「scoring」の検索結果

Claude Codeでの修正や繰り返し作業から、確信度付きの小さな行動ルールを抽出し、プロジェクト別に管理してスキルやコマンドへ育てるスキル。

• 修正指示や反復作業の知識化
• プロジェクト別の規約管理
• 行動ルールのスキル・コマンド化

プロンプト、正規表現、SQL、小さなコードを、採点基準に沿って繰り返し改良します。AIが候補を作り、失敗例を手がかりによりよい案を探すスキルです。

• 正解例に沿ってプロンプトを改善したいとき
• 合格率で正規表現を調整したいとき
• テストでSQLやコードを改善したいとき

Music generation queueing, retrieval, and completion endpoints via Venice.ai. Suited for jingles, background loops, and prototype scoring.

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Search, score, scan, and import agent skills from GitHub repositories that contain SKILL.md, CLAUDE.md, .cursorrules, and similar agent skill files. Discover community skills across many tool and provider categories, evaluate relevance with heuristic scoring, check for malware or hardcoded secrets, and install into Hermes, Claude Code, Gemini CLI, or OpenCode agent directories.

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Generate trading signals using npx neural-trader anomaly detection engine with Z-score scoring and neural prediction

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Analyze git diffs for risk scoring, reviewer recommendations, and change classification. Use when preparing a PR, reviewing a large or cross-module change, or before merging to assess risk and pick reviewers.

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Comprehensive truth scoring, code quality verification, and automatic rollback system with 0.95 accuracy threshold for ensuring high-quality agent outputs and codebase reliability.

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When the user wants help with revenue operations, lead lifecycle management, or marketing-to-sales handoff processes. Also use when the user mentions 'RevOps,' 'revenue operations,' 'lead scoring,' 'outbound pipeline stages,' 'lead routing,' 'MQL,' 'SQL,' 'pipeline stages,' 'deal desk,' 'CRM automation,' 'marketing-to-sales handoff,' 'data hygiene,' 'leads aren't getting to sales,' 'pipeline management,' 'lead qualification,' or 'when should marketing hand off to sales.' Use this for anything involving the systems and processes that connect marketing to revenue. For cold outreach emails, see cold-email. For email drip campaigns, see emails. For pricing decisions, see pricing.

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Performs pathway and gene-set enrichment analysis on gene lists or ranked gene data and interprets the results. Used when the user has a set of genes (differentially expressed genes from PyDESeq2/Scanpy, CRISPR-screen hits, cluster marker genes, proteomics hits) and wants to know which biological pathways, GO terms, or gene sets are over-represented or enriched. Covers over-representation analysis (ORA / Enrichr / Fisher / hypergeometric), ranked Gene Set Enrichment Analysis (GSEA / preranked), single-sample scoring (ssGSEA/GSVA), and functional profiling via gseapy, g:Profiler, Enrichr libraries, MSigDB, GO, KEGG, Reactome, and WikiPathways — plus gene-ID mapping, choosing the right background universe, multiple-testing correction, redundancy reduction, dotplots/enrichment maps, and publication-ready tables. Use this for "pathway analysis", "enrichment analysis", "GO enrichment", "KEGG/Reactome pathways", "GSEA", "over-representation", "functional annotation", or "what pathways are my genes in".

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Provides Therapeutics Data Commons workflows through PyTDC for registry discovery, dataset access, task-aware splits, evaluator metrics, benchmark groups, and bounded molecular-oracle scoring. Use when working with TDC therapeutic ML datasets or benchmarks.

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Looks up precomputed AlphaGenome Atlas effects for any GRCh38 single-nucleotide variant (AVI score with Phred and 18 SHAP feature attributions, plus raw and quantile scores for RNA-seq, DNase, ATAC, ChIP-TF, ChIP-histone, CAGE, PRO-cap, splicing, polyadenylation and contact-map tracks), scores variants or scans windows on demand with the AlphaGenome model for human and mouse (variant scoring, in silico mutagenesis, REF-versus-ALT track prediction), and builds Atlas website deep links. Use when the user mentions AlphaGenome, AlphaGenome Atlas, AVI or AlphaGenome Variant Impact, DeepMind variant effect prediction, or wants to prioritise or mechanistically interpret non-coding, regulatory, splicing, enhancer, promoter, or chromatin-accessibility effects of SNVs from a VCF, credible set, or region. Research use only; not a clinical tool.

