Results for “evidence-based”

Enforces running verification commands and confirming output before claiming work is complete, fixed, or passing, ensuring evidence precedes any success claims.

Evidence-based health research using tiered trusted sources with GRADE-inspired evidence ratings. Integrates Apple Health data for personalized context. Use when user asks health, nutrition, exercise, sleep, or wellness questions.

Runs evidence-first research workflows by combining search, synthesis, and recommendation skills for current facts, comparisons, or enrichment.

Review, triage, validate, and discuss ClawHub GitHub issues and pull requests with structured evidence, best-fix analysis, and maintainer handoff.

Advocate for design quality, scope, and time with cross-functional partners and leadership using evidence and shared goals.

Runs evidence-first research workflows, choosing the lightest useful path from search to deep synthesis, and labels facts, inference, and recommendations.

4-phase systematic debugging methodology with root cause analysis and evidence-based verification. Use when debugging complex issues.

Stops fantasy approvals, evidence-based certification - Default to "NEEDS WORK", requires overwhelming proof for production readiness

Create user acceptance test journeys from a target user's perspective, with role-based scenarios, acceptance criteria, and evidence capture

Use to assemble evidence that customers are getting good outcomes from support, at the level a board or regulator expects, rather than reporting activity metrics. Trigger for "evidence our customers get good outcomes", "consumer duty evidence from support", "board report on customer outcomes", outcomes-based regulatory reporting, or a request to show harm is not occurring.

Organize email inbox, draft replies, send messages, and verify delivery with evidence-based confirmation.

Conducts thorough landscape research, competitive analysis, best practices evaluation, and evidence-based recommendations. Expert in market research and trend analysis.

6-layer AI governance: safety gates, evidence-based debugging, anti-slack detection, and machine-enforced hooks. Makes AI safe, thorough, and honest.

Diagnose and root-cause issues with OCI Kubernetes Engine clusters and workloads through evidence-driven investigation.

Turn research inputs into concise evidence, patterns, confidence levels, and product decisions.

6-layer AI governance: safety gates, evidence-based debugging, anti-slack detection, and machine-enforced hooks. Makes AI safe, thorough, and honest.

Evidence-first current-state research workflow for ECC. Use when the user wants fresh facts, comparisons, enrichment, or a recommendation built from current public evidence and any supplied local context.

Runs evidence-first current-state research, comparing options and enriching people or companies, then reports with explicit evidence boundaries.

Turn a refined research proposal or method idea into a detailed, claim-driven experiment roadmap. Use after `research-refine`, or when the user asks for a detailed experiment plan, ablation matrix, evaluation protocol, run order, compute budget, or paper-ready validation that supports the core problem, novelty, simplicity, and any LLM / VLM / Diffusion / RL-based contribution.

Select evidence-based study strategies matched to material type, learning goal, and student habits. Use when advising students on revision techniques, homework, or independent study approaches.

Claim-driven 实验设计：界定目标 claims → 设计实验块（baseline/validation/ablation/robustness）→ 构建执行顺序 → 可选 Review LLM review → 写入 wiki

Enforces a disciplined, evidence-based debugging protocol. Use when the user reports a bug, an unexpected behavior, a broken test, or asks "why is X not working?". Prevents premature fixes and treats symptoms. Blocks code changes until a root cause is confirmed.

Mines a codebase for evidence-backed improvement opportunities, forcing two escalation gates to surface breakthrough ideas, and outputs a ranked portfolio with a plan seed without implementing.

Evaluate scientific claims and evidence quality by assessing experimental design, identifying biases and confounders, and applying evidence grading frameworks like GRADE and Cochrane Risk of Bias.

Formulate testable hypotheses from observations, design experiments, and generate predictions using a structured scientific method framework.

Research-backed evolution advice for your knowledge system. Analyzes health reports, friction patterns, and derivation history to propose specific changes with research justification. Never auto-implements — proposals require your approval.

Design and evaluate paid-ad experiments with hypotheses, randomization, sample-size calculations, guardrails, and decision rules for A/B and split tests.

Turn a refined research proposal or method idea into a detailed, claim-driven experiment roadmap. Use when the user asks for a detailed experiment plan, ablation matrix, evaluation protocol, run order, compute budget, or paper-ready validation that supports the core problem, novelty, simplicity, and any LLM / VLM / Diffusion / RL-based contribution.

基于已摄取文献和本地数据，生成经管实证研究设计：问题、机制、变量、模型、识别、机制、异质性、稳健性

以证据为先的ECC当前状态研究工作流程。当用户希望基于当前公开证据和提供的本地上下文获取最新事实、比较、丰富信息或建议时使用。