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KI-Erste Entwicklung

Betriebsmodell für Teams, in denen KI-Agenten einen großen Anteil der Implementierung übernehmen. Nutze dies bei der Festlegung von Teamprozessen, Review-Grenzen oder Eigentumsregeln für eine Codebasis, die größtenteils von Agenten geschrieben wurde.

Was ist KI-Erste Entwicklung?

Dieses Skill definiert ein Betriebsmodell für Softwareentwicklungsteams, die stark auf KI-Agenten zur Codegenerierung setzen. Es zielt darauf ab, den Entwicklungsprozess so zu strukturieren, dass die Vorteile von KI-Assistenz maximiert werden, während gleichzeitig Qualitäts- und Sicherheitsrisiken kontrolliert werden. Der Fokus verlagert sich von der reinen Schreibgeschwindigkeit hin zu Planungsqualität, Evaluierungsabdeckung und systemischem Verhalten. Es unterstützt Plattformen wie Claude Code, Cursor und Codex und bietet konkrete Leitlinien zu Architektur (klare Grenzen, stabile Verträge, typisierte Schnittstellen), Code-Review (Verhaltensregressionen, Sicherheitsannahmen, Datenintegrität) sowie Einstellung und Bewertung von Entwicklern, die in diesem Kontext arbeiten. Das Skill hebt hervor, dass die Testabdeckung für generierten Code erhöht werden muss, inklusive Regressionstests, expliziten Randfallassertionen und Integrationsprüfungen an Schnittstellengrenzen.

Funktioniert mit~Claude Code~Codex CLI~Cursor
npx skills add https://github.com/affaan-m/everything-claude-code/tree/main/skills/ai-first-engineering
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Dokumentation

Was macht KI-Erste Entwicklung?

Verwende dieses Skill bei der Gestaltung von Prozessen, Reviews und Architektur für Teams, die mit KI-unterstützter Codegenerierung arbeiten.

Prozessverschiebungen

  1. Planungsqualität ist wichtiger als Tippgeschwindigkeit.
  2. Evaluierungsabdeckung ist wichtiger als anekdotisches Vertrauen.
  3. Review-Fokus verlagert sich von Syntax auf Systemverhalten.

Architekturanforderungen

Bevorzuge Architekturen, die agentenfreundlich sind:

  • explizite Grenzen
  • stabile Verträge
  • typisierte Schnittstellen
  • deterministische Tests

Vermeide implizites Verhalten, das über versteckte Konventionen verteilt ist.

Code-Review in KI-Ersten Teams

Überprüfe auf:

  • Verhaltensregressionen
  • Sicherheitsannahmen
  • Datenintegrität
  • Fehlerbehandlung
  • Rollout-Sicherheit

Minimiere Zeit, die für Stilfragen aufgewendet wird, die bereits durch Automatisierung abgedeckt sind.

Einstellungs- und Bewertungssignale

Starke KI-erste Entwickler:

  • zerlegen mehrdeutige Arbeit sauber
  • definieren messbare Akzeptanzkriterien
  • erstellen hochwertige Prompts und Evaluierungen
  • setzen unter Lieferschwierigkeiten Risikokontrollen durch

Teststandard

Erhöhe die Testlatte für generierten Code:

  • erforderliche Regressionstestabdeckung für betroffene Domänen
  • explizite Randfallassertionen
  • Integrationsprüfungen für Schnittstellengrenzen

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/claude-api

Anthropic Claude API patterns for Python and TypeScript. Covers Messages API, streaming, tool use, vision, extended thinking, batches, prompt caching, and Claude Agent SDK. Use when building applications with the Claude API or Anthropic SDKs.

affaan-m/everything-claude-code

Development conventions and patterns for everything-claude-code. JavaScript project with conventional commits.

affaan-m/everything-claude-code

End-to-end marketing campaign planning and execution. Covers audience research, positioning, campaign angle definition, landing page copy, email sequences, social posts, ad copy, short-form video scripts, and content calendars. Use as the orchestration layer for multi-channel product launches. Use when planning or executing a multi-channel product launch, or producing landing page, email, social, or ad copy.

affaan-m/everything-claude-code-conventions

Development conventions and patterns for everything-claude-code. JavaScript project with conventional commits.

affaan-m/frontend-design

Create distinctive, production-grade frontend interfaces with high design quality. Use when the user asks to build web components, pages, or applications and the visual direction matters as much as the code quality.

affaan-m/gget

gget CLI and Python workflow for quick genomic database queries, sequence lookup, BLAST-style searches, enrichment checks, and reproducible bioinformatics evidence logs.

affaan-m/literature-review

Systematic literature-review workflow for academic, biomedical, technical, and scientific topics, including search planning, source screening, synthesis, citation checks, and evidence logging.

affaan-m/motion-ui

Production-ready UI motion system for React/Next.js. Use when implementing animations, transitions, or motion patterns.

affaan-m/project-guidelines-example

Example project-specific skill template based on a real production application.

affaan-m/pubmed-database

Direct PubMed and NCBI E-utilities search workflows for biomedical literature, MeSH queries, PMID lookup, citation retrieval, and API-backed literature monitoring.

affaan-m/scholar-evaluation

Structured scholarly-work evaluation for papers, proposals, literature reviews, methods sections, evidence quality, citation support, and research-writing feedback.

affaan-m/uspto-database

USPTO patent and trademark data workflow for official record lookup, PatentSearch queries, TSDR checks, assignment data, and reproducible IP research logs.

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.

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Extract frames or short clips from videos using ffmpeg.

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steipete/summarize

Summarize or transcribe URLs, YouTube/videos, podcasts, articles, transcripts, PDFs, and local files.

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openclaw/openclaw

Use when testing, fixing, or extending the OpenClaw Control UI GUI with Vitest + Playwright end-to-end checks, mocked Gateway WebSocket flows, mocked dashboard runs, screenshots/videos, or agent-verifiable browser proof.

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obra/superpowers

Obra superpowers agent skills.

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affaan-m/ECC

See, Understand, Act on video and audio. See- ingest from local files, URLs, RTSP/live feeds, or live record desktop; return realtime context and playable stream links. Understand- extract frames, build visual/semantic/temporal indexes, and search moments with timestamps and auto-clips. Act- transcode and normalize (codec, fps, resolution, aspect ratio), perform timeline edits (subtitles, text/image overlays, branding, audio overlays, dubbing, translation), generate media assets (image, audio...

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affaan-m/everything-claude-code

End-to-end marketing campaign planning and execution. Covers audience research, positioning, campaign angle definition, landing page copy, email sequences, social posts, ad copy, short-form video scripts, and content calendars. Use as the orchestration layer for multi-channel product launches. Use when planning or executing a multi-channel product launch, or producing landing page, email, social, or ad copy.

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