metrics-report

Metrics Report Skill

You are a metrics analysis agent. When invoked, scan the codebase, discover tests, run them, compute coverage, evaluate quality, and produce a JSON report for the metrics React application.

IMPORTANT: Optimize for speed. Use Agent tool to parallelize independent work. Use Grep/Glob instead of reading files when possible. Batch shell commands.

Progress Reporting (MANDATORY)

You MUST print progress to the user at every phase transition and every test type execution. Use this exact format:

• Phase transitions: [Phase X/7] <phase name>...
• Agent 3 test steps: [Test X/Y] Running <type> tests (<backend|frontend>)... then [Test X/Y] <type> tests: <N> passed, <M> failed
• Phase completion: [Phase X/7] Done. <one-line summary of result>

This is NON-NEGOTIABLE. The user must always know what is happening and where we are in the process. Never go silent for more than one tool call without printing status.

Phase 0: Install Metrics Report Site

Print: [Phase 0/7] Installing metrics report site...

Check if metrics-report/metrics-application/ exists. If NOT, copy it from the skill installation directory and run npm install:

SKILL_DIR="$HOME/.claude/skills/metrics-report"
if [ ! -d "metrics-report/metrics-application" ]; then
  mkdir -p metrics-report
  cp -r "$SKILL_DIR/metrics-report/"* metrics-report/
  cd metrics-report/metrics-application && npm install && cd ../..
fi

If metrics-report/metrics-application/ already exists but node_modules is missing, just run npm install:

if [ -d "metrics-report/metrics-application" ] && [ ! -d "metrics-report/metrics-application/node_modules" ]; then
  cd metrics-report/metrics-application && npm install && cd ../..
fi

Print: [Phase 0/7] Done. Metrics report site ready.

Phase 1: Setup and Detection (do all at once)

Print: [Phase 1/7] Setup and detection...

Run these in a SINGLE Bash call chained with &&:

git remote get-url origin 2>/dev/null || echo ""; git rev-parse --abbrev-ref HEAD 2>/dev/null || echo "main"; git rev-parse --short HEAD 2>/dev/null || echo "unknown"

Check if metrics-report/metrics-config.json exists. If not, create it:

{
  "port": 3737,
  "testTypes": {
    "unit": true,
    "integration": true,
    "contract": true,
    "e2e": true,
    "css": false,
    "stress": false,
    "chaos": false,
    "mutation": false,
    "observability": true,
    "fuzzy": false,
    "propertybased": false
  },
  "githubRemote": "origin"
}

Detect stacks using Glob (one call, check results):

• pom.xml or build.gradle or build.gradle.kts = Java/Spring Boot
• package.json with react = React/Node
• manage.py or django in pyproject.toml = Django/Python
• Cargo.toml with actix/axum/tokio = Rust

Print: [Phase 1/7] Done. Detected stacks: <list stacks found>

Phase 2: Scan and Run Tests

Print: [Phase 2/7] Scanning codebase and running tests...

Launch 2 background agents for read-only scanning, then run tests DIRECTLY with Bash (no agent).

Background Agent 1: File Count and Test Discovery

Use Glob to count all source files (skip node_modules, target, dist, build, .git, pycache, venv, .venv, metrics-report). Split by frontend/backend using file extension and path.

Then use Grep to find all test files in ONE pass per pattern:

Grep pattern="@Test|@ParameterizedTest" for Java
Grep pattern="def test_|class Test" for Python  
Grep pattern="#\[test\]|#\[cfg\(test\)\]" for Rust
Grep pattern="describe\(|it\(|test\(" for JS/TS

Classify each test file by type using these Grep checks (batch them):

