「scoring」の検索結果

Aggregate findings and generate multi-format pentest reports with remediation roadmap and risk scoring.

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GEO-first SEO analysis tool. Optimizes websites for AI-powered search engines (ChatGPT, Claude, Perplexity, Gemini, Google AI Overviews) while maintaining traditional SEO foundations. Performs full GEO audits, citability scoring, AI crawler analysis, llms.txt generation, brand mention scanning, platform-specific optimization, schema markup, technical SEO, content quality (E-E-A-T), and client-ready GEO report generation. Use when user says "geo", "seo", "audit", "AI search", "AI visibility", "optimize", "citability", "llms.txt", "schema", "brand mentions", "GEO report", or any URL for analysis.

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AI citability scoring and optimization. Analyzes web page content to determine how likely AI systems (ChatGPT, Claude, Perplexity, Gemini) are to cite or quote passages from the page. Provides a citability score (0-100) with specific rewrite suggestions.

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INTERNAL sub-agent, not a user-facing skill. Performs context-isolated nine-dimension rubric scoring for cheat-score, cheat-predict, and cheat-bump. Accepts only script and rubric-note paths, returns strict JSON, and refuses post-publish evidence.

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Produces a deep, honest peer-review of how someone uses Claude Code by statistically analyzing their local session data (~/.claude/usage-data/ and ~/.claude/projects/). Trigger whenever the user asks to review, analyze, audit, or critique their own Claude Code usage, workflow, habits, or skill level — including phrases like "analyze my cc usage", "review my cc sessions", "peer review my cc workflow", "deeper than /insights", or any ask for an honest audit of their AI workflow. Also covers portfolio/hiring-manager framings (e.g. "portfolio for Anthropic/OpenAI/xAI"), but ALWAYS ask the user in Step 0 which version to build (self / hr / both) before running — never silently produce an HR report, because that version goes to outsiders and needs explicit privacy setup. The HTML report is laid out ledger-first for SELF (opening band, output/team/leak/trend ledgers, then peer review, 9-dim scoring, try-this-week, case study, claim-indexed evidence). The HR variant is a recruiter version v1: identity card, earned badges (threshold-based, earned-only), allowlist-filtered output ledger, one case study, and a methodology & scope disclosure — no scores, no peer-review memo, no charts.

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AI-powered design review for Figma components with weighted dual-scoring system. Evaluates Style Guide Implementation (70%) and LLM Metadata Accessibility (30%). For export, hands off to atomic-design skill.

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Evaluate design from a UX perspective, assessing visual hierarchy, information architecture, emotional resonance, cognitive load, and overall quality with quantitative scoring, persona-based testing, and actionable feedback. Use when the user asks to review, critique, evaluate, or give feedback on a design or component.

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Evaluate design from a UX perspective, assessing visual hierarchy, information architecture, emotional resonance, cognitive load, and overall quality with quantitative scoring, persona-based testing, and actionable feedback. Use when the user asks to review, critique, evaluate, or give feedback on a design or component.

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Use when assessing AI/ML systems for prompt injection, jailbreak vulnerabilities, model inversion risk, data poisoning exposure, or agent tool abuse. Covers MITRE ATLAS technique mapping, injection signature detection, and adversarial robustness scoring.

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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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This skill should be used when the user asks to "implement LLM-as-judge", "compare model outputs", "create evaluation rubrics", "mitigate evaluation bias", or mentions direct scoring, pairwise comparison, position bias, evaluation pipelines, or automated quality assessment.

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Detects prompt injection attacks targeting LLM-based applications using a multi-layered defense combining regex pattern matching for known attack signatures, heuristic scoring for structural anomalies, and transformer-based classification with DeBERTa models. The detector analyzes user inputs before they reach the LLM, flagging direct injections (system prompt overrides, role-play escapes, instruction hijacking) and indirect injections (encoded payloads, multi-language obfuscation, delimiter-based escapes). Based on the OWASP LLM Top 10 (LLM01:2025 Prompt Injection) and Simon Willison's prompt injection taxonomy. Activates for requests involving prompt injection detection, LLM input sanitization, AI security scanning, or prompt attack classification.

