[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6M9CGJF9AJSWNFKBP25XPQ0":3,"skill-same:01M6M9CGJF9AJSWNFKBP25XPQ0":109},{"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":28,"publishedAt":51,"linkOnly":52,"markdownContent":53,"markdownBody":54,"frontMatter":55,"refName":59,"commitSha":60,"skillDirectory":61,"forksCount":62,"repositoryHtmlUrl":63,"repositoryHomepageUrl":64,"ownerHtmlUrl":65,"ownerBlogUrl":66,"ownerDescription":67,"updatedAt":68,"install":69,"resources":82},"01M6M9CGJF9AJSWNFKBP25XPQ0","suede-referrals",null,"Design a referral and affiliate program with incentives, commission and payout rules, fraud controls, disclosure, attribution, and cohort measurement. Use when asked to \"design our referral program\" or repair customer, ambassador, or partner incentives. Returns a program spec, economics, tooling options, and launch checklist. NOT FOR: payouts or billing changes, brand partnerships (use suede-co-marketing), launch packaging (use suede-launch-packaging), or lifecycle messages (use suede-emails).","hashgraph-online\u002Fawesome-codex-plugins","hashgraph-online","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F160371962?v=4","01M6CNB6NRJVNNGN14TR01Q6D3","HOL (Hashgraph Online)","01M6JT1HSKKG9V68KQAQGBA7XP",1571,"Apache-2.0","https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002FJasonColapietro\u002Fsuede-creator-skills\u002Fskills\u002Fsuede-referrals\u002FSKILL.md",[18,23],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000015","视觉设计","SCENARIO","使用场景",{"categoryNo":24,"categoryName":25,"taxonomyType":26,"taxonomyTypeDesc":27},"01JBSEEDCAT000000000000032","设计师","PROFESSION","适用职业",[29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50],"suede referrals","referrals","program","design","when","referral","for","incentives","measurement","co","billing","economics","repair","options","packaging","brand","returns","marketing","lifecycle","referral program","messages","customer","2026-11-10 10:12:30",false,"---\nname: suede-referrals\ndescription: \"Design a referral and affiliate program with incentives, commission and payout rules, fraud controls, disclosure, attribution, and cohort measurement. Use when asked to \\\"design our referral program\\\" or repair customer, ambassador, or partner incentives. Returns a program spec, economics, tooling options, and launch checklist. NOT FOR: payouts or billing changes, brand partnerships (use suede-co-marketing), launch packaging (use suede-launch-packaging), or lifecycle messages (use suede-emails).\"\nmetadata:\n  display-name: \"Suede AI Referral Skills\"\n  version: 2.1.0\n---\n\n# Suede AI Referral Skills\n\n## Compatibility\n\nUses supplied margin, LTV, and cohort data; current platform and disclosure checks need web access. Program design does not require a live payout or affiliate integration.\n\nFor a concrete input, expected handoff, and failure check, read\n[the worked handoff](references\u002Fworked-handoff.md).\n\nSuede AI Referrals designs measurable customer, affiliate, and partner loops from\nincentive economics through attribution and fraud controls. It separates\nmodeled loop performance from observed results and keeps activation and payouts\nbehind approval.\n\n## Before Starting\n\n**Check for product marketing context first:**\nIf `.agents\u002Fproduct-marketing.md` exists (or `.claude\u002Fproduct-marketing.md`, or the legacy `product-marketing-context.md` filename, in older setups), read it before asking questions. Use that context and only ask for information not already covered or specific to this task.\n\nGather this context (ask if not provided):\n\n### 1. Program Type\n- Customer referral program, affiliate program, or both?\n- B2B or B2C?\n- What's the average customer LTV?\n- What's your current CAC from other channels?\n\n### 2. Current State\n- Existing referral\u002Faffiliate program?\n- Current referral rate (% who refer)?\n- What incentives have you tried?\n\n### 3. Product Fit\n- Is your product shareable?\n- Does it have network effects?\n- Do customers naturally talk about it?\n\n### 4. Resources\n- Tools\u002Fplatforms you use or consider?\n- Budget for referral incentives?