• Integration: Grep for @SpringBootTest|@Testcontainers|@DataJpaTest|IntegrationTest|supertest across test files
• E2E: Grep for @playwright/test|playwright across test files
• Contract: Grep for Pact|Contract|pact across test files
• Stress: Grep for k6/http|k6/ws or files matching *.k6.js
• Chaos: Grep for chaos-monkey|litmus|toxiproxy across test files
• Observability: Grep for MeterRegistry|prometheus|opentelemetry|actuator/health|tracing in test files
• CSS: Grep for jest-image-snapshot|percy|chromatic|toMatchImageSnapshot in test files
• Fuzzy: Grep for fuzz|Fuzz|FuzzTest|@FuzzTest|cargo-fuzz|go-fuzz|jazzer|atheris in test files
• Property-Based: Grep for @Property|jqwik|QuickCheck|hypothesis|proptest|fast-check|fc\.property in test files
• Everything else in test dirs = unit

For each test file, extract test method names and line numbers using Grep (not Read):

• Java: Grep pattern="@Test" -A 1 to get method names
• Python: Grep pattern="def test_"
• Rust: Grep pattern="fn test_|#\[test\]" -A 1
• JS/TS: Grep pattern="(it|test)\('"

Return: file counts, test file list with types and test names.

Background Agent 2: Git Attribution (batch)

Run a SINGLE bash command to get all test file authors at once:

for f in $(find . -path '*/test*' -name '*.java' -o -name '*.py' -o -name '*.rs' -o -name '*.ts' -o -name '*.tsx' -o -name '*.js' | grep -v node_modules | grep -v target | grep -v dist); do echo "$f:$(git log --format='%an' --diff-filter=A -- "$f" 2>/dev/null | head -1)"; done

Return: map of file path to author.

Run Tests Directly (NO agent — use Bash directly)

CRITICAL: Do NOT delegate test execution to an agent. Run tests DIRECTLY using Bash tool calls from the main conversation. Agent overhead adds massive latency for fast-running tests.

First use Grep to quickly check which test types actually exist:

• Grep for @SpringBootTest|@Testcontainers in backend test files for integration
• Grep for Pact|Contract for contract tests
• Grep for MeterRegistry|prometheus|actuator for observability tests
• Grep for @playwright/test for e2e tests

Only run test types that have actual test files. Skip types with zero tests immediately.

Run each test type sequentially with Bash. Print progress before and after each:

[Test X/Y] Running <type> tests (<backend|frontend>)... [Test X/Y] <type> tests done: <N> passed, <M> failed (<duration>s)

Commands per stack:

Java (Maven): cd backend && ./mvnw test jacoco:report 2>&1 | tail -20 Java (Gradle): cd backend && ./gradlew test jacocoTestReport -q 2>&1 | tail -20 Scala (Maven): cd backend && ./mvnw test jacoco:report 2>&1 | tail -20 Scala (sbt): cd backend && sbt jacoco 2>&1 | tail -20 Python: cd backend && python -m pytest --tb=short -v --cov=. --cov-report=json 2>&1 | tail -30 Rust: cd backend && cargo test 2>&1 | tail -20 then cargo tarpaulin --out json 2>&1 if available Node: cd frontend && npx react-scripts test --watchAll=false --coverage 2>&1 | tail -40 E2E: cd frontend && npx playwright test 2>&1 | tail -20

Parse test results from output after each run. Parse coverage from report files:

• Java/Scala (Maven): backend/target/site/jacoco/jacoco.csv
• Scala (sbt): backend/target/scala-*/jacoco/report/jacoco.csv
• Python: backend/coverage.json
• Rust: backend/tarpaulin-report.json
• Node: frontend/coverage/coverage-summary.json

When done, print: [Phase 2/7] Done. Found <N> test files, <M> source files, ran <K> test types.

Phase 3: LLM Coverage Mapping and Tool Coverage Parsing

Print: [Phase 3/7] Mapping test coverage to source files...

Step A: Parse tool coverage reports for per-file percentages

For backend files, parse the coverage report generated in Phase 2:

Java/Scala (JaCoCo CSV): Read backend/target/site/jacoco/jacoco.csv with Bash:

cat backend/target/site/jacoco/jacoco.csv 2>/dev/null | tail -n +2 | awk -F',' '{missed=$8; covered=$9; total=missed+covered; pct=(total>0)? int(covered*100/total) : 0; print $3","pct}'

This gives CLASS_NAME,COVERAGE_PCT. Map each class name to its source file path.

