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plankton-code-quality

Write-time code quality enforcement using Plankton — auto-formatting, linting, and Claude-powered fixes on every file edit via hooks. Use when setting up write-time formatting, linting, or auto-fix hooks on file edits.

Was ist plankton-code-quality?

plankton-code-quality is a Claude Code agent skill that write-time code quality enforcement using Plankton — auto-formatting, linting, and Claude-powered fixes on every file edit via hooks. Use when setting up write-time formatting, linting, or auto-fix hooks on file edits.

Funktioniert mitClaude Code~Codex CLI~Cursor
npx skills add https://github.com/affaan-m/ECC/tree/main/skills/plankton-code-quality

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Dokumentation

Plankton Code Quality Skill

Integration reference for Plankton (credit: @alxfazio), a write-time code quality enforcement system for Claude Code. Plankton runs formatters and linters on every file edit via PostToolUse hooks, then spawns Claude subprocesses to fix violations the agent didn't catch.

When to Use

  • You want automatic formatting and linting on every file edit (not just at commit time)
  • You need defense against agents modifying linter configs to pass instead of fixing code
  • You want tiered model routing for fixes (Haiku for simple style, Sonnet for logic, Opus for types)
  • You work with multiple languages (Python, TypeScript, Shell, YAML, JSON, TOML, Markdown, Dockerfile)

How It Works

Three-Phase Architecture

Every time Claude Code edits or writes a file, Plankton's multi_linter.sh PostToolUse hook runs:

Phase 1: Auto-Format (Silent)
├─ Runs formatters (ruff format, biome, shfmt, taplo, markdownlint)
├─ Fixes 40-50% of issues silently
└─ No output to main agent

Phase 2: Collect Violations (JSON)
├─ Runs linters and collects unfixable violations
├─ Returns structured JSON: {line, column, code, message, linter}
└─ Still no output to main agent

Phase 3: Delegate + Verify
├─ Spawns claude -p subprocess with violations JSON
├─ Routes to model tier based on violation complexity:
│   ├─ Haiku: formatting, imports, style (E/W/F codes) — 120s timeout
│   ├─ Sonnet: complexity, refactoring (C901, PLR codes) — 300s timeout
│   └─ Opus: type system, deep reasoning (unresolved-attribute) — 600s timeout
├─ Re-runs Phase 1+2 to verify fixes
└─ Exit 0 if clean, Exit 2 if violations remain (reported to main agent)

What the Main Agent Sees

ScenarioAgent seesHook exit
No violationsNothing0
All fixed by subprocessNothing0
Violations remain after subprocess[hook] N violation(s) remain2
Advisory (duplicates, old tooling)[hook:advisory] ...0

The main agent only sees issues the subprocess couldn't fix. Most quality problems are resolved transparently.

Config Protection (Defense Against Rule-Gaming)

LLMs will modify .ruff.toml or biome.json to disable rules rather than fix code. Plankton blocks this with three layers:

  1. PreToolUse hookprotect_linter_configs.sh blocks edits to all linter configs before they happen
  2. Stop hookstop_config_guardian.sh detects config changes via git diff at session end
  3. Protected files list.ruff.toml, biome.json, .shellcheckrc, .yamllint, .hadolint.yaml, and more

Package Manager Enforcement

A PreToolUse hook on Bash blocks legacy package managers:

  • pip, pip3, poetry, pipenv → Blocked (use uv)
  • npm, yarn, pnpm → Blocked (use bun)
  • Allowed exceptions: npm audit, npm view, npm publish

Setup

Quick Start

Note: Plankton requires manual installation from its repository. Review the code before installing.

# Install core dependencies
brew install jaq ruff uv

# Install Python linters
uv sync --all-extras

# Start Claude Code — hooks activate automatically
claude

No install command, no plugin config. The hooks in .claude/settings.json are picked up automatically when you run Claude Code in the Plankton directory.