\n\n---\n\n## Referral vs. Affiliate\n\n### Customer Referral Programs\n\n**Best for:**\n- Existing customers recommending to their network\n- Products with natural word-of-mouth\n- Lower-ticket or self-serve products\n\n**Characteristics:**\n- Referrer is an existing customer\n- One-time or limited rewards\n- Higher trust, lower volume\n\n### Affiliate Programs\n\n**Best for:**\n- Reaching audiences you don't have access to\n- Content creators, influencers, bloggers\n- Higher-ticket products that justify commissions\n\n**Characteristics:**\n- Affiliates may not be customers\n- Ongoing commission relationship\n- Higher volume, variable trust\n\n---\n\n## Referral Program Design\n\n### The Referral Loop\n\n```\nTrigger Moment → Share Action → Convert Referred → Reward → (Loop)\n```\n\n### Step 1: Identify Trigger Moments\n\n**High-intent moments:**\n- Right after first \"aha\" moment\n- After achieving a milestone\n- After exceptional support\n- After renewing or upgrading\n\n**Prompt cadence for customers who have not referred:** day 7, day 30, day 60,\nand after any milestone. The timing is this skill's call; the message copy is\nnot: hand that to `suede-emails`.\n\n### Step 2: Design Share Mechanism\n\n**Ranked by effectiveness:**\n1. In-product sharing (highest conversion)\n2. Personalized link\n3. Email invitation\n4. Social sharing\n5. Referral code (works offline)\n\n### Step 3: Choose Incentive Structure\n\n**Single-sided rewards** (referrer only): Simpler, works for high-value products\n\n**Double-sided rewards** (both parties): Higher conversion, win-win framing\n\n**Tiered rewards**: Gamifies referral process, increases engagement\n\n**For program patterns and incentive sizing**: See [references\u002Fprogram-examples.md](references\u002Fprogram-examples.md)\n\n---\n\n## Program Optimization\n\n### Improving Referral Rate\n\n**If few customers are referring:**\n- Ask at better moments\n- Simplify sharing process\n- Test different incentive types\n- Make referral prominent in product\n\n**If referrals aren't converting:**\n- Improve landing experience for referred users\n- Strengthen incentive for new users\n- Ensure referrer's endorsement is visible\n\n### A\u002FB Tests to Run\n\n**Incentive tests:** Amount, type, single vs. double-sided, timing\n\n**Messaging tests:** Program description, CTA copy, landing page copy\n\n**Placement tests:** Where and when the referral prompt appears\n\n### Common Problems & Fixes\n\n| Problem | Fix |\n|---------|-----|\n| Low awareness | Add prominent in-app prompts |\n| Low share rate | Simplify to one click |\n| Low conversion | Optimize referred user experience |\n| Fraud\u002Fabuse | Apply the fraud controls below |\n| One-time referrers | Add tiered\u002Fgamified rewards |\n\n### Fraud Controls\n\nRead [references\u002Faffiliate-programs.md](references\u002Faffiliate-programs.md)\n§Fraud Prevention before designing rewards or writing program terms: it carries\nthe technical, policy, and structural control set (delayed payout after\nactivation, device and IP signals, clawback on refunds, per-period caps,\nmanual review of suspicious patterns). Every program in this skill uses it,\ncustomer referral programs included, not only affiliate programs.\n\nSet these thresholds explicitly, because the reference leaves them open:\n- Name the qualifying downstream event that releases a reward (a paid conversion\n  or day-N retention, N stated), never signup alone.\n- Hold payouts until the refund\u002Fchargeback window has closed, and state that\n  window in days.\n- State the dollar amount above which a payout goes to manual review before it\n  is released.\n\n---\n\n## Measuring Success\n\n### Key Metrics\n\n**Program health:**\n- Active referrers (referred someone in last 30 days)\n- Referral conversion rate\n- Rewards earned\u002Fpaid\n\n**Business impact:**\n- % of new customers from referrals\n- CAC via referral vs. other channels\n- LTV of referred customers\n- Referral program ROI\n\n### Typical Findings\n\nPeer-reviewed field research (for example \"Referral Programs and Customer\nValue,\" Journal of Marketing, 2011, a study of bank customers) found referred\ncustomers more valuable and less likely to churn than other customers; vendor\nreports claim a wide range of larger effects with undisclosed methods. Treat\nany figure as a hypothesis: compare referred and non-referred cohorts on\nretention, LTV, and onward referral rate in this product's own data before\nclaiming an effect.