EasyPlatform lint

[Code Quality] Run linters and fix issues for backend or frontend

install

source · Clone the upstream repo

git clone https://github.com/duc01226/EasyPlatform

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

T=$(mktemp -d) && git clone --depth=1 https://github.com/duc01226/EasyPlatform "$T" && mkdir -p ~/.claude/skills && cp -r "$T/.claude/skills/lint" ~/.claude/skills/duc01226-easyplatform-lint && rm -rf "$T"

manifest: .claude/skills/lint/SKILL.md

source content

[IMPORTANT] Use
TaskCreate
to break ALL work into small tasks BEFORE starting — including tasks for each file read. This prevents context loss from long files. For simple tasks, AI MUST ATTENTION ask user whether to skip.

Critical Thinking Mindset — Apply critical thinking, sequential thinking. Every claim needs traced proof, confidence >80% to act. Anti-hallucination: Never present guess as fact — cite sources for every claim, admit uncertainty freely, self-check output for errors, cross-reference independently, stay skeptical of own confidence — certainty without evidence root of all hallucination.

AI Mistake Prevention — Failure modes to avoid on every task:

Check downstream references before deleting. Deleting components causes documentation and code staleness cascades. Map all referencing files before removal.

Verify AI-generated content against actual code. AI hallucinates APIs, class names, and method signatures. Always grep to confirm existence before documenting or referencing.

Trace full dependency chain after edits. Changing a definition misses downstream variables and consumers derived from it. Always trace the full chain.

Trace ALL code paths when verifying correctness. Confirming code exists is not confirming it executes. Always trace early exits, error branches, and conditional skips — not just happy path.

When debugging, ask "whose responsibility?" before fixing. Trace whether bug is in caller (wrong data) or callee (wrong handling). Fix at responsible layer — never patch symptom site.

Assume existing values are intentional — ask WHY before changing. Before changing any constant, limit, flag, or pattern: read comments, check git blame, examine surrounding code.

Verify ALL affected outputs, not just the first. Changes touching multiple stacks require verifying EVERY output. One green check is not all green checks.

Holistic-first debugging — resist nearest-attention trap. When investigating any failure, list EVERY precondition first (config, env vars, DB names, endpoints, DI registrations, data preconditions), then verify each against evidence before forming any code-layer hypothesis.

Surgical changes — apply the diff test. Bug fix: every changed line must trace directly to the bug. Don't restyle or improve adjacent code. Enhancement task: implement improvements AND announce them explicitly.

Surface ambiguity before coding — don't pick silently. If request has multiple interpretations, present each with effort estimate and ask. Never assume all-records, file-based, or more complex path.

Prerequisites: MUST ATTENTION READ before executing:

Understand Code First — HARD-GATE: Do NOT write, plan, or fix until you READ existing code.
Search 3+ similar patterns (
grep
/
glob
) — cite
file:line
evidence
Read existing files in target area — understand structure, base classes, conventions
Run
python .claude/scripts/code_graph trace <file> --direction both --json
when
.code-graph/graph.db
exists
Map dependencies via
connections
or
callers_of
— know what depends on your target
Write investigation to
.ai/workspace/analysis/
for non-trivial tasks (3+ files)
Re-read analysis file before implementing — never work from memory alone

NEVER invent new patterns when existing ones work — match exactly or document deviation
BLOCKED until:
- [ ]
Read target files
- [ ]
Grep 3+ patterns
- [ ]
Graph trace (if graph.db exists)
- [ ]
Assumptions verified with evidence

Quick Summary

Goal: Run linters (.NET analyzers and/or ESLint/Prettier) and report or auto-fix code quality issues.

Workflow:

Parse — Determine scope from arguments: backend, frontend, or both; fix mode or report-only
Execute — Run
```
dotnet build
```
for .NET analyzers or
```
nx lint
```
/
```
prettier
```
for Angular
Report — Group issues by severity (error/warning/info) with file paths and line numbers

Key Rules:

No argument = run both backend + frontend in report-only mode
```
fix
```
argument = apply safe auto-fixes, report remaining manual items
Always show file paths and line numbers in output

Be skeptical. Apply critical thinking, sequential thinking. Every claim needs traced proof, confidence percentages (Idea should be more than 80%).

Run linting: $ARGUMENTS

Instructions

Parse arguments:
- ```
backend
```
  or
```
be
```
  → Run .NET analyzers
- ```
frontend
```
  or
```
fe
```
  → Run ESLint/Prettier
- ```
fix
```
  → Auto-fix issues where possible
- No argument → Run both, report only

For Backend (.NET):

dotnet build {SolutionName}.sln /p:TreatWarningsAsErrors=false

Check for analyzer warnings (CA*, IDE*, etc.)
Report code style violations

For Frontend (Angular/Nx):

cd src/{ExampleAppWeb}
nx lint playground-text-snippet
nx lint {lib-name}

With auto-fix:

nx lint playground-text-snippet --fix
npx prettier --write "apps/**/*.{ts,html,scss}" "libs/**/*.{ts,html,scss}"

Report format:
- Group issues by severity (error, warning, info)
- Show file paths and line numbers
- Suggest fixes for common issues
Auto-fix behavior:
- If
```
fix
```
  argument provided, apply safe auto-fixes
- Report what was fixed vs what needs manual attention

Closing Reminders

IMPORTANT MUST ATTENTION break work into small todo tasks using
```
TaskCreate
```
BEFORE starting
IMPORTANT MUST ATTENTION search codebase for 3+ similar patterns before creating new code
IMPORTANT MUST ATTENTION cite
```
file:line
```
evidence for every claim (confidence >80% to act)
IMPORTANT MUST ATTENTION add a final review todo task to verify work quality
IMPORTANT MUST ATTENTION search 3+ existing patterns and read code BEFORE any modification. Run graph trace when graph.db exists.
MUST ATTENTION apply critical thinking — every claim needs traced proof, confidence >80% to act. Anti-hallucination: never present guess as fact.
MUST ATTENTION apply AI mistake prevention — holistic-first debugging, fix at responsible layer, surface ambiguity before coding, re-read files after compaction.