[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6MBZ649917VWHFDS8M7DWCC":3,"skill-same:01M6MBZ649917VWHFDS8M7DWCC":98},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":16,"categories":17,"keywords":23,"publishedAt":45,"linkOnly":46,"markdownContent":47,"markdownBody":48,"frontMatter":49,"refName":56,"commitSha":57,"skillDirectory":58,"forksCount":59,"repositoryHtmlUrl":60,"repositoryHomepageUrl":61,"ownerHtmlUrl":62,"ownerBlogUrl":63,"ownerDescription":64,"updatedAt":65,"install":66,"resources":79},"01M6MBZ649917VWHFDS8M7DWCC","customer-discovery-conversations",null,"Plan and audit customer discovery conversations that produce concrete evidence instead of compliments, opinions, hypotheticals, or feature-request noise. Use when planning customer interviews, rewriting interview scripts, validating startup ideas before building, avoiding biased questions, or applying Mom Test-style customer learning.","hashgraph-online\u002Fawesome-codex-plugins","hashgraph-online","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F160371962?v=4","01M6CNB6NRJVNNGN14TR01Q6D3","HOL (Hashgraph Online)","01M6JT1HSKKG9V68KQAQGBA7XP",1559,"MIT","https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002FLVTD-LLC\u002Fskills\u002Fskills\u002Fcustomer-discovery-conversations\u002FSKILL.md",[18],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000023","Skill与智能体开发","SCENARIO","使用场景",[24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44],"customer discovery conversations","discovery","conversations","customer discovery","customer","startups","when","request","interviews","customer-discovery","scripts","feature","ideas","evidence","interview","before building","marketing","plan","audit","planning","code","2026-11-10 10:12:30",false,"---\nname: customer-discovery-conversations\ndescription: Plan and audit customer discovery conversations that produce concrete evidence instead of compliments, opinions, hypotheticals, or feature-request noise. Use when planning customer interviews, rewriting interview scripts, validating startup ideas before building, avoiding biased questions, or applying Mom Test-style customer learning.\nlicense: MIT\ncompatibility: Codex, Claude Code, and other Agent Skills-compatible clients.\nmetadata:\n  version: \"0.1.3\"\n  displayName: Customer Discovery Conversations\n  category: Marketing\n  tags: customer-discovery,customer-interviews,startups,validation,research\n---\n\n# Customer Discovery Conversations\n\nUse this skill to turn customer conversations into decision-quality evidence.\nThe goal is not approval for an idea. The goal is to learn concrete facts about\ncustomers' lives, current behavior, constraints, costs, workarounds, and risks.\n\n## Source Traceability\n\nPrimary source: The Mom Test by Rob Fitzpatrick, especially chapters 1-4 and 8.\nGuidance is paraphrased for this MIT repo; authoring notes used converted EPUB\nlines 236-2362 and 3613-4108.\n\n## Workflow Routing\n\n| Need | Use |\n|------|-----|\n| Rewrite an interview script | `workflows\u002Frewrite-interview-questions.md` |\n| Run a batch of conversations | `workflows\u002Frun-conversation-batch.md` |\n| Recover from compliments, hypotheticals, or ideas | `workflows\u002Frecover-bad-data.md` |\n| Segment is too broad | `customer-segment-slicing` |\n| Product or sales meeting needs a real next step | `customer-commitment-validation` |\n| Raw notes need synthesis | `customer-learning-notes` |\n\n## Conversation Workflow\n\n1. State the decision the conversations should improve.\n2. Name the riskiest assumptions, including at least one uncomfortable question.\n3. Choose the narrow customer or stakeholder type. If the segment is fuzzy, use\n   `customer-segment-slicing` first.\n4. Prepare up to three learning goals for this type of person.\n5. Rewrite questions toward current behavior and specific past examples.\n6. Keep the conversation about the customer until there is enough evidence to\n   discuss a solution.\n7. During the conversation, anchor vague claims to recent examples and dig into\n   current costs, workarounds, constraints, and goals.\n8. Capture notes, signals, and follow-up tasks for review.\n\n## Question Rules\n\n- Talk about the customer's life, work, tools, constraints, and recent past.\n- Ask scary questions early enough that the answer can still change the plan.\n- Turn opinions, compliments, and hypotheticals into requests for examples.\n- Ask what already costs them time, money, reputation, or operational pain.\n- Save the pitch until the customer evidence justifies discussing a solution.