「score」の検索結果

Run convex-doctor static analysis, interpret findings, and fix issues across security, performance, correctness, schema, and architecture categories. Use when running convex-doctor, fixing convex-doctor warnings or errors, improving the convex-doctor score, or when asked about Convex code quality, static analysis, or linting Convex functions.

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Static analysis checklist for Convex backends covering 72 rules across security, performance, correctness, schema, architecture, configuration, and client-side patterns. Use when writing, reviewing, or auditing Convex code. Trigger on mentions of "convex-doctor", "health score", "static analysis", "anti-patterns", "audit convex", or before shipping backend 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。

Run every Convex audit (authz, reviewer, advisor, insights) into one scored, deduped readiness report with an ordered fix plan — Lighthouse for your backend.

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 websites for SEO, performance, security, technical, content, and 15 other issue cateories with 230+ rules using the squirrelscan CLI. Returns LLM-optimized reports with health scores, broken links, meta tag analysis, and actionable recommendations. Use to discover and asses website o...

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。

The AI native file format. EXIF for AI — stamps every file with trust scores, source provenance, and compliance metadata. Embeds into 20+ formats (DOCX, PDF, images, code). EU AI Act, SOX, HIPAA auditing.

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 after completing any non-trivial task. The agent self-rates its output on 5 axes — accuracy, completeness, clarity, actionability, conciseness — with concrete evidence per criterion. Produces a structured 1-5 scorecard with specific improvement suggestions.

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Implement User and Entity Behavior Analytics using Elasticsearch/OpenSearch to build behavioral baselines, calculate anomaly scores, perform peer group analysis, and detect insider threat indicators such as data exfiltration, privilege abuse, and unauthorized access patterns.

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Detects AI-generated deepfake audio used in voice phishing (vishing) attacks by extracting spectral features (MFCC, spectral centroid, spectral contrast, zero-crossing rate) and classifying samples with machine learning models. Supports batch analysis of audio files, generates confidence scores, and produces forensic reports. Activates for requests involving deepfake voice detection, vishing investigation, AI-generated speech analysis, voice cloning detection, or audio authenticity verification.

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Conduct a defensible cybersecurity risk assessment using the NIST SP 800-30 Rev 1 methodology: prepare scope and a risk model, identify threat sources and threat events, identify vulnerabilities and predisposing conditions, determine likelihood and impact, compute risk, and communicate results as a prioritized risk register. Use when an organization needs an actual risk *assessment* (not a maturity score), when a control framework (CSF, ISO 27001, RMF, SOC 2, PCI) requires a documented risk analysis as input, when leadership asks "what are our top risks and how bad are they", when assessing risk for a new system or major change, or when building a risk register from scratch. This is the methodology that feeds framework selection, ATO packages, and treatment decisions. Keywords: risk assessment, NIST 800-30, threat modeling, likelihood and impact, risk register, risk analysis, threat sources, vulnerabilities, risk determination, qualitative risk, risk matrix, residual risk, risk treatment.

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Parses Software Bill of Materials (SBOM) in CycloneDX and SPDX JSON formats to identify supply chain vulnerabilities by correlating components against the NVD CVE database via the NVD 2.0 API. Builds dependency graphs, calculates risk scores, identifies transitive vulnerability paths, and generates compliance reports. Activates for requests involving SBOM analysis, software composition analysis, supply chain security assessment, dependency vulnerability scanning, CycloneDX/SPDX parsing, or CVE correlation.

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 websites for SEO, performance, security, technical, content, and 15 other issue cateories with 230+ rules using the squirrelscan CLI. Returns LLM-optimized reports with health scores, broken links, meta tag analysis, and actionable recommendations. Use to discover and asses website or webapp issues and health.

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。

Mine Claude Code session logs for skill idea candidates. Use when running the skill generation pipeline to extract, score, and backlog new skill ideas from recent coding sessions.

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Review skills in any project using a dual-axis method: (1) deterministic code-based checks (structure, scripts, tests, execution safety) and (2) LLM deep review findings. Use when you need reproducible quality scoring for `skills/*/SKILL.md`, want to gate merges with a score threshold (for example 90+), or need concrete improvement items for low-scoring skills. Works across projects via --project-root.

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。

IT要求・活動のインパクトスコアを算出

価値概念スコアの良い点・悪い点を分析し、改善点と改善のための問いを生成

価値概念との乖離を検証

IT要求・活動インパクトスコアの良い点・悪い点を分析し、改善点と改善のための問いを生成

価値概念のリストアップとインパクト評価を実行

価値概念とIT要求・活動のスコア乖離検証結果の良い点・悪い点を分析し、改善のための問いを生成

検証結果に基づいて価値概念スコアを洗練・修正する

検証結果に基づいてIT要求・活動インパクトスコアを洗練・修正する

検証結果に基づいてスコア乖離検証結果を洗練・修正する

スコア乖離検証結果の検証を行う時の観点を説明する

IT要求・活動インパクトスコアの検証を行う時の観点を説明する