「sel」の検索結果

Run and grow the OpenAmer Agent-to-Agent swarm: identity, trust, node-to-node ask, signed skill/insight sharing, and the autonomous self-learning loop.

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Sifa 1 , Fabian Küch 6 , Andreas Herten 4 , René Jäkel 3 , Georg Rehm 5 , Stefan Kesselheim 4 , Joac

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In each setting, LLM-based methods substantially outperform classical baselines, achieving up to 68% recall at 90% precision compared to near 0% for t

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Navigating Sparse Evidence: Agentic Visual RAG via Explicit Context Selection and Consolidation Soochow University ; Baidu Inc.

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Use when the agent needs recursive reasoning, episodic memory retrieval, internet learning, or world-model prediction. Mini-OpenAmer: the 2B hybrid Mamba core with 9 tools, running 24/7 on Damir's laptop.

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Use for the self-evolving civilization - skills, memories, swarm agents, energy economy, grid duels, metacognition. Run `python scripts/autonomous_loop.py --loop` from the repo root (or `darwin_engine.py --full` for skill evolution only).

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Validate protein designs using AlphaFold2 structure prediction. Use this skill when: (1) Validating designed sequences fold correctly, (2) Predicting binder-target complex structures, (3) Calculating confidence metrics (pLDDT, pTM, ipTM), (4) Self-consistency validation of designs, (5) Multi-chain complex prediction with AlphaFold-Multimer.

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scikit-learn compatible Python toolkit for time series ML: classify, cluster, regress, segment, transform with 30+ algorithms (ROCKET, InceptionTime, KNN-DTW, HIVE-COTE, WEASEL). Handles panel, multivariate, and unequal-length series. Maintained successor to sktime. Alternatives: sktime (larger ecosystem), tslearn (fewer algorithms), catch22 (features only).

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AI-powered adeno-associated virus (AAV) vector design for gene therapy including capsid engineering, promoter selection, and tropism optimization.

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Run programmatic Tavily search and extraction while filtering raw results outside the main agent context. Use for multi-step or high-volume research where titles, snippets, and selected passages should be curated before synthesis.

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RED-GREEN-REFACTOR for process documentation - baseline without skill, write addressing failures, iterate closing loopholes

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Systematic strategic planning framework for philosophy and interdisciplinary academic papers targeting preprint platforms (PhilArchive, arXiv, PhilSci-Archive). Use when users want to: (1) plan a paper on a specific topic, (2) identify research gaps and assess originality, (3) develop optimized paper outlines, (4) prepare for preprint submission, or (5) understand platform requirements and writing standards. Triggered by phrases like 'plan a paper on,' 'help me design a paper about,' 'identify research gaps in,' 'is this idea original,' or when users need structured research planning. The skill guides through three phases: Platform Analysis (identifying target venue and studying sample papers), Theoretical Framework (AI-driven literature search and gap identification), and Outline Optimization (structured design with reviewer-perspective self-assessment). Each phase includes quality evaluation standards and validation checkpoints.

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Create isolated git worktrees with smart directory selection and safety verification

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Train or fine-tune sentence-transformers models across `SentenceTransformer` (bi-encoder, dense or static embedding model for retrieval, similarity, clustering, classification, paraphrase mining, dedup, multimodal), `CrossEncoder` (reranker, pair scoring for two-stage retrieval / pair classification), `SparseEncoder` (SPLADE, sparse embedding model for learned-sparse retrieval), and `MultiVectorEncoder` (ColBERT / late-interaction, per-token embeddings scored with MaxSim). Covers loss selection, hard-negative mining, evaluators, distillation, LoRA, Matryoshka, and Hugging Face Hub publishing. Use for any sentence-transformers training task.

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NVIDIA RAG Blueprint evaluation guidance for measuring retrieval and answer quality with stable datasets, baselines, and reproducible scoring workflows.

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Guide for creating or updating effective portable skills with specialized knowledge, workflows, or tool integrations. Use for Codex, Claude Code, GitHub Copilot, or shared-catalog skill work after selecting the target host and installation path.

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Capture a self-contained static HTML snapshot from a running app or mock component so it can be reviewed or uploaded to Stitch.

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Install audited skills from a curated list or GitHub repo path into Codex or Claude Code after selecting the target host. Use when a user asks to list or install a skill, including from a private repo; preserve Codex system-skill boundaries and use an explicit Claude destination for Claude Code.

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Evaluate recommender system quality for the CSX4207 Vinyl Record Store. Use whenever you must compute or report recommender metrics (Precision@k, Recall@k, HitRate@k, MRR, MAP@k, NDCG@k, coverage, diversity, novelty, serendipity, personalization), design an evaluation protocol/split, run a baseline comparison, or write the evaluation section of a course deliverable. Covers formulas, JavaScript reference implementations, and the reporting checklist.

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Guides MongoDB users through implementing and optimizing Atlas Search (full-text), Vector Search (semantic), and Hybrid Search solutions. Use this skill when users need to build search functionality for text-based queries (autocomplete, fuzzy matching, faceted search), semantic similarity (embeddings, RAG applications), or combined approaches. Also use when users need text containment, substring matching ('contains', 'includes', 'appears in'), case-insensitive or multi-field text search, or filtering across many fields with variable combinations. Provides workflows for selecting the right search type, creating indexes, constructing queries, and optimizing performance using the MongoDB MCP server.

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Scan a codebase for deepening opportunities, present them as a visual HTML report, then guide a user-directed decision loop for the selected candidate.

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Trains and fine-tunes vision models for object detection (D-FINE, RT-DETR v2, DETR, YOLOS), image classification (timm models — MobileNetV3, MobileViT, ResNet, ViT/DINOv3 — plus any Transformers classifier), and SAM/SAM2 segmentation using Hugging Face Transformers on Hugging Face Jobs cloud GPUs. Covers COCO-format dataset preparation, Albumentations augmentation, mAP/mAR evaluation, accuracy metrics, SAM segmentation with bbox/point prompts, DiceCE loss, hardware selection, cost estimation, Trackio monitoring, and Hub persistence. Use when users mention training object detection, image classification, SAM, SAM2, segmentation, image matting, DETR, D-FINE, RT-DETR, ViT, timm, MobileNet, ResNet, bounding box models, or fine-tuning vision models on Hugging Face Jobs.

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Run evaluations for Hugging Face Hub models using inspect-ai and lighteval on local hardware. Use for backend selection, local GPU evals, and choosing between vLLM / Transformers / accelerate. Not for HF Jobs orchestration, model-card PRs, .eval_results publication, or community-evals automation.

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。