\n\n## Good Evidence\n\nPrefer recent examples, current workarounds, money\u002Ftime\u002Freputation already\nspent, existing tools and budgets, named stakeholders, decision paths, and strong\nemotion backed by a concrete story.\n\nTreat compliments, opinions, future promises, generic claims, and feature\nrequests without motivation as weak evidence.\n\n## Output Format\n\n```markdown\n# Customer Discovery Plan\n\n## Context\n- Decision:\n- Segment:\n- Risk questions:\n\n## Conversation Guide\n| Goal | Ask About | Avoid | Follow-Up |\n|------|-----------|-------|-----------|\n\n## Evidence To Capture\n- Current behavior, past examples, workarounds:\n- Costs, budget, constraints, stakeholders:\n- Strong emotion, follow-up, commitment:\n\n## Review Criteria\n- What changed in our beliefs?\n- What is still unknown?\n- What are the next three questions?\n```\n\n## Quality Bar\n\n- Do not help users collect approval or compliments.\n- Do not ask customers to design the product for the team.\n- Do not treat \"I would buy this\" as validation without commitment.\n- Do not run conversations without a decision, segment, and learning goals.\n- Prefer fewer high-value conversations over many generic interviews.\n","# Customer Discovery Conversations\n\nUse this skill to turn customer conversations into decision-quality evidence.\nThe goal is not approval for an idea. The goal is to learn concrete facts about\ncustomers' lives, current behavior, constraints, costs, workarounds, and risks.\n\n## Source Traceability\n\nPrimary source: The Mom Test by Rob Fitzpatrick, especially chapters 1-4 and 8.\nGuidance is paraphrased for this MIT repo; authoring notes used converted EPUB\nlines 236-2362 and 3613-4108.\n\n## Workflow Routing\n\n| Need | Use |\n|------|-----|\n| Rewrite an interview script | `workflows\u002Frewrite-interview-questions.md` |\n| Run a batch of conversations | `workflows\u002Frun-conversation-batch.md` |\n| Recover from compliments, hypotheticals, or ideas | `workflows\u002Frecover-bad-data.md` |\n| Segment is too broad | `customer-segment-slicing` |\n| Product or sales meeting needs a real next step | `customer-commitment-validation` |\n| Raw notes need synthesis | `customer-learning-notes` |\n\n## Conversation Workflow\n\n1. State the decision the conversations should improve.\n2. Name the riskiest assumptions, including at least one uncomfortable question.\n3. Choose the narrow customer or stakeholder type. If the segment is fuzzy, use\n   `customer-segment-slicing` first.\n4. Prepare up to three learning goals for this type of person.\n5. Rewrite questions toward current behavior and specific past examples.\n6. Keep the conversation about the customer until there is enough evidence to\n   discuss a solution.\n7. During the conversation, anchor vague claims to recent examples and dig into\n   current costs, workarounds, constraints, and goals.\n8. Capture notes, signals, and follow-up tasks for review.\n\n## Question Rules\n\n- Talk about the customer's life, work, tools, constraints, and recent past.\n- Ask scary questions early enough that the answer can still change the plan.\n- Turn opinions, compliments, and hypotheticals into requests for examples.\n- Ask what already costs them time, money, reputation, or operational pain.\n- Save the pitch until the customer evidence justifies discussing a solution.\n\n## Good Evidence\n\nPrefer recent examples, current workarounds, money\u002Ftime\u002Freputation already\nspent, existing tools and budgets, named stakeholders, decision paths, and strong\nemotion backed by a concrete story.\n\nTreat compliments, opinions, future promises, generic claims, and feature\nrequests without motivation as weak evidence.\n\n## Output Format\n\n```markdown\n# Customer Discovery Plan\n\n## Context\n- Decision:\n- Segment:\n- Risk questions:\n\n## Conversation Guide\n| Goal | Ask About | Avoid | Follow-Up |\n|------|-----------|-------|-----------|\n\n## Evidence To Capture\n- Current behavior, past examples, workarounds:\n- Costs, budget, constraints, stakeholders:\n- Strong emotion, follow-up, commitment:\n\n## Review Criteria\n- What changed in our beliefs?\n- What is still unknown?\n- What are the next three questions?\n```\n\n## Quality Bar\n\n- Do not help users collect approval or compliments.\n- Do not ask customers to design the product for the team.\n- Do not treat \"I would buy this\" as validation without commitment.\n- Do not run conversations without a decision, segment, and learning goals.\n- Prefer fewer high-value conversations over many generic interviews.