Lead nurture orchestration - multi-track nurture design by intent stage, scoring, behavioral triggers, MQL→SQL handoff, and revival of cold leads. Use when: lead nurture, drip campaign, nurture track, lead scoring, MQL to SQL, lifecycle marketing, nurture sequence, behavioral trigger, lead revival, MQL handoff.
Audits how likely AI answer engines (ChatGPT, Claude, Perplexity, Gemini, Google AI Overviews) are to cite your pages for the questions buyers ask, then ranks fixes by citation lift. Use when: ai search visibility, generative engine optimization, geo audit, aeo, llm citations, are we cited in ai answers, ai overviews…
Interviews the team once and writes a single GTMCONTEXT.md (ICP, positioning, voice, proof, competitors) that every other GTM skill reads, then scores how fresh and complete it is. Use when: set up gtm context, company context file, shared positioning file, brand context, icp file, onboard the gtm skills, context is…
Stress-tests a live opportunity by separating what the buyer has proven from what the rep believes, then scores deal risk and names the next action that would change the forecast. Use when: deal review, deal risk, pipeline review, forecast call, is this deal real, qualify this opportunity, stalled deal, commit or…
Carves accounts into sales territories that are fair on opportunity, not just account count, and flags coverage gaps and overloaded reps before the quarter starts. Use when: territory planning, territory design, carve territories, rebalance accounts, sales capacity planning, quota fairness, coverage model, account…
Builds outbound sequences that start from a specific buying trigger (funding, hiring, tech change, leadership move, product usage) instead of a static persona list, with a routing table that picks the cadence for each trigger. Use when: outbound sequence, cold email cadence, signal-based outbound, trigger-based…
Turns closed-won and closed-lost deals into ranked, evidence-backed reasons (not rep-selected dropdowns) and routes each pattern to the team that can fix it. Use when: win loss analysis, why did we lose, why do we win, closed lost review, loss reasons, churned deals, competitive losses, buyer interviews.
Design user experiences for autonomous AI agents that act on behalf of users - control panels, consent flows, action previews, audit trails, and undo mechanisms. Use when: agentic AI, autonomous agents, AI autonomy controls, agent UX, AI actions, computer use, agent consent, agent audit trail.
Design conversational AI interfaces that feel natural, recover from failures gracefully, and build user trust through dialogue. Use when: chatbot UX, conversational UI, dialogue design, AI assistant interface, chat flow, turn-taking, AI persona voice, multi-turn context.
Design graceful failure experiences for AI products - hallucinations, uncertainty, wrong outputs, and edge cases. Use when: AI hallucination UX, error handling for AI, uncertainty design, graceful degradation, AI failure recovery, confidence thresholds, safe fallbacks.
Design feedback mechanisms that help AI systems learn from users - thumbs up/down, preference ranking, corrections, and human-in-the-loop escalation. Use when: RLHF UX, user feedback for AI, thumbs up down design, AI correction flow, human in the loop, feedback signal design, AI improvement loops.
Map human-AI interaction journeys - trust arcs, capability discovery paths, autonomy progression, and AI-specific touchpoints. Use when: AI user journey, human-AI interaction mapping, trust arc mapping, AI capability discovery, AI adoption journey, AI experience map.
Design how AI presents results across text, code, images, charts, and mixed media - response formatting, output hierarchy, and cross-modal transitions. Use when: AI output design, response formatting, AI results display, multimodal output, AI-generated content presentation, code output UX, AI visualization.
Design onboarding experiences that help users build accurate mental models of AI capabilities, set expectations, and discover features progressively. Use when: AI onboarding, progressive disclosure for AI, capability communication, AI mental models, expectation setting, AI feature discovery, first-time AI user…
Design AI-driven personalization that adapts interfaces to users while respecting privacy, avoiding filter bubbles, and maintaining user agency. Use when: adaptive UI, AI personalization, recommendation UX, filter bubble prevention, privacy personalization balance, algorithmic fairness UX, user preference learning.
Design the input experience for AI products - how users craft, structure, and refine their instructions to AI systems. Use when: prompt interface, AI input design, prompt templates, prompt suggestions, context window UX, instruction design, AI input affordances, prompt engineering UX.
At most 3 mods per repository are shown here, and a mod shipped inside a plugin is left to that plugin's page — the rest are on their repository pages: