Claude-skill-registry code-health

Analyze codebase health - large files, test coverage gaps, duplicate code, dead/legacy code, and documentation issues. Use when asked to scan, audit, or assess code quality, find refactoring targets, or identify technical debt.

install

source · Clone the upstream repo

git clone https://github.com/majiayu000/claude-skill-registry

Claude Code · Install into ~/.claude/skills/

T=$(mktemp -d) && git clone --depth=1 https://github.com/majiayu000/claude-skill-registry "$T" && mkdir -p ~/.claude/skills && cp -r "$T/skills/data/code-health" ~/.claude/skills/majiayu000-claude-skill-registry-code-health && rm -rf "$T"

manifest: skills/data/code-health/SKILL.md

Code Health Analysis

Run

scripts/health.py

to analyze codebase health. The script auto-detects project type (Go, Python, JS/TS) and runs appropriate checks.

Usage

Primary (LLM/programmatic use):

# Full scan with JSON output (recommended for LLM consumption)
python /path/to/skill/scripts/health.py --json [directory]

# Specific checks with JSON output
python /path/to/skill/scripts/health.py --check size --json [directory]
python /path/to/skill/scripts/health.py --check tests --json [directory]
python /path/to/skill/scripts/health.py --check dupes --json [directory]
python /path/to/skill/scripts/health.py --check dead --json [directory]
python /path/to/skill/scripts/health.py --check docs --json [directory]

Secondary (human debugging):

# Human-readable output with emojis (for manual inspection)
python /path/to/skill/scripts/health.py [directory]
python /path/to/skill/scripts/health.py --check size [directory]

Checks

Check	Description
`size`	Large files, function counts, git churn
`tests`	Coverage gaps, missing test files, test quality
`dupes`	Duplicate function names, similar patterns
`dead`	Legacy markers, unused exports, stale code
`docs`	Undocumented exports, missing READMEs

Output

JSON format (recommended): Structured output for LLM/programmatic parsing with severity levels:

```
"critical"
```
: Immediate attention needed
```
"warning"
```
: Should address soon
```
"info"
```
: Nice to fix

Each finding includes file path, line number (when applicable), message, and suggested action.

Human-readable format (debugging only): Pretty-printed output with emoji severity indicators (🔴 critical, 🟡 warning, 🟢 info) for manual inspection.

AST-Based Function Analysis

For more accurate function analysis, use the included AST parsers:

gofuncs - Go Function Analyzer

go run scripts/gofuncs.go -dir /path/to/project

Output format:

file:line:type:exported:name:receiver:signature

```
type
```
:
```
f
```
=function,
```
m
```
=method
```
exported
```
:
```
y
```
=public,
```
n
```
=private

Example:

api.go:15:f:n:fetchItems:()[]Item
config.go:144:m:y:GetCategory:*Mapper:(string)string

pyfuncs - Python Function Analyzer

python scripts/pyfuncs.py --dir /path/to/project

Output format:

file:line:type:exported:name:class:signature:decorators

```
type
```
:
```
f
```
=function,
```
m
```
=method,
```
s
```
=staticmethod,
```
c
```
=classmethod,
```
p
```
=property
```
exported
```
:
```
y
```
=public,
```
n
```
=private (underscore prefix)

Example:

main.py:15:f:y:process_data::(data:List[str])->Dict[str,int]:
api.py:45:m:y:fetch:APIClient:async (url:str)->Response:cache,retry

jsfuncs - JavaScript/TypeScript Function Analyzer

node scripts/jsfuncs.js --dir /path/to/project

Output format:

file:line:type:exported:name:class:signature:decorators

```
type
```
:
```
f
```
=function,
```
m
```
=method,
```
a
```
=arrow,
```
c
```
=constructor,
```
g
```
=getter,
```
s
```
=setter
```
exported
```
:
```
y
```
=public,
```
n
```
=private

Example:

main.js:15:f:y:processData::(data:string[])=>Promise<Object>:
api.ts:45:m:y:fetch:APIClient:async (url:string)=>Response:

Use Cases for AST Tools

These tools provide accurate function-level analysis for:

Complexity analysis: Count functions per file, analyze parameter complexity
Test coverage: Identify which specific functions lack tests
Duplicate detection: Find similar function signatures across the codebase
API surface analysis: List all public vs private functions
Documentation gaps: Cross-reference with doc comments

Combine with

health.py

for comprehensive codebase health analysis.

Requirements

Required:

python3

Optional (for better results):

rg

(ripgrep),

fd

git

go

(for Go projects),

staticcheck

node

(for JS/TS projects)

Missing tools are reported but don't block execution.