | ✗→✓ | ▲ Improved | 9% | 0% |
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.
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-04 | fail→pass | 14,978 | 8,147 | -46% | 1 | 1 | 0% | 2,524 | 2,952 | +17% | 0 | 0 | — |
case-01 | fail→fail | 16,996 | 3,451 | -80% | 1 | 1 | 0% | 2,902 | 1,878 | -35% | 0 | 0 | — |
case-02 | fail→fail | 15,896 | 2,411 | -85% | 1 | 1 | 0% | 2,578 | 1,790 | -31% | 0 | 0 | — |
case-03 | fail→fail | 19,695 | 3,427 | -83% | 1 | 1 | 0% | 3,149 | 1,852 | -41% | 0 | 0 | — |
case-05 | pass→pass | 9,953 | 4,123 | -59% | 1 | 1 | 0% | 1,568 | 2,352 | +50% | 0 | 0 | — |
case-06 | fail→pass | 11,803 | 5,811 | -51% | 1 | 1 | 0% | 1,907 | 2,711 | +42% | 0 | 0 | — |
case-07 | pass→pass | 10,766 | 3,959 | -63% | 1 | 1 | 0% | 1,676 | 2,301 | +37% | 0 | 0 | — |
case-08 | fail→pass | 10,594 | 4,353 | -59% | 1 | 1 | 0% | 1,691 | 2,484 | +47% | 0 | 0 | — |
case-09 | pass→pass | 9,374 | 9,585 | +2% | 1 | 1 | 0% | 1,633 | 3,343 | +105% | 0 | 0 | — |
case-10 | pass→pass | 12,296 | 6,644 | -46% | 1 | 1 | 0% | 1,871 | 2,696 | +44% | 0 | 0 | — |
case-11 | fail→fail | 8,543 | 7,443 | -13% | 1 | 1 | 0% | 1,366 | 1,946 | +42% | 0 | 0 | — |
case-12 | pass→pass | 12,586 | 1,679 | -87% | 1 | 1 | 0% | 2,280 | 1,871 | -18% | 0 | 0 | — |
case-13 | pass→pass | 10,618 | 4,133 | -61% | 1 | 1 | 0% | 1,765 | 2,311 | +31% | 0 | 0 | — |
case-14 | pass→pass | 8,847 | 5,604 | -37% | 1 | 1 | 0% | 1,462 | 2,639 | +81% | 0 | 0 | — |
case-15 | pass→pass | 10,278 | 4,722 | -54% | 1 | 1 | 0% | 1,585 | 2,375 | +50% | 0 | 0 | — |
case-16 | pass→pass | 8,606 | 3,586 | -58% | 1 | 1 | 0% | 1,352 | 2,363 | +75% | 0 | 0 | — |
case-17 | fail→pass | 9,056 | 3,192 | -65% | 1 | 1 | 0% | 1,356 | 2,102 | +55% | 0 | 0 | — |
case-18 | fail→pass | 10,354 | 1,726 | -83% | 1 | 1 | 0% | 1,705 | 1,858 | +9% | 0 | 0 | — |
case-19 | pass→pass | 7,705 | 4,081 | -47% | 1 | 1 | 0% | 1,318 | 2,366 | +80% | 0 | 0 | — |
case-20 | pass→pass | 7,811 | 3,563 | -54% | 1 | 1 | 0% | 1,195 | 2,254 | +89% | 0 | 0 | — |
case-21 | pass→fail | 8,452 | 17,431 | +106% | 1 | 1 | 0% | 1,605 | 4,242 | +164% | 0 | 0 | — |
case-22 | pass→fail | 11,787 | 19,514 | +66% | 1 | 1 | 0% | 2,351 | 4,802 | +104% | 0 | 0 | — |
case-23 | pass→pass | 4,317 | 21,350 | +395% | 1 | 1 | 0% | 676 | 5,461 | +708% | 0 | 0 | — |
DecimalAI ran this skill against gemini-3.6-flash twice over the same eval suite — once with the skill loaded and once without — and compared the two runs case by case. 23 cases were attempted, and 19 counted toward the lift figure. The other 4 produced results that are not comparable between the two arms, so they are excluded from the headline rather than averaged into it. The headline lift of +13 percentage points is the difference between those two pass rates over the 19 comparable cases. 2 cases got worse with the skill loaded, and they are included in that figure.
Without the skill loaded, the model failed this case. With it loaded, the same prompt on the same model passed. This is one improved case from the latest verified run; every case, including any that regressed, is in the table above.
Other measured skills in the registry, with their headline benchmark lift.