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Use when evaluating a newly adapted model's accuracy/eval score. Runs evalscope eval (or the test/srt/run_eval.py fallback) against an OpenAI-compatible endpoint on datasets like gsm8k / aime, producing per-dataset reports and a score. Accuracy only — throughput/latency benchmarks belong to model-speed-benchmark. Triggers on accuracy, eval, evalscope, gsm8k, aime, run_eval — not throughput.
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Plan and document studies that assess whether an analytical procedure suits its intended use. Select the governing framework, evaluate calibration, accuracy, precision, and detection limits, and compare methods using acceptance criteria defined before data collection.
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Launch an SGLang server, run a quick accuracy check, and capture an execution trace for performance inspection. Return the trace location and generated files, ready to open in Perfetto or Chrome tracing.
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Reduce LLM weight usage through pruning, which removes selected model parameters. Explore Wanda and SparseGPT for compression without retraining, choose hardware-friendly patterns for faster inference, and compare the compressed model's accuracy with the original.
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Build probability estimates from historical frequencies and supporting or opposing evidence. Structure forecasts with explicit assumptions and outcome criteria, update them as evidence changes, and assess past predictions using accuracy and confidence calibration.
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Guides LLM compression through pruning, which removes selected model weights. Covers Wanda and SparseGPT methods without retraining, accuracy comparisons, and structured weight patterns that can accelerate inference on compatible hardware.
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Use when running throughput/latency (serving speed) benchmarks against an already-running model server. Uses sgl_jax.bench_serving to sweep batch-size/concurrency and measure throughput and latency, producing summary.csv and per-point bs_<N>/result.jsonl. Speed only — accuracy/eval belongs to model-accuracy-benchmark. Triggers on throughput, latency, bench_serving, batch-size sweep, tok/s, TTFT, TPOT — not accuracy/eval.
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Use when thumbnail concepts need real-size, accuracy-first scoring without misleading claims. Not for generating thumbnails or declaring winners that fail the accuracy rubric.
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End-to-end automated model quantization and accuracy tuning workflow. Use when user asks for automated model quantization and accuracy tuning, e.g. "自动量化", "量化调优", "一键量化", "精度调优", etc. 当用户给出多卡（≥2 卡）卡号时，量化阶段通过 EP 并行适配（msmodelslim-ep-parallel-adaptation）保证多卡 EP 并行，调优主流程仍在本 Skill。
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使用whisper在本地转录音频文件，保存为文本或生成翻译字幕，无需API密钥。可选择不同大小的模型，根据任务需求平衡处理速度与准确度。
Set up deployment checks for healthcare applications covering clinical decision support, patient information exposure, data integrity, workflows, and integrations. Block releases when critical tests fail, and flag lower workflow or integration pass rates for review.
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Have an AI agent assess its completed work for accuracy, completeness, clarity, actionability, and conciseness. Receive a scorecard with evidence, an average score, and prioritized improvements tied to the original request.
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Helps you build similarity search with FAISS to find nearby vectors, numerical representations of data. Covers index selection, saving and loading, GPU use, and tuning search speed, accuracy, and memory consumption for large datasets.
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Compare README, specification, and product documents with recent git history to find outdated claims or missing features. Make small factual corrections, preserve the existing style, and prepare a pull request for review.
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Verify RuView pipeline output against a reference hash, package evidence for others to check, and review accuracy statements. The skill distinguishes measured, claimed, and synthetic results and requires real device logs for hardware validation.
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Tune AgentDB memory use and search speed through quantization, which stores vectors more compactly, search indexes, caching, and batch operations. Includes configuration examples and benchmarks to help balance accuracy, speed, and storage as datasets grow.
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Retrieve World Monitor forecasts to review scenario probabilities, changes, and supporting signals by region or topic. Check forecast accuracy records with their audit status, and distinguish unavailable data from an absence of forecasted risks.
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Evaluate improvements to Archify diagrams using actual outputs and agent work records. Compare accuracy, readability, and total delivery time to investigate layout, connection routing, diagnostics, and authoring guidance, then document what the evidence supports.
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Review OpenAI Cookbook documentation and notebook prose for technical accuracy, readable wording, consistent terminology, and publication metadata. Make focused edits, preserve notebook code and outputs, and report unresolved issues with validation results.
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Create agents that learn from interactions and adapt their behavior over time. Monitor learning progress, review efficiency and accuracy metrics, trigger adjustments from feedback, and share acquired knowledge with other agents through dedicated tools.
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Tune AgentDB memory use and search speed through quantization, which stores vectors more compactly, search indexes, caching, and batch operations. Includes configuration examples and benchmarks to help balance accuracy, speed, and storage as datasets grow.
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Review existing Appwrite blog posts for tone, structure, SEO, formatting, and product accuracy. Compare claims with local documentation, identify specific problems, and produce a structured report with suggested rewrites and prioritized fixes.
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Design how an AI agent finds and loads tools as needed. Compare discovery approaches, organize large tool collections, and assess tool selection accuracy, token costs, and cache reuse without placing every tool definition in the initial context.
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Evaluate BMad skills using your agent's configured runner. Compare results with the model alone or another version, grade outputs against criteria, and test whether descriptions trigger correctly, preserving transcripts and evidence for each run.
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