\n",{"name":5,"license":15,"metadata":50,"description":7,"compatibility":55},{"tags":51,"version":52,"category":53,"displayName":54},"customer-discovery,customer-interviews,startups,validation,research","0.1.3","Marketing","Customer Discovery Conversations","Codex, Claude Code, and other Agent Skills-compatible clients.","main","2b63f856ce19c2a797929cacabd9e0fc936812fa","plugins\u002FLVTD-LLC\u002Fskills\u002Fskills\u002Fcustomer-discovery-conversations",474,"https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins","https:\u002F\u002Fhol.org\u002Fplugins\u002Fbest-codex-plugins","https:\u002F\u002Fgithub.com\u002Fhashgraph-online","https:\u002F\u002Fhol.org","Open-source standards and developer tools for interoperable AI agents: discovery, identity, communication, payments, privacy and security.","2026-11-11 14:40:14",{"source":8,"skillName":5,"commands":67,"prompt":77,"zipAvailable":78},[68,71,74],{"tool":69,"command":70},"npx","npx skills add hashgraph-online\u002Fawesome-codex-plugins --skill customer-discovery-conversations",{"tool":72,"command":73},"bunx","bunx skills add hashgraph-online\u002Fawesome-codex-plugins --skill customer-discovery-conversations",{"tool":75,"command":76},"pnpm","pnpm dlx skills add hashgraph-online\u002Fawesome-codex-plugins --skill customer-discovery-conversations","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002FLVTD-LLC\u002Fskills\u002Fskills\u002Fcustomer-discovery-conversations\u002FSKILL.md",true,[80,86,90,94],{"path":81,"resourceType":82,"resourceTypeDesc":83,"sizeBytes":84,"sourceHtmlUrl":85},"agents\u002Fopenai.yaml","REFERENCE","参考文档",208,"https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002FLVTD-LLC\u002Fskills\u002Fskills\u002Fcustomer-discovery-conversations\u002Fagents\u002Fopenai.yaml",{"path":87,"resourceType":82,"resourceTypeDesc":83,"sizeBytes":88,"sourceHtmlUrl":89},"workflows\u002Frecover-bad-data.md",1010,"https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002FLVTD-LLC\u002Fskills\u002Fskills\u002Fcustomer-discovery-conversations\u002Fworkflows\u002Frecover-bad-data.md",{"path":91,"resourceType":82,"resourceTypeDesc":83,"sizeBytes":92,"sourceHtmlUrl":93},"workflows\u002Frewrite-interview-questions.md",1543,"https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002FLVTD-LLC\u002Fskills\u002Fskills\u002Fcustomer-discovery-conversations\u002Fworkflows\u002Frewrite-interview-questions.md",{"path":95,"resourceType":82,"resourceTypeDesc":83,"sizeBytes":96,"sourceHtmlUrl":97},"workflows\u002Frun-conversation-batch.md",1342,"https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002FLVTD-LLC\u002Fskills\u002Fskills\u002Fcustomer-discovery-conversations\u002Fworkflows\u002Frun-conversation-batch.md",[99,108,115,122,148,173],{"skillNo":100,"name":101,"nameZh":6,"description":102,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":103,"licenseFileUrl":6,"sourceHtmlUrl":104,"categories":105,"keywords":106,"publishedAt":107},"01M746ZWACVKMZ1BJXJZHYJSSX","migrate-to-e2e","Moves an existing Playwright, Cypress, Selenium, Appium, Detox, Maestro, or TesterArmy suite to e2e one slice at a time, with the old suite running beside the new one until each ported test is proven. Keeps a MIGRATION.md progress file so any later session resumes where the last stopped. Optionally plants a defect to show both suites catch it. Use for \u002Fmigrate-to-e2e, \"migrate our tests to e2e\", \"port these Maestro flows\", \"move our TesterArmy tests\", or when a project has an e2e config and an older suite still running.","Apache-2.0","https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002Ftester-army\u002Fe2e\u002Fskills\u002Fmigrate-to-e2e\u002FSKILL.md",[],[],"2026-11-11 15:11:14",{"skillNo":109,"name":110,"nameZh":6,"description":111,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":103,"licenseFileUrl":6,"sourceHtmlUrl":112,"categories":113,"keywords":114,"publishedAt":107},"01M746ZPTAX1Q0FBKAB1CYND7E","e2e","Writes, runs, and debugs end-to-end tests for web and mobile apps with e2e, the agentic test runner, which mixes agent.act goals with exact locator assertions on a Playwright browser or an iOS or Android device. Also drives a project's running app live over the e2e MCP server to look at a screen, check a UI change, take a screenshot or record a video, and try a locator; and runs bug bashes, parallel e2e explore runs with each bug proven by a repro test. Use when a project depends on e2e or has an e2e.config.ts, when asked for end-to-end, browser, mobile, or agentic UI tests, to check a change in the running app, to QA or bug bash an app or branch, or when an e2e run