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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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HTCMC PCHSystem 专用 skill——为各子服务目录生成与更新「分布式 CLAUDE.md」（提炼该服务的雷点、关键要素、文档索引）。当新建子服务、子服务架构发生变更、或需要补齐/更新某个服务的 CLAUDE.md 时使用。传入服务名（mcdr-plugin / user-service / project-service / scoring-service / title-service / wiki-service / alert-service / frontend），本 skill 自动读取对应架构文档，按统一模板生成或增量更新该服务目录下的 CLAUDE.md。在 PCHSystem 项目内，只要用户提到「子服务 CLAUDE.md」「为某服务生成/更新 CLAUDE.md」「补齐服务雷点」「服务记忆文件」，或正在新增/改动某个服务目录，就应使用本 skill——禁止手写子服务 CLAUDE.md。

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Review audit-related documents (control design documents, bottleneck analyses, requirements definitions, etc.) for quality, scoring them 0-100 with a severity-rated findings list. Use when reviewing audit documents, checking control design quality, or verifying cross-document consistency. Supports documents governed by US GAAP, IFRS, or J-GAAP.

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Professional contract review skill for business agreements including NDAs, MSAs, SLAs, SOWs, and software license agreements. Provides systematic clause-by-clause analysis, risk assessment with quantified scoring, red flag detection, and negotiation guidance. Use this skill when reviewing vendor contracts, partnership agreements, service agreements, or any business contract requiring risk evaluation and negotiation preparation. Triggers include "review this contract", "analyze this NDA", "check this agreement for risks", "prepare for contract negotiation", or when evaluating terms and conditions.

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Generic helpdesk first-response skill for creating KB-based response drafts. Use when handling support tickets, creating response templates, or building a structured helpdesk workflow. Supports error code detection, keyword matching, confidence scoring, multi-language templates, and escalation workflows. Customize by providing your own KB articles and configuration.

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Use when logging, scoring, and triaging a learner's persistent weak areas to drive intervention selection.

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Create a look-alike audience from a seed audience and a candidate population dataset. Classifies Rosetta Stone attributes, generates the same materialized-view scoring pipeline Lookalike Studio emits (Naive-Bayes categorical weights + Gaussian continuous similarity), gates on approval, submits via `narrative_workflows_create`, and monitors the build to completion. Use when: "create a lookalike audience", "find more users like this segment", "expand my seed audience to 500k similar users", "score the population against my customers", "build a look-alike of dataset X". (narrative-audience)

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Verify a tested web agent's real behavior from its recorded Pi or Codex session and selected screenshots before scoring. The judge prompt provides absolute artifact paths; use targeted queries to compare actual outputs, errors, and the final answer with the recorded behavior, then return only the verdict JSON. Only for ego-bench judging—never to drive a browser or rerun the agent.

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Knowledge base from NIST SP 800-171 Rev. 3 (Protecting CUI in Nonfederal Systems). Use for CUI confidentiality requirements on contractors and other nonfederal organizations, the 17 requirement families, ODPs, SSPs, POA&Ms, and tailoring from SP 800-53. Covers 800-171r3 only; does not replace SP 800-171A assessment procedures, full SP 800-53 baselines, CMMC scoring mechanics, or classified-information controls.

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Knowledge base from the DoD Risk, Issue, and Opportunity (RIO) Management Guide for Defense Acquisition Programs (OUSD R&E, Sep 2023 incl. Change 2.2). Use for the defense-acquisition mechanics of program risk management: the RIO definitions (risk/issue/opportunity), the five-step risk process (plan-identify-analyze-mitigate-monitor), 1-5 likelihood/consequence scoring and the 5x5 risk matrix, the four mitigation options (accept/avoid/transfer/control), burn-down plans, issue management (probability=1), opportunity management toward should-cost, cross-program/interface risk, tailoring RIO to the six AAF acquisition pathways (UCA/MTA/MCA/Software/DBS/Services), specialized methods (RMF, MBCRA/Cyber Table Top, Agile metrics, FMECA, ITRA/DTRAM), and institutionalizing RIO via the PRMP, boards (RMB/JRMB/ROMB/RWG), tools, tiered roles, and WBS/IMP/IMS/EVM/TPM integration. This is the DoD-acquisition complement to nasa-risk (general CRM/RIDM theory) and dau-se-guidebook (the SE process frame). Does NOT teach general risk theory from scratch, and defers specialized risk regimes (system safety/ESOH, HSI, detailed cyber/RMF, spectrum) to their own DoD instructions and the relevant packs.

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。

Structured decision making: Pro/Con matrix, weighted scoring, decision tree, scenario analysis, and Eisenhower matrix.

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。