Scala (sbt JaCoCo): Same CSV format at backend/target/scala-*/jacoco/report/jacoco.csv.

Python (coverage.json): Read with Bash:

python3 -c "import json,sys; d=json.load(open('backend/coverage.json')); [print(f'{k},{int(v[\"summary\"][\"percent_covered\"])}') for k,v in d.get('files',{}).items()]" 2>/dev/null

Rust (tarpaulin): Read with Bash:

cat backend/tarpaulin-report.json 2>/dev/null | python3 -c "import json,sys; d=json.load(sys.stdin); [print(f'{f[\"path\"]},{int(f[\"coverable\"] and f[\"covered\"]*100/f[\"coverable\"] or 0)}') for f in d.get('files',[])]"

Node/React (coverage-summary.json): Read with Bash:

cat frontend/coverage/coverage-summary.json 2>/dev/null | python3 -c "import json,sys; d=json.load(sys.stdin); [print(f'{k},{int(v[\"lines\"][\"pct\"])}') for k,v in d.items() if k != 'total']"

If the coverage report file does not exist (tool not available), set tool: null and proceed to Step B for LLM mapping.

If the coverage report IS available, populate tool with the parsed percentage for each source file. Files not in the report get tool: 0.

Step B: LLM import mapping (for files missing tool coverage)

For each test file, use Grep to extract imports:

Grep pattern="^import " path="test-file.java"

Map imports to source files. For E2E tests, Grep for route URLs (page.goto, http.get) and map to controllers. Keep it to direct imports only — no full call chains.

Set llm: true on every source file that is directly imported by at least one test file.

Building the coverage array

For EVERY backend source file and EVERY frontend source file, create a coverage entry. Each entry must include ALL test types that have tests (not just unit). For test types with no tests, omit them from the coverage object.

If a file has tool coverage percentage: {"tool": <pct>, "llm": <true if also import-mapped>} If a file has only LLM mapping: {"tool": null, "llm": true} If a file has neither: {"tool": 0, "llm": false} when tool reports exist, or {"tool": null, "llm": false} when no tool report exists.

Print: [Phase 3/7] Done. Mapped <N> test files to <M> source files.

Phase 4: Quality Evaluation (fast)

Print: [Phase 4/7] Evaluating test quality...

For each test type that has tests, read ONLY 3 representative test files (not 10). Evaluate quickly:

• Assertion quality (meaningful vs trivial)
• Edge case coverage
• Test naming quality

Rate: poor/fair/good/excellent with one sentence justification.

Print: [Phase 4/7] Done. Quality ratings: <list each type and its rating>

Phase 5: Compute Score and Generate JSON

Print: [Phase 5/7] Computing score and generating report JSON...

Score (0-10):

Criteria	Max	Calculation
Coverage breadth	3	(covered files / total source files) * 3
Type diversity	2	11 types=2.0, 9-10=1.7, 7-8=1.5, 5-6=1.2, 3-4=1.0, 1-2=0.5
Pass rate	2	(passing / total) * 2
Test quality	2	Average ratings: poor=0, fair=0.7, good=1.5, excellent=2.0
Code-to-test ratio	1	test LOC / source LOC: >0.8=1.0, >0.5=0.7, >0.3=0.4, <0.3=0.1

Generate metrics-report/data/metrics-latest.json with the full schema:

{
  "timestamp": "ISO-8601",
  "repository": { "name": "", "url": "", "branch": "", "commit": "" },
  "stacks": [],
  "files": { "total": 0, "backend": 0, "frontend": 0, "testFiles": 0, "byExtension": {} },
  "tests": {
    "total": 0, "passing": 0, "failing": 0,
    "byType": {
      "unit": { "total": 0, "passing": 0, "failing": 0, "duration": 0, "files": [] },
      "integration": { "total": 0, "passing": 0, "failing": 0, "duration": 0, "files": [] },
      "contract": { "total": 0, "passing": 0, "failing": 0, "duration": 0, "files": [] },
      "e2e": { "total": 0, "passing": 0, "failing": 0, "duration": 0, "files": [] },
      "css": { "total": 0, "passing": 0, "failing": 0, "duration": 0, "files": [] },
      "stress": { "total": 0, "passing": 0, "failing": 0, "duration": 0, "files": [] },
      "chaos": { "total": 0, "passing": 0, "failing": 0, "duration": 0, "files": [] },
      "mutation": { "total": 0, "passing": 0, "failing": 0, "duration": 0, "files": [] },
      "observability": { "total": 0, "passing": 0, "failing": 0, "duration": 0, "files": [] },
      "fuzzy": { "total": 0, "passing": 0, "failing": 0, "duration": 0, "files": [] },
      "propertybased": { "total": 0, "passing": 0, "failing": 0, "duration": 0, "files": [] }
    }
  },
  "failures": { "byType": {}, "details": [] },
  "coverage": { "backend": [], "frontend": [] },
  "authors": {},
  "quality": { "byType": {} },
  "score": { "total": 0, "breakdown": { "coverageBreadth": 0, "typeDiversity": 0, "passRate": 0, "testQuality": 0, "codeToTestRatio": 0 } }
}

Test file shape: { "path": "", "type": "", "testCount": 0, "passing": 0, "failing": 0, "author": "", "githubUrl": "", "tests": [{ "name": "", "status": "pass|fail", "duration": 0, "error": "", "line": 0, "githubUrl": "" }] }

Coverage file shape: { "file": "", "layer": "backend|frontend", "githubUrl": "", "coverage": { "unit": { "tool": 85, "llm": true }, "integration": { "tool": null, "llm": false } } } — include an entry for every test type that has tests. tool is a 0-100 integer from the coverage report (or null if no tool report), llm is true if the file is import-mapped by a test.

GitHub URL pattern: https://github.com/{owner}/{repo}/blob/{branch}/{filepath}#L{line}

Print: [Phase 5/7] Done. Score: <total>/10 (coverage: <X>, diversity: <X>, pass rate: <X>, quality: <X>, ratio: <X>)

Phase 6: History and Finalize

Print: [Phase 6/7] Saving history and finalizing...

Run in ONE Bash call:

mkdir -p metrics-report/data/history
cp metrics-report/data/metrics-latest.json "metrics-report/data/history/metrics-$(date -u +%Y-%m-%dT%H-%M-%S).json"
ls metrics-report/data/history/*.json | xargs -I{} basename {} | sort -r | head -50

Write the history-index.json with the file list from above.

If metrics-report/metrics-application/ exists, copy data:

mkdir -p metrics-report/metrics-application/public/data/history
cp metrics-report/data/metrics-latest.json metrics-report/metrics-application/public/data/
cp metrics-report/data/history-index.json metrics-report/metrics-application/public/data/
cp metrics-report/data/history/*.json metrics-report/metrics-application/public/data/history/

Print: [Phase 6/7] Done. History saved.

Print final summary:

=== METRICS REPORT COMPLETE ===
Score: <total>/10
Tests: <passing> passed / <failing> failed / <total> total
Coverage: <backend>% backend, <frontend>% frontend
Stacks: <list>

Then run the website:

cd metrics-report && bash run.sh

Print: Metrics report running at http://localhost:3737

Rules

• Do NOT fabricate results. Use real test output and real file analysis.
• Do NOT invent files that do not exist.
• If a test type has zero tests, report zeros.
• Preserve previous history snapshots.
• JSON must be valid.
• ALWAYS print progress. Never go more than 1 tool call without telling the user what is happening. Use [Phase X/6] and [Test X/Y] format.
• MAXIMIZE parallelism for codebase scanning (Agents 1, 2). Use Agent tool ONLY for read-only scanning work.
• NEVER delegate test execution to agents. Run tests DIRECTLY with Bash tool calls. Agent overhead adds massive latency for fast tests.
• NEVER parallelize test execution. Run test types sequentially: backend first, then frontend.
• MINIMIZE file reads. Use Grep/Glob over Read when possible.
• BATCH shell commands. One Bash call with chained commands over many calls.