fails.","https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002Ftester-army\u002Fe2e\u002Fskills\u002Fe2e\u002FSKILL.md",[],[],{"skillNo":116,"name":117,"nameZh":6,"description":118,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":103,"licenseFileUrl":6,"sourceHtmlUrl":119,"categories":120,"keywords":121,"publishedAt":107},"01M746ZJN784HVHMDP9MQGCSGG","create-verification-skill","Generate a project-local verify-\u003Capp> skill on top of e2e, so any coding agent can launch the app, check it is worth driving, exercise a feature the way a user does, keep evidence, and bug bash it. Writes the skill, a feature map with one explore charter per feature, and proves it once. Use for \u002Fcreate-verification-skill, \"make a verification skill for this repo\", or when a project has no scripted way to prove UI behavior.","https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002Ftester-army\u002Fe2e\u002Fskills\u002Fcreate-verification-skill\u002FSKILL.md",[],[],{"skillNo":123,"name":124,"nameZh":6,"description":125,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":103,"licenseFileUrl":6,"sourceHtmlUrl":126,"categories":127,"keywords":128,"publishedAt":45},"01M716A6ETNB9B1SQ3GXZC75VR","deja-search","deja-vu memory — search the user's past AI coding sessions with the deja CLI. Holds what they already settled and never wrote into the repo (naming rules, banned or already-tried tools, required checks before merge, preferred values, who owns what). Use before your first edit in a task, before you call a change done or ready to merge, before you recommend a tool, library, design or reviewer, before re-debugging an error that may already be solved, when they say 'didn't we fix this before', 'what did we decide about X' or 'I already have that', and before telling them something on this machine does not exist.","https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002Fvshulcz\u002Fdeja-vu\u002Fskills\u002Fdeja-search\u002FSKILL.md",[],[129,130,131,132,133,134,135,136,137,138,139,30,140,141,142,143,144,145,146,147],"search","deja search","never","error","ready","memory","re","decide","fix","design","debugging","you","done","reviewer","io","change","cli","merge","past",{"skillNo":149,"name":150,"nameZh":6,"description":151,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":103,"licenseFileUrl":6,"sourceHtmlUrl":152,"categories":153,"keywords":154,"publishedAt":45},"01M716A3J2W5SENSE9VGMXB375","deja-reflect","End-of-session reflection and memory curation with deja-vu. Review what was decided, what commands broke and what repaired them, and record durable project rules or decisions with deja remember so the next session starts with them.","https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002Fvshulcz\u002Fdeja-vu\u002Fskills\u002Fdeja-reflect\u002FSKILL.md",[],[155,156,157,143,158,159,160,161,162,163,164,165,166,167,134,168,169,170,171,172],"deja reflect","reflect","review","with","record","github","next","https","node","Rules","decisions","brew","remember","project rules","project","session","end","reflection",{"skillNo":174,"name":175,"nameZh":6,"description":176,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":103,"licenseFileUrl":6,"sourceHtmlUrl":177,"categories":178,"keywords":186,"publishedAt":45},"01M715T3RR4BAYSR57HPPTJ0PF","revid","Make, edit, export and post AI videos with Revid (revid.ai) through the Revid MCP server. Use whenever the user wants a short-form or long-form video made (from an idea, a script, a URL, an article, a PDF, a tweet, a product page, their own clips, or a song), wants to find what is going viral on TikTok, YouTube, Instagram or X before making something, wants to edit or re-export an existing Revid project, generate a standalone AI image or clip, make a series episode, or publish and schedule a finished video to connected social accounts. Covers setup (hosted MCP with OAuth, or an API key), the 36 tools, credits, polling and the rules for publishing. Routes to the more specific revid-* skills when one fits.","https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002Ftmakersas\u002Frevid-skills\u002Fskills\u002Frevid\u002FSKILL.md",[179,182,183],{"categoryNo":180,"categoryName":181,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000016","图像与多媒体",{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":184,"categoryName":185,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000010","后端与接口",[175,187,188,189,190,191,192,169,193,135,194,195,196,197,30,198,199,200,201,202],"URL","Video","short-form","standalone","PDF","viral","tiktok","short","social","MCP","song","image","mcp server","schedule","api","publish"]