Die Werkzeugbeschreibungen sind auf Englisch.

LLM-Beobachtbarkeit

Monitoring, Traces, Bewertung und Qualität von LLM-Anwendungen.

Anwendungsfall

Aktivität

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121

Werkzeug-Rangliste

Nach gemessenem Wachstum und quellenübergreifend normalisiert sortiert. Rohwerte behalten ihr eigenes Zeitfenster; eine Änderung erfordert mindestens zwei Messungen in 7 Tagen.

  1. 1
    Aktiv

    Deterministic, local-only audit reports for Claude Code AI agent sessions. Rust + SQLite. Zero network calls. MCP server for agents.

    githubgemessenes WachstumQuelle öffnen ↗

    22GitHub-Sternestabil
  2. 2

    Skill
    Aktiv

    Axiom is a curated marketplace of shared plugins for Claude Code and Codex.

    githubgemessenes WachstumQuelle öffnen ↗

    Installieren git clone https://github.com/netopsengineer/axiom ~/.claude/skills/axiom

    5GitHub-Sternestabil
  3. 3

    Agent
    Aktiv

    面向长程研发任务的 Java Agent Harness,支持 A2A 跨语言协作、可恢复执行、上下文工程与 Eval 驱动开发。

    githubgemessenes WachstumQuelle öffnen ↗

    0GitHub-Sternestabil
  4. 4
    Ruhend

    OpenTelemetry semantic conventions and instrumentation for agent provenance, derivation lineage, and acceptance criteria evaluation. Fills the Microsoft AI stack observability gap.

    githubgemessenes WachstumQuelle öffnen ↗

    0GitHub-Sternestabil
  5. 5
    Aktiv

    Agent 降智检测与自愈公评网络 — an immune system for the AI agent society

    githubgemessenes WachstumQuelle öffnen ↗

    1GitHub-Sternestabil
  6. 6
    Aktiv

    Skill instalable para Claude Code que mide si tu agente cumple las reglas establecidas. Lee el historial de sesiones de tu disco y devuelve un porcentaje por regla de cumplimiento. Sin instalar nada, sin red, biblioteca estándar.

    githubgemessenes WachstumQuelle öffnen ↗

    Installieren /plugin marketplace add jleonceo/skill-adherencia-reglas

    1GitHub-Sternestabil
  7. 7

    Sonstige
    Aktiv

    A Go-native framework for LLM agents, with OpenTelemetry observability built in.

    githubgemessenes WachstumQuelle öffnen ↗

    10GitHub-Sternestabil
  8. 8

    Skill
    Ruhend

    An assembly line for AI software development. 35 skills, 11 agent personas, 29 commands. From raw idea to shipped code.

    githubgemessenes WachstumQuelle öffnen ↗

    Installieren /plugin marketplace add aneja5/forge-skills

    3GitHub-Sternestabil
  9. 9
    Aktiv

    Make Claude write clearly, for everyone.

    githubgemessenes WachstumQuelle öffnen ↗

    Installieren /plugin marketplace add stefanobaghino/simple-output-styles

    16GitHub-Sternestabil
  10. 10
    Aktiv

    Multi-agent quality gate skill for Claude Code that researches, reviews, tests and challenges AI-generated work before the final answer.

    githubgemessenes WachstumQuelle öffnen ↗

    Installieren git clone https://github.com/ma-nucho-pro/supervisor-skill-claude ~/.claude/skills/supervisor-skill-claude

    2GitHub-Sternestabil
  11. 11
    Aktiv

    Self improving agents through iterations

    githubgemessenes WachstumQuelle öffnen ↗

    105GitHub-Sternestabil
  12. 12

    Sonstige
    Ruhend

    Official Python SDK for GT8004 — AI agent observability with MCP, A2A, x402 payment tracking. FastAPI, Flask, FastMCP middleware included.

    githubgemessenes WachstumQuelle öffnen ↗

    1GitHub-Sternestabil
  13. 13

    Agent
    Aktiv

    Multi-agent SRE on-call investigator that auto-triages Slack/Discord infrastructure alerts via AWS Bedrock AgentCore, fanning out to specialized agents (CloudWatch, EKS, Slack/Discord scanners) for parallel investigation.

    githubgemessenes WachstumQuelle öffnen ↗

    3GitHub-Sternestabil
  14. 14

    Agent
    Aktiv

    A local A2A event and continuity service for agent applications, with structured history, provenance-safe transcripts, literal search, and natural-language queries.

    githubgemessenes WachstumQuelle öffnen ↗

    1GitHub-Sternestabil
  15. 15

    Agent
    Aktiv

    Provide clear documentation for AgentStack’s MCP protocol, plugins, and ecosystem API with usage examples and tool references.

    githubgemessenes WachstumQuelle öffnen ↗

    0GitHub-Sternestabil
  16. 16

    Skill
    Aktiv

    Glanceable Claude Code and Codex state in your terminal tabs: white=idle, blue=working, orange=waiting. Multi-terminal (iTerm2, WezTerm, AI Power Term), a live status bar with Anthropic usage limits, /sfl and /nil window save-and-restore,…

    githubgemessenes WachstumQuelle öffnen ↗

    Installieren git clone https://github.com/wasulajr/headsup ~/.claude/skills/headsup

    1GitHub-Sternestabil
  17. 17
    Aktiv

    Run many AIs on one board and keep control of all of it. Deterministic code decides who acts — never a model. A privacy floor keeps sensitive work on your machine, your own tests decide what counts as done, and every action lands on a…

    githubgemessenes WachstumQuelle öffnen ↗

    Installieren git clone https://github.com/sandhusukhdeep2/sc-prism-releases ~/.claude/skills/sc-prism-releases

    1GitHub-Sternestabil

Lern- und Referenzressourcen

Nach gemessenem Wachstum und quellenübergreifend normalisiert sortiert. Diese Ressourcen bleiben getrennt zugänglich und fließen nicht in die Hauptwertung ein.

  1. 1
    AktivQuelle öffnen ↗

    Handbook técnico aberto sobre engenharia de IA em produção: ML tradicional, LLMs, RAG, agentes, segurança, observabilidade, FinOps e deployment.

    githubRessourcegemessenes Wachstum
    1GitHub-Sternestabil
  2. 2
    AktivQuelle öffnen ↗

    Documentation-discovery telemetry for Claude Code — heat/cold maps, health grade, evidence-backed router fixes. 100% local, zero tokens. /tt

    github

    Installieren /plugin marketplace add Hedde/trigger_tree

    Ressourcegemessenes Wachstum
    14GitHub-Sternestabil
  3. 3
    AktivQuelle öffnen ↗

    My complete journey to becoming an Agentic AI Engineer through structured learning, projects, experiments, and production-ready implementations of modern AI systems.

    githubRessourcegemessenes Wachstum
    18GitHub-Sternestabil
  4. 4

    tunelab

    Skill
    AktivQuelle öffnen ↗

    Claude Code plugin for LLM fine-tuning, distillation, and evaluation — decide whether you need fine-tuning at all, distill your LLM logs into small local models (MLX/LoRA), evaluate with held-out discipline, and learn the why at every step.

    github

    Installieren /plugin marketplace add rchaz/tunelab

    Ressourcegemessenes Wachstum
    6GitHub-Sternestabil