Per-Project Integration

To use Plankton hooks in your own project:

  1. Copy .claude/hooks/ directory to your project
  2. Copy .claude/settings.json hook configuration
  3. Copy linter config files (.ruff.toml, biome.json, etc.)
  4. Install the linters for your languages

Language-Specific Dependencies

LanguageRequiredOptional
Pythonruff, uvty (types), vulture (dead code), bandit (security)
TypeScript/JSbiomeoxlint, semgrep, knip (dead exports)
Shellshellcheck, shfmt
YAMLyamllint
Markdownmarkdownlint-cli2
Dockerfilehadolint (>= 2.12.0)
TOMLtaplo
JSONjaq

Pairing with ECC

Complementary, Not Overlapping

ConcernECCPlankton
Code quality enforcementPostToolUse hooks (Prettier, tsc)PostToolUse hooks (20+ linters + subprocess fixes)
Security scanningAgentShield, security-reviewer agentBandit (Python), Semgrep (TypeScript)
Config protectionPreToolUse blocks + Stop hook detection
Package managerDetection + setupEnforcement (blocks legacy PMs)
CI integrationPre-commit hooks for git
Model routingManual (/model opus)Automatic (violation complexity → tier)

Recommended Combination

  1. Install ECC as your plugin (agents, skills, commands, rules)
  2. Add Plankton hooks for write-time quality enforcement
  3. Use AgentShield for security audits
  4. Use ECC's verification-loop as a final gate before PRs

Avoiding Hook Conflicts

If running both ECC and Plankton hooks:

  • ECC's Prettier hook and Plankton's biome formatter may conflict on JS/TS files
  • Resolution: disable ECC's Prettier PostToolUse hook when using Plankton (Plankton's biome is more comprehensive)
  • Both can coexist on different file types (ECC handles what Plankton doesn't cover)

Configuration Reference

Plankton's .claude/hooks/config.json controls all behavior:

{
  "languages": {
    "python": true,
    "shell": true,
    "yaml": true,
    "json": true,
    "toml": true,
    "dockerfile": true,
    "markdown": true,
    "typescript": {
      "enabled": true,
      "js_runtime": "auto",
      "biome_nursery": "warn",
      "semgrep": true
    }
  },
  "phases": {
    "auto_format": true,
    "subprocess_delegation": true
  },
  "subprocess": {
    "tiers": {
      "haiku":  { "timeout": 120, "max_turns": 10 },
      "sonnet": { "timeout": 300, "max_turns": 10 },
      "opus":   { "timeout": 600, "max_turns": 15 }
    },
    "volume_threshold": 5
  }
}

Key settings:

  • Disable languages you don't use to speed up hooks
  • volume_threshold — violations > this count auto-escalate to a higher model tier
  • subprocess_delegation: false — skip Phase 3 entirely (just report violations)

Environment Overrides

VariablePurpose
HOOK_SKIP_SUBPROCESS=1Skip Phase 3, report violations directly
HOOK_SUBPROCESS_TIMEOUT=NOverride tier timeout
HOOK_DEBUG_MODEL=1Log model selection decisions
HOOK_SKIP_PM=1Bypass package manager enforcement

References

  • Plankton (credit: @alxfazio)
  • Plankton REFERENCE.md — Full architecture documentation (credit: @alxfazio)
  • Plankton SETUP.md — Detailed installation guide (credit: @alxfazio)

ECC v1.8 Additions

Copyable Hook Profile

Set strict quality behavior:

export ECC_HOOK_PROFILE=strict
export ECC_QUALITY_GATE_FIX=true
export ECC_QUALITY_GATE_STRICT=true

Language Gate Table

  • TypeScript/JavaScript: Biome preferred, Prettier fallback
  • Python: Ruff format/check
  • Go: gofmt

Config Tamper Guard

During quality enforcement, flag changes to config files in same iteration:

  • biome.json, .eslintrc*, prettier.config*, tsconfig.json, pyproject.toml

If config is changed to suppress violations, require explicit review before merge.

CI Integration Pattern

Use the same commands in CI as local hooks:

  1. run formatter checks
  2. run lint/type checks
  3. fail fast on strict mode
  4. publish remediation summary

Health Metrics

Track:

  • edits flagged by gates
  • average remediation time
  • repeat violations by category
  • merge blocks due to gate failures

Individual skills in this repo

This repo contains 20 individual skills — each has its own dedicated page.

accessibility

Design, implement, and audit inclusive digital products using WCAG 2.2 Level AA. Use when building or auditing UI that must meet WCAG 2.2 Level AA, or when reviewing a change for keyboard, contrast, or screen-reader support.

affaan-m/content-engine

Create platform-native content systems for X, LinkedIn, TikTok, YouTube, newsletters, and repurposed multi-platform campaigns. Use when the user wants social posts, threads, scripts, content calendars, or one source asset adapted cleanly across platforms.

