Results for “evidence-based-advocacy”

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

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.

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.

Use when adapting academic findings for a public audience (op-ed, social post, podcast) and the simplification must stay evidence-faithful, avoid overclaiming, respect embargoes, and disclose AI assistance.

Orchestrates evidence-based research across local PDFs, scholarly search, citation-context tools, web sources, and code repositories to produce traceable syntheses with counterevidence audits.

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.

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

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

Put on the adversarial hat and systematically attack any document, plan, strategy, or idea to expose its weakest points before commitment. Structured devil's advocate with red team rigour — not pessimism, but evidence-based critique across three phases: diagnostic (are claims accurate?), creative (is the problem artificially constrained?), challenge (are solutions robust?). Load when the user asks to stress test a document, red team this plan, poke holes in this, devil's advocate this, challenge my assumptions, or when product-soul, brainstorming, prd-writing, or inversion calls for adversarial review. Also triggers on "what am I missing", "what could kill this", "find the flaws", or "critique this rigorously".

Turn happy customers into references, case studies, and referrals, on purpose instead of by accident. Reads who is happy enough to ask, picks the right ask, sets the value exchange, and builds a pipeline of advocacy over time. Built for B2B customer success teams, customizable to your product and your program. Trigger on "build an advocacy program", "who can be a reference", "find case study candidates", "ask for a referral", "customer advocacy pipeline", or any reference-and-referral planning.

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.

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.

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.

Apply six decades of persuasion science—Cialdini's principles of reciprocity, commitment, social proof, authority, liking, scarcity, and unity—to product design, copy, and sales, ethically.

Draft an internal legal research memo for the supervising attorney with verified authority and flagged open questions.

Generate paid-ad campaign concepts, messaging, copy, creative briefs, and production plans grounded in a validated brand profile, campaign objective, and platform requirements.

Validate Pleasur.ai Stage 01 blog-keyword candidates with the Business potential, Intent, and Difficulty method using current Ahrefs evidence. Use before AIO vetting and prioritization to reject poor business fits, mismatched search intent, and SERPs the brand cannot realistically win.

Map customer journeys from awareness to advocacy by visualizing touchpoints, identifying drop-off points, analyzing channel interactions, and optimizing the end-to-end experience.

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

Conducts exhaustive multi-source research on a topic, tagging each claim with source class and confidence, and includes a mandatory adversarial counterpoint and open-questions list.

Stress-test a feature idea for an existing product by surfacing risky assumptions across Value, Usability, Viability, and Feasibility using multi-perspective devil's advocate thinking.

VS-Enhanced Qualitative Design Consultant with Ethnography & Action Research Enhanced VS 3-Phase process: Avoids overused phenomenology, proposes context-optimal qualitative strategies Absorbed H1 (Ethnographic Research Advisor) and H2 (Action Research Facilitator) capabilities Use when: selecting qualitative research design, planning phenomenology/grounded theory/case study/ethnography/action research Triggers: phenomenology, 현상학, grounded theory, 근거이론, case study, 사례연구, narrative inquiry, ethnography, 민족지, action research, 실행연구, qualitative design

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

Build an evidence-backed ECC install plan for a specific repo by sorting skills, commands, rules, hooks, and extras into DAILY vs LIBRARY buckets using parallel repo-aware review passes. Use when ECC should be trimmed to what a project actually needs instead of loading the full bundle.

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

VS-Enhanced Theoretical Framework Architect with Critique & Visualization Full VS 5-Phase process: Modal theory avoidance, Long-tail exploration, differentiated framework presentation Absorbed A3 (Devil's Advocate) critique and A6 (Conceptual Framework Visualizer) capabilities Use when: building theoretical foundations, designing conceptual models, deriving hypotheses, critiquing frameworks, visualizing models Triggers: theoretical framework, 이론적 프레임워크, conceptual model, 개념적 모형, hypothesis derivation, critique, devil's advocate, 반론, visualization, diagram

Generate professional clinical decision support documents for pharmaceutical and clinical research, including biomarker-stratified cohort analyses and evidence-based treatment recommendation reports with GRADE grading, statistical analysis, and publication-ready LaTeX/PDF output.

Refreshes paid media research by auditing platform claims, API policies, and evidence against primary sources, demoting stale or unsupported assertions.

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。

Verifies claims, articles, screenshots, and URLs through source-grounded analysis with an evidence ledger, source credibility evaluation, and manipulation detection. Supports quick checks, full fact-check cards, two-source comparisons, and prebunking in multiple languages and policy contexts.

Diagnose paid-ad campaigns and draft or apply optimizations using evidence, financial constraints, and experiments. Supports budget reallocation, bid changes, creative rotation, and CPA/ROAS improvement.

Compile deep repository evidence into artifact-bound correctness doctrine, including authority/law/proof maps, owned invariants, failure archaeology, and knowledge routing, with an optional minimal repository-specific skill portfolio.

Use to assemble a fair, evidence-backed coaching pack for a support agent's one-to-one from QA evaluations and conversation history. Trigger for "prepare a coaching session for X", "what areas does X need to improve", "areas of markdown for this agent", "what coaching opportunities stand out", "build a coaching agenda from these tickets", or preparing a weekly or monthly agent review.

Skeptical pushback on the research dossier before it feeds the outline. Asks whether claims are cited, whether surprising findings are actually surprising, whether we missed the strongest competitor angle. One revision pass on FAIL (BLOG_AGENT_RESEARCH_REVISION_BUDGET, default 1).

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.