1.3천건 · 관련도순
Testing and benchmarking LLM agents including behavioral testing, capability assessment, reliability metrics, and production monitoring—where even top agents achieve less than 50% on real-world benchmarks Use when: agent testing, agent evaluation, benchmark agents, agent reliability, test agent.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Run CIS benchmark checks against cluster nodes and control planes when an agent needs a narrow Kubernetes hardening audit, not a general platform listing.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Run a real-task benchmark suite against OpenClaw agents so model or harness changes can be compared before they hit production workflows.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Use EnterpriseRAG-Bench to evaluate an enterprise RAG or knowledge-agent system against a realistic synthetic company corpus with answer, recall, and comparative scoring.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Evaluates fund performance against peer universes with vintage year comparison, quartile ranking, and strategy-specific benchmarking. Use when benchmarking fund performance, analyzing vintage comparisons, or assessing relative positioning.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Use Therapeutics Data Commons through the PyTDC Python package for registry discovery, approved dataset access, task-aware splits (scaffold, cold-start, temporal, combination), evaluator metrics, benchmark groups, and bounded molecular-oracle workflows. Use this skill to find which TDC datasets exist for a therapeutic task, load them with a split that does not leak, score predictions with the task's own official metric rather than a generic one, and run benchmark groups reproducibly. Also trigger on PyTDC, Therapeutics Data Commons, tdc.single_pred, tdc.multi_pred, ADMET Benchmark Group, scaffold split, get_split, or molecular oracles such as GSK3B, JNK3 and DRD2.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Select an OCR/HTR engine by benchmarking candidates on the same labelled samples — source ground truth from open datasets (e.g. Hugging Face PAGE-XML corpora) or hand-transcription, wrap every engine behind a transcribe(image_path) -> str adapter (vision-LLMs via any OpenAI-compatible endpoint, dedicated HTR REST APIs, cloud OCR), and score with zero-dependency raw CER, lenient CER, WER, and a critical name/date token diff. Use when choosing an engine for handwriting or historical-document transcription, when published accuracy claims need honest verification on your own material, or when a records workflow must surface confident-but-wrong names and dates that a low aggregate CER hides.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
SWE-bench pipeline for A/B testing Grafema with Claude Code. Use when: (1) running SWE-bench experiments, (2) comparing baseline vs grafema conditions, (3) evaluating results, (4) debugging container issues.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Aggregate DiT inference outputs into per-dimension VBench metrics via AISBench. Run after the quant-tuning-evaluate DiT workflow produces a directory of mp4s and the user supplies a VBench-1.0-mini JSON + pre-populated cache dir. Single mode (Standard).
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Design AI benchmarks for comparing models and tracking improvement. TRIGGERS - Use when user needs help with ai-benchmark-creator related tasks.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Create benchmark suites for AI model comparison. TRIGGERS - Use when user needs help with ai-benchmark-suite related tasks.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Perform triangulated cognitive research combining SQLite memory, GitHub 10k+ stars patterns, and ToT DAG benchmarking before critical architectural decisions. Automatically triggers subagent research escalation when confidence is low (<0.70). Keywords: research, benchmark, github, memory, triangulation, grounding, best-practices, escalation
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Design, run, and review fair performance, token, and cost comparisons across GitHub Copilot CLI, Codex CLI, and direct model APIs. Use for AI CLI benchmark, Copilot vs Codex, prompt-cache diagnosis, cache read/write analysis, fresh-process latency measurement, token-cost comparison, or when a benchmark must separate provider caching from local CLI caches.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
"Calculate and benchmark social media engagement rates across platforms and variants. Use this skill when the user needs to compute engagement metrics, compare performance across accounts or posts, or set engagement benchmarks — even if they say 'what is my engagement rate', 'benchmark engagement', or 'social media KPIs'.".
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Bench-aware Frappe operations guidance for app installs, branch switching, updates, migrations, site inspection, ports, and process state. Use when a task involves bench commands or local environment operations.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Container and Kubernetes security assessment. Tests container escape vectors, Docker/containerd socket exposure, K8s RBAC misconfigurations, pod security violations, exposed API servers, etcd access, service account token abuse, image layer secrets, private registry attacks, SSRF to metadata services, cross-namespace network bypass, CIS benchmarks, crypto miner detection, resource exhaustion, and admission controller gaps. Both external and internal (compromised pod) perspectives. Uses trivy, kube-bench, kubectl, nuclei, dive, amicontained, and docker-bench-security. Covers OWASP Kubernetes Top 10 and all 22 Kubernetes Goat attack scenarios.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
他山工作台（tashan-workbench）开发专属 Skill。接手/开发/调试工作台时激活：启动命令、架构速查、历史踩坑、测试方法。触发词：「他山工作台」「openwork-local」「tashan-workbench 开发」「工作台怎么启动」「接手工作台」「工作台调试」。
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Benchmark multi-agent coordination systems with experiment design, power analysis, human rating protocols, bootstrap confidence intervals, and reproducible reporting. Use for salvage latency, recovery fidelity, coordination overhead, and protocol comparison studies. NOT for ML model benchmarking, web-product A/B testing, survey design, or general-purpose data science.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Experimental/manual harness canary procedure. It records benchmark manifests and scores but does not provide a fully automated runner.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Testes e benchmarking de agentes LLM incluindo testes comportamentais, avaliação de capacidades, métricas de confiabilidade e monitoramento em produção—onde até os melhores agentes alcançam menos de 50% em benchmarks do mundo real. Use quando: testes de agentes, avaliação de agentes, benchmark de agentes, confiabilidade de agentes, teste de agentes.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Avalia LLMs em 100+ benchmarks de 18+ harnesses (MMLU, HumanEval, GSM8K, segurança, VLM) com execução multi-backend. Use quando precisar de avaliação escalável em Docker local, HPC Slurm ou plataformas cloud. Plataforma enterprise-grade da NVIDIA com arquitetura container-first para benchmarking reproduzível.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Verify someone else's code fix against your own live corpora (agent transcripts, sessions, memory files) before trusting or merging it. Use when an external PR or patch touches how transcripts, sessions or agent memory are read. Triggers: /corpus-bench, run it on our corpus, verify the fix on our transcripts, corpus bench.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Generate self-contained HTML/CSS benchmark and comparison charts in an ego-inspired Browser-Native Editorial style. Use for blue-white product metrics, model benchmarks, A/B comparisons, efficiency reports, difficulty breakdowns, and compact technical data panels.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Compare standardized metrics across multiple ego-benchmark-harness runs by recalculating them from tasks.jsonl and session JSONL, never from HTML reports. Covers overall aggregates (success rate/average score), Odysseys per-rubric pass rates, duration distributions (avg/p50/p90/max), turns/tools/cost, error and exception counts, per-task success matrices, helper distributions, and the share of DOM operations. Use whenever users ask to compare data or metrics across runs, calculate aggregate/Odysseys/duration/turn/error/helper metrics, inspect differences among runs, or compare multiple rwb/real-world-bench runs. Use pi-session-analyzer instead for deep forensic analysis of why an individual task was slow or failed.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.