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Complete automated literature discovery pipeline: multi-source search → six-dimension scoring → fine reading → formatted delivery → archival. Combines a configurable engine with daily cron-driven application layer. Works with Feishu, Telegram, or any messaging platform.

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Generate project-specific AGENTS.md and companion rules by analyzing a codebase. Supports full, minimal, update, and dry-run modes with package-manager detection, monorepos, backups, managed blocks, confidence scoring, and command validation.

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6 production-ready AI engineering workflows: prompt evaluation (8-dimension scoring), context budget planning, RAG pipeline design, agent security audit (65-point checklist), eval harness building, and product sense coaching.

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Test a learner on a single Claude Code tutorial lesson (01-10) with 10 questions, scoring answers and flagging weak spots. Use before, during, or after a lesson. Don't use for whole-tutorial assessment or explaining a topic instead of testing it.

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PluginEval quality methodology, covering dimensions, rubrics, and scoring formulas. Use this skill when understanding how plugin quality is measured, when interpreting a low score on a specific dimension, when deciding how to improve a skill's triggering accuracy or orchestration fitness, when setting score thresholds for your marketplace, or when explaining quality badges to external partners like Neon.

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Patterns and techniques for adding governance, safety, and trust controls to AI agent systems. Use this skill when: - Building AI agents that call external tools (APIs, databases, file systems) - Implementing policy-based access controls for agent tool usage - Adding semantic intent classification to detect dangerous prompts - Creating trust scoring systems for multi-agent workflows - Building audit trails for agent actions and decisions - Enforcing rate limits, content filters, or tool restrictions on agents - Working with any agent framework (PydanticAI, CrewAI, OpenAI Agents, LangChain, AutoGen)

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Run the AgentRC readiness assessment on the current repository and produce a static HTML dashboard at reports/index.html. Wraps `npx github:microsoft/agentrc readiness` and hands off rendering to the @ai-readiness-reporter custom agent. Supports policies (--policy) for org-specific scoring. Use when asked to assess, audit, or score the AI readiness of a repo.

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Multi-factor cross-sectional stock ranking. Combines factor standardization, equal-weight or IC-weighted scoring, and TopN portfolio construction. Suitable for multi-instrument portfolio strategies.

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Detects ransomware encryption activity in real time using entropy analysis, file system I/O monitoring (Sysmon, watchdog, psutil), and behavioral scoring to identify mass file modification, abnormal entropy spikes in written data, and suspicious process behavior characteristic of encryption routines. Use when building real-time ransomware detection, tuning entropy thresholds, or investigating suspected active encryption on an endpoint.

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Detect AI-generated deepfake audio used in voice phishing (vishing) by extracting spectral features (MFCC, spectral centroid, spectral contrast, zero-crossing rate) and classifying samples with machine learning models, supporting batch audio analysis, confidence scoring, and forensic reporting. Use for deepfake voice detection, vishing investigations, AI-generated speech analysis, voice cloning detection, or audio authenticity verification.

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Detects prompt injection using regex signature matching, heuristic scoring for structural anomalies, and DeBERTa-based transformer classification, flagging direct injections (system-prompt overrides, role-play escapes) and indirect injections (encoded payloads, obfuscation) per OWASP LLM Top 10 (LLM01:2025). Use for input validation layers in chatbots/agents/RAG pipelines, or for retrospectively classifying injection attempts in logs or incident investigations.

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Builds automated threat intelligence feed integration pipelines connecting STIX/TAXII feeds, open-source threat intel, and commercial TI platforms into SIEM and security tools for real-time IOC matching and alerting. Use when SOC teams need to operationalize threat intelligence by automating feed ingestion, normalization, scoring, and distribution to detection systems.

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Build an automated IOC enrichment pipeline on OpenCTI (STIX 2.1 native threat intel platform) using its internal enrichment connectors to pull context from VirusTotal, Shodan, AbuseIPDB, and GreyNoise, correlate indicators with known actors/campaigns, and score them for analyst prioritization. Use when deploying OpenCTI or automating enrichment and confidence scoring of newly ingested indicators.

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。