Results for “loving-awareness”

当意识到自己处于迷茫、例行公事或心流等不同生活状态，并希望主动管理而非被动承受时

Use to identify signals of customer vulnerability in support conversations and audit whether they were recognised and acted on. Trigger for "identify vulnerable customers", "did we spot the vulnerability signals", "customers in financial difficulty", bereavement or health disclosures in support, vulnerable customer handling audits, or designing vulnerability recognition for a support team.

Finds, scores, and reaches high-value contacts through social graph analysis and warm path discovery, generating personalized outreach drafts for email, LinkedIn, and X.

Guides users through Nonviolent Communication to turn emotionally charged thoughts into constructive messages, structuring responses with observation, feeling, need, and request.

当感到生活失控、注意力分散，需要系统性重获专注力时

AI 原生的潜在客户情报和外联流水线。用 agent 驱动的信号评分、共同关系人排名、暖场路径发现、来源语音建模和多渠道外联（邮件、LinkedIn、X），替代 Apollo、Clay 和 ZoomInfo。在用户想找到、评估并联系高价值联系人时使用。

Provides a design system specification inspired by Lovable's website, including visual theme, color palette, typography rules, and component styling for building warm, approachable interfaces.

Map a current event below the surface into patterns, structures, and mental models. Use when a class or team needs systemic understanding before action.

Manages Loom video libraries and analyzes engagement via the Loom API, covering video discovery, folder organization, sharing settings, and viewer analytics.

Monitor and react to content distribution lifecycle events using the Sling Distribution API, covering event handling, queue monitoring, and distribution tracking.

Design a wise systems intervention from an existing analysis. Maps proposed actions against Meadows' leverage points, checks for unintended consequences, and generates alternatives.

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

Networking Outreach

Takes raw leads and enriches them to outreach-ready status. Chains company-researcher, cro-auditor, decision-maker-finder, apollo-lead-finder, email-validator, and personalization-enricher.

Build and query AI-powered knowledge bases from claude-mem observations, enabling focused conversational sessions on specific topics.

当需要进入高度专注、高效且愉悦的心流状态以完成创造性或学习性任务时

Slow down interpretation from observation to action. Use when students or adults need to examine assumptions in conflict, dialogue, or inquiry.

Analyzes recent conversation messages to identify a supporter personality type and generate a shareable identity card with tool recommendations.

Curating cross-agent knowledge and institutional memory: extracts patterns from agent journals into METAPATTERNS.md, detects knowledge decay, propagates best practices. Use for memory curation.

Guides daily mindfulness practice to reduce stress, improve focus, and build emotional regulation through structured routines and reflection.

When the user wants to create or optimize an email sequence, drip campaign, automated email flow, or lifecycle email program. Also use when the user mentions "email sequence," "drip campaign," "nurture sequence," "onboarding emails," "welcome sequence," "re-engagement emails," "email automation," or "lifecycle emails." For in-app onboarding, see onboarding-cro.

当需要建立可持续的创造节奏，将灵感转化为行动时

Turn "which leads should I work?" into a lead-by-lead work plan. Per-lead status (never-touched, engaged-not-replied, gone-cold, hot), days-dark per lead, last-touch quality, a re-engagement ranker, and a recommended next action for every lead. Built for SDRs and full-cycle reps, customizable to your CRM and your outreach tool. Trigger on "which leads should I work?", "who haven't I touched?", "show me cold leads", "hot leads to follow up on", "who's gone dark?", "re-engagement candidates", or any lead-level work-assignment question.

Create and optimize popups, modals, overlays, slide-ins, and banners to increase conversions without harming user experience or brand trust.

Interactive project learning coach via interview-style Q&A. Reads codebase and docs, dynamically generates interview questions per knowledge domain and sub-topic, conducts up to 4 follow-up rounds, scores answers, provides learning guidance with code/doc references, and persists progress. 10 domains × 3-5 sub-topics = 45 knowledge points for comprehensive interview coverage. Use when user says '学习项目', '了解项目', '检验项目', '项目学习', '面试准备', 'learn project', 'study project', 'review project', 'interview prep', 'knowledge check', or wants to understand/master the project through guided Q&A.

Compassionate bereavement support, memorial creation, grief education, and healing journey guidance. Specializes in understanding grief stages, creating meaningful tributes, and supporting the non-linear path of loss.

When the user wants to create or optimize an email sequence, drip campaign, automated email flow, or lifecycle email program. Also use when the user mentions "email sequence," "drip campaign," "nurture sequence," "onboarding emails," "welcome sequence," "re-engagement emails," "email automation," or "lifecycle emails." For in-app onboarding, see onboarding-cro.

Points developers to the canonical WordPress Presence API sources and explains its ephemeral-table-plus-TTL architectural pattern, so they avoid inventing postmeta-based presence implementations.

Looker Studio (formerly Google Data Studio) expertise. Build dashboards, design data visualizations, connect data sources, and create marketing reports. Use when the user asks about Looker Studio, Data Studio, marketing dashboards, data visualization, report building, or connecting analytics data sources.

Designs effective alerting and incident management for production services, covering alert rules, on-call rotations, runbooks, and incident response workflows.

Provides a framework for proactive health monitoring and optimization, treating the human body as an asset to be maintained through telemetry, diagnostics, and interventions.

Evaluates LLMs across 60+ academic benchmarks (MMLU, HumanEval, GSM8K, TruthfulQA, HellaSwag). Use when benchmarking model quality, comparing models, reporting academic results, or tracking training progress. Industry standard used by EleutherAI, HuggingFace, and major labs. Supports HuggingFace, vLLM, APIs.

Internal codebase knowledge-check assistant for maintainers. Focus on architecture understanding, implementation details, and change-risk analysis for this repository.

When the user wants to reduce churn, build cancellation flows, set up save offers, recover failed payments, or implement retention strategies. Also use when the user mentions 'churn,' 'cancel flow,' 'offboarding,' 'save offer,' 'dunning,' 'failed payment recovery,' 'win-back,' 'retention,' 'exit survey,' 'pause subscription,' 'involuntary churn,' 'people keep canceling,' 'churn rate is too high,' 'how do I keep users,' or 'customers are leaving.' Use this whenever someone is losing subscribers or wants to build systems to prevent it. For post-cancel win-back email sequences, see emails. For in-app upgrade paywalls, see paywalls.

Optimizes transformer attention with Flash Attention for 2-4x speedup and 10-20x memory reduction. Supports PyTorch native SDPA, flash-attn library, H100 FP8, and sliding window attention.