affaan-m/fal-ai-media

Unified media generation via fal.ai MCP — image, video, and audio. Covers text-to-image (Nano Banana), text/image-to-video (Seedance, Kling, Veo 3), text-to-speech (CSM-1B), and video-to-audio (ThinkSound). Use when the user wants to generate images, videos, or audio with AI.

affaan-m/manim-video

日本語翻訳:このファイルは manim-video 用の日本語翻訳が必要です

affaan-m/remotion-video-creation

Remotion のベストプラクティス - React で動画を作成する。3D、アニメーション、音声、字幕、チャート、トランジションなどをカバーするドメイン固有の29のルール。

affaan-m/video-editing

AI-assisted video editing workflows for cutting, structuring, and augmenting real footage. Covers the full pipeline from raw capture through FFmpeg, Remotion, ElevenLabs, fal.ai, and final polish in Descript or CapCut. Use when the user wants to edit video, cut footage, create vlogs, or build video content.

agent-architecture-audit

Full-stack diagnostic for agent and LLM applications. Audits the 12-layer agent stack for wrapper regression, memory pollution, tool discipline failures, hidden repair loops, and rendering corruption. Produces severity-ranked findings with code-first fixes. Essential for developers building agent applications, autonomous loops, or any LLM-powered feature. Use when an agent or LLM feature misbehaves and the failing layer is unknown, or before shipping an agent stack.

agent-eval

Head-to-head comparison of coding agents (Claude Code, Aider, Codex, etc.) on custom tasks with pass rate, cost, time, and consistency metrics. Use when choosing between coding agents, or when a change to an agent setup needs measured pass rate, cost, and time rather than an impression.

agent-harness-construction

Design and optimize AI agent action spaces, tool definitions, and observation formatting for higher completion rates. Use when defining or revising an agent

agentic-engineering

Operate as an agentic engineer using eval-first execution, decomposition, and cost-aware model routing. Use when planning or executing engineering work that agents will carry out end to end.

agentic-os

Build persistent multi-agent operating systems on Claude Code. Covers kernel architecture, specialist agents, slash commands, file-based memory, scheduled automation, and state management without external databases. Use when building a persistent multi-agent system on Claude Code with its own memory, commands, and scheduling.

agent-introspection-debugging

Structured self-debugging workflow for AI agent failures using capture, diagnosis, contained recovery, and introspection reports. Use when an agent run fails and you need a reproducible diagnosis instead of a retry.

agent-payment-x402

Add x402 payment execution to AI agents with per-task budgets, spending controls, and non-custodial wallets. Supports Base through agentwallet-sdk and X Layer through OKX Payments / OKX Agent Payments Protocol. Use when an agent must pay for something itself and needs per-task budgets, spending controls, and a non-custodial wallet.

agent-self-evaluation

Use after completing any non-trivial task. The agent self-rates its output on 5 axes — accuracy, completeness, clarity, actionability, conciseness — with concrete evidence per criterion. Produces a structured 1-5 scorecard with specific improvement suggestions.

agent-sort

Build an evidence-backed ECC install plan for a specific repo by sorting skills, commands, rules, hooks, and extras into DAILY vs LIBRARY buckets using parallel repo-aware review passes. Use when ECC should be trimmed to what a project actually needs instead of loading the full bundle.

ai-first-engineering

Engineering operating model for teams where AI agents generate a large share of implementation output. Use when setting team process, review gates, or ownership rules for a codebase largely written by agents.

ai-regression-testing

Regression testing strategies for AI-assisted development. Sandbox-mode API testing without database dependencies, automated bug-check workflows, and patterns to catch AI blind spots where the same model writes and reviews code. Use when adding regression coverage to AI-assisted code, or when the same model both wrote and reviewed a change.

android-clean-architecture

Clean Architecture patterns for Android and Kotlin Multiplatform projects — module structure, dependency rules, UseCases, Repositories, and data layer patterns. Use when structuring modules, layers, or data flow in an Android or KMP project.

angular-developer

Generates Angular code and provides architectural guidance. Trigger when creating projects, components, or services, or for best practices on reactivity (signals, linkedSignal, resource), forms, dependency injection, routing, SSR, accessibility (ARIA), animations, styling (component styles, Tailwind CSS), testing, or CLI tooling.

api-connector-builder

Build a new API connector or provider by matching the target repo

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