48 件 ・ 関連度順
概要と使いどころ
Reduce LLM size and accelerate inference using pruning techniques like Wanda and SparseGPT. Use when compressing models without retraining, achieving 50% sparsity with minimal accuracy loss, or enabling faster inference on hardware accelerators. Covers unstructured pruning, structured pruning, N:M sparsity, magnitude pruning, and one-shot methods.
日本語の概要は準備中です。原文の説明を表示しています。
Reduce LLM size and accelerate inference using pruning techniques like Wanda and SparseGPT. Use when compressing models without retraining, achieving 50% sparsity with minimal accuracy loss, or enabling faster inference on hardware accelerators. Covers unstructured pruning, structured pruning, N:M sparsity, magnitude pruning, and one-shot methods.
日本語の概要は準備中です。原文の説明を表示しています。
Reduza o tamanho de LLMs e acelere a inferência usando técnicas de pruning como Wanda e SparseGPT. Use para comprimir modelos sem retreinamento, alcançando 50% de esparsidade com perda mínima de acurácia, ou ativando inferência mais rápida em aceleradores de hardware. Cobre pruning não estruturado, pruning estruturado, esparsidade N:M, pruning por magnitude e métodos one-shot.
日本語の概要は準備中です。原文の説明を表示しています。
Reduce LLM size and accelerate inference using pruning techniques like Wanda and SparseGPT. Use when compressing models without retraining, achieving 50% sparsity with minimal accuracy loss, or enabling faster inference on hardware accelerators. Covers unstructured pruning, structured pruning, N:M sparsity, magnitude pruning, and one-shot methods.
日本語の概要は準備中です。原文の説明を表示しています。
Cultivate bonsai trees from species selection through long-term seasonal care. Covers species suitability, structural and maintenance pruning, wiring technique, repotting protocol, soil mix preparation, seasonal care schedules, and contemplative sitting practice. Use when selecting a species for a first or next bonsai, performing structural or maintenance pruning, repotting when roots are circling or growth has stalled, wiring branches for shaping, developing a seasonal care calendar, or building a contemplative practice with a living tree.
日本語の概要は準備中です。原文の説明を表示しています。
Infer transcription factor regulons from single-cell RNA-seq with pySCENIC by combining GRNBoost2 co-expression, cisTarget motif-enrichment pruning, and AUCell per-cell activity scoring. Covers the motif-pruning-as-directionality principle, regulon specificity scoring, run-to-run stability, and database/species matching. Use when identifying TF regulons, scoring TF activity per cell, finding master regulators of cell identity, or comparing regulon activity across conditions. For enhancer-driven multiomic GRNs see multiomics-grn; for bulk inference and VIPER protein-activity see grn-inference.
日本語の概要は準備中です。原文の説明を表示しています。
Infer transcription factor regulons from single-cell RNA-seq with pySCENIC by combining GRNBoost2 co-expression, cisTarget motif-enrichment pruning, and AUCell per-cell activity scoring. Covers the motif-pruning-as-directionality principle, regulon specificity scoring, run-to-run stability, and database/species matching. Use when identifying TF regulons, scoring TF activity per cell, finding master regulators of cell identity, or comparing regulon activity across conditions. For enhancer-driven multiomic GRNs see multiomics-grn; for bulk inference and VIPER protein-activity see grn-inference.
日本語の概要は準備中です。原文の説明を表示しています。
Infer transcription factor regulons from single-cell RNA-seq with pySCENIC by combining GRNBoost2 co-expression, cisTarget motif-enrichment pruning, and AUCell per-cell activity scoring. Covers the motif-pruning-as-directionality principle, regulon specificity scoring, run-to-run stability, and database/species matching. Use when identifying TF regulons, scoring TF activity per cell, finding master regulators of cell identity, or comparing regulon activity across conditions. For enhancer-driven multiomic GRNs see multiomics-grn; for bulk inference and VIPER protein-activity see grn-inference.
日本語の概要は準備中です。原文の説明を表示しています。
XやLinkedInのつながりを現在の仕事や関心に合わせて見直します。整理・維持・追加の候補を理由付きで示し、本人の文体に合う連絡文の下書きまでまとめます。
This skill should be used when designing autonomous agent harnesses: research loops, evaluation scaffolds, locked and editable surfaces, durable logs, novelty gates, pruning, rollback, PR preparation, and human approval boundaries.
