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Found 145 Skills
Evaluate, optimize, and enhance prompts using 58 proven prompting techniques. Use when user asks to improve, optimize, or analyze a prompt; when a prompt needs better clarity, specificity, or structure; or when generating prompt variations for different use cases. Covers quality assessment, targeted improvements, and automatic optimization across techniques like CoT, few-shot learning, role-play, and 50+ more.
This skill should be used when the user asks to "review PR and save results", "run PR review with documentation", "create PR review document", "review and document PR", "save PR review to docs", "document PR review", or mentions reviewing a PR with the intention of saving the review results. Executes comprehensive PR review using pr-review-toolkit with opus model and posts results as a PR comment.
Detectar sobreexposición, subexposición e iluminación desigual en frames capturados
This skill should be used when the user asks to "analyze skill quality", "evaluate this skill", "review skill quality", "check my skill", or "generate quality report". Evaluates local skills across description quality, content organization, writing style, and structural integrity.
Gold-standard code review for SAP CC Go repositories against the project's lead review standards. Dispatches 10 domain-specialist agents in parallel — each loads domain-specific references and scans ALL packages for violations in their assigned domain. Produces a prioritized report with REJECTED/CORRECT code examples. Optional --fix mode applies corrections on a worktree branch. This is the definitive "would this code pass lead review?" assessment.
Comprehensive memory quality review across 6 dimensions: purity, freshness, coverage, clarity, relevance, and structure. Generates prioritized findings with specific memory references and actionable recommendations.
Comprehensive code review checklist for Go projects. Evaluates code quality, idiomatic patterns, error handling, naming, package structure, and test coverage. Use when reviewing Go code, PRs, or before merging changes. Trigger examples: "review this code", "check this PR", "code review", "review Go file". Do NOT use for security-specific audits (use go-security-audit) or performance-specific analysis (use go-performance-review).
Evaluates Claude Agent Skills on 10 quality axes with letter grades (A+ through F) and specific improvement recommendations. Use when auditing a skill, comparing skills, prioritizing improvements, or performing quality control on a skill library. Activate on "grade skill", "evaluate skill", "skill quality", "skill audit", "skill review", "rate skill". NOT for creating skills (use skill-architect), grading code quality, or evaluating non-skill documents.
Objective task quality evaluation framework using quantitative KPIs. KPIs are automatically calculated by a hook when task files are modified and saved to TASK-XXX--kpi.json. Use when: reading KPI data for task evaluation, understanding quality metrics, deciding whether to iterate or approve based on data.
Evaluate the quality of CAW (Cobo Agentic Wallet) Agent in local Claude Code, and generate scoring data and analysis reports. Use when: Users want to run CAW evaluation, conduct evaluation, test Skill, assess Agent quality, generate evaluation reports, or say "run evaluation", "evaluate CAW", "eval", "score". For weak model / openclaw evaluation, please use caw-eval-openclaw (only installed on openclaw servers).
Deep financial statement analysis for listed companies via Longbridge — cross-statement reconciliation (IS↔BS↔CF), DuPont decomposition (ROE = net margin × asset turnover × equity multiplier), earnings-quality scoring (accrual ratio), and 10-item financial fraud red-flag checklist. Builds on raw data from longbridge-financial-report. Triggers: "三表勾稽", "杜邦分析", "杜邦拆解", "盈利质量", "应计利润", "财务造假", "财报深度", "财务红旗", "三表分析", "財務深度", "三表勾稽", "杜邦分析", "盈利質量", "應計利潤", "財務造假", "財報深度", "財務紅旗", "DuPont analysis", "accrual ratio", "earnings quality", "financial fraud red flags", "cross-statement reconciliation", "three-statement analysis".
Bootstrap evaluators from production traces — emit SDK code, a framework-agnostic JSON spec, or publish online LLM-judge evaluators directly to Datadog. Use when user says "bootstrap evaluators", "generate evaluators", "create evals from traces", "eval bootstrap", "write evaluators", "build eval suite", "publish evaluators", or wants to generate BaseEvaluator/LLMJudge code or online judge configs from production LLM trace data. Works with ml_app and optional RCA report or failure hypothesis.