日本語の概要は準備中です。原文の説明を表示しています。
Proactively extract critical values from tool results into working notes before automatic context pruning destroys them.
日本語の概要は準備中です。原文の説明を表示しています。
Maintain a free-form scratchpad of decisions, extracted values, and open questions so context pruning doesn't lose anything you still need.
日本語の概要は準備中です。原文の説明を表示しています。
Use Bio.Phylo to read/write phylogenetic trees and perform visualization and statistics; use when tree parsing/conversion, pruning/rerooting, distance calculation, or plotting is required.
日本語の概要は準備中です。原文の説明を表示しています。
Execute tasks through systematic exploration, pruning, and expansion using Tree of Thoughts methodology with meta-judge evaluation specifications and multi-agent evaluation
日本語の概要は準備中です。原文の説明を表示しています。
Reorganize the user's X and LinkedIn network with review-first pruning, add/follow recommendations, and channel-specific warm outreach drafted in the user's real voice. Use when the user wants to clean up following lists, grow toward current priorities, or rebalance a social graph around higher-signal relationships.
日本語の概要は準備中です。原文の説明を表示しています。
Generates 3D conformer ensembles using RDKit ETKDGv3 with knowledge-enhanced distance geometry, MMFF94/UFF force-field optimization, CREST + GFN2-xTB semi-empirical refinement, and macrocycle-aware torsion preferences. Provides explicit decision rules for single vs ensemble conformer use, RMSD pruning, energy windows, conformer count, and force-field choice. Use when preparing 3D ligands for docking, generating descriptor input for 3D QSAR, or sampling macrocycle/peptide conformational ensembles.
日本語の概要は準備中です。原文の説明を表示しています。
Audit whether tests earn their maintenance cost and which suite owns each contract. Use when pruning redundant tests, investigating implementation coupling or test-only production hooks, reviewing the value of proposed coverage, or auditing an entire subsystem. Use write-tests to implement the resulting test changes.
日本語の概要は準備中です。原文の説明を表示しています。
Manages digital garden notes, link structures, and health metrics. Use when curating a knowledge base, pruning stale notes, or tracking content maturity.
日本語の概要は準備中です。原文の説明を表示しています。
Dead code and dependency cleanup, repo-wide: unused deps, unreferenced exports, unreachable code, orphaned files — removed only with tool evidence, in reversible waves. Runs on green main. For diff-scoped cleanup use absolute-simplify. Triggers on "absolute prune", "remove dead code", "find unused deps/exports", "what can we delete", "clean up orphaned files".
日本語の概要は準備中です。原文の説明を表示しています。
Generates 3D conformer ensembles using RDKit ETKDGv3 with knowledge-enhanced distance geometry, MMFF94/UFF force-field optimization, CREST + GFN2-xTB semi-empirical refinement, and macrocycle-aware torsion preferences. Provides explicit decision rules for single vs ensemble conformer use, RMSD pruning, energy windows, conformer count, and force-field choice. Use when preparing 3D ligands for docking, generating descriptor input for 3D QSAR, or sampling macrocycle/peptide conformational ensembles.
日本語の概要は準備中です。原文の説明を表示しています。
Use when reducing model size, improving inference speed, or deploying to edge devices - covers quantization, pruning, knowledge distillation, ONNX export, and TensorRT optimizationUse when ", " mentioned.
日本語の概要は準備中です。原文の説明を表示しています。
Remove redundant or unsupported page content.
日本語の概要は準備中です。原文の説明を表示しています。
Evaluate any agent skill against a merged framework — Anthropic's Claude Code best practices plus Matt Pocock's writing-great-skills methodology — across 4 axes (Trigger, Structure, Steering, Pruning). Produces an evidence-cited scorecard (0–100), a weighted overall score, and diagnosed failure modes with prioritized fixes. Use when the user asks to evaluate, rate, or audit a skill ("evaluate this skill", "skill scorecard", "review SKILL.md"), or to compare two skills.
日本語の概要は準備中です。原文の説明を表示しています。
Adapts LLMs: SFT dataset loss masking, LoRA and QLoRA, full FT vs PEFT, distillation, pruning, tokenizer fragmentation. Use when fine-tuning or compressing a model.
日本語の概要は準備中です。原文の説明を表示しています。