\n\n### Disclosure and incentive rules\n\n- Referrers and affiliates who are paid or rewarded must clearly disclose that\n  connection where they recommend you (FTC Endorsement Guides, revised 2023:\n  [FTC guidance](https:\u002F\u002Fwww.ftc.gov\u002Fbusiness-guidance\u002Fresources\u002Fftcs-endorsement-guides-what-people-are-asking)).\n- Do not reward reviews conditioned on positive sentiment, and do not suppress\n  negative ones (FTC Consumer Reviews and Testimonials Rule, effective October\n  21, 2024).\n- Contests and milestone giveaways can fall under sweepstakes law; payouts can\n  create tax reporting duties. Flag both for the user's counsel and finance owner.\n\n---\n\n## Launch Checklist\n\n### Before Launch\n- [ ] Define program goals and success metrics\n- [ ] Design incentive structure\n- [ ] Build or configure referral tool\n- [ ] Create referral landing page\n- [ ] Set up tracking and attribution\n- [ ] Define fraud prevention rules\n- [ ] Create terms and conditions\n- [ ] Test complete referral flow\n\n### Launch\n- [ ] Announce to existing customers\n- [ ] Add in-app referral prompts\n- [ ] Update website with program details\n- [ ] Brief support team\n\n### Post-Launch (First 30 Days)\n- [ ] Review conversion funnel\n- [ ] Identify top referrers\n- [ ] Gather feedback\n- [ ] Fix friction points\n- [ ] Send reminder emails to non-referrers\n\n---\n\n## Affiliate Programs\n\n**For affiliate program design, commission structures, recruitment, fraud\nprevention, and tools**: See [references\u002Faffiliate-programs.md](references\u002Faffiliate-programs.md)\n\n---\n\n## Tool Integrations\n\nThese are evaluation examples, not guaranteed integrations. Verify current\nvendor documentation, pricing, account access, attribution behavior, payout\ncontrols, tax support, and data-export terms before recommending a platform.\n\n| Tool | Best For | Verify Before Use |\n|------|----------|-------------------|\n| **Rewardful \u002F Tolt** | SaaS affiliate programs | Billing integration, attribution, payouts |\n| **Mention Me** | Enterprise referral programs | Identity, fraud, and reporting controls |\n| **Dub.co** | Link tracking and attribution | Attribution window and privacy settings |\n| **Stripe** | Commission-related payment records | Live objects, fees, approvals, tax workflow |\n| **Introw** | Tiered channel partner operations | Deal registration and payout governance |\n| **PartnerStack** | Enterprise partner ecosystems | Fees, attribution, approval, data export |\n\n---\n\n## Boundaries\n\n- Do not enable a program, create affiliate accounts, issue links, execute\n  payouts, or change billing and commission objects without verified live state\n  and explicit approval.\n- Do not invent attribution, conversion, fraud, or viral-coefficient results;\n  distinguish modeled economics from observed data.\n- Do not decide tax, labor, privacy, contest, endorsement, or incentive\n  compliance. Surface the jurisdiction-specific review needed before launch.\n\n## Routing\n\n- Use `suede-launch-packaging` to coordinate the approved program launch.\n- Use `suede-emails` for referral invitation and nurture sequences.\n- Use `suede-offers` (behavioral-models catalog) to frame incentive tests as evidence-tiered hypotheses.\n- Use `suede-ab-testing` to design and evaluate the incentive, messaging, and placement tests above.\n- Use `suede-analytics` to define and read referral attribution.\n","# Suede AI Referral Skills\n\n## Compatibility\n\nUses supplied margin, LTV, and cohort data; current platform and disclosure checks need web access. Program design does not require a live payout or affiliate integration.\n\nFor a concrete input, expected handoff, and failure check, read\n[the worked handoff](references\u002Fworked-handoff.md).\n\nSuede AI Referrals designs measurable customer, affiliate, and partner loops from\nincentive economics through attribution and fraud controls. It separates\nmodeled loop performance from observed results and keeps activation and payouts\nbehind approval.\n\n## Before Starting\n\n**Check for product marketing context first:**\nIf `.agents\u002Fproduct-marketing.md` exists (or `.claude\u002Fproduct-marketing.md`, or the legacy `product-marketing-context.md` filename, in older setups), read it before asking questions. Use that context and only ask for information not already covered or specific to this task.\n\nGather this context (ask if not provided):\n\n### 1. Program Type\n- Customer referral program, affiliate program, or both?\n- B2B or B2C?\n- What's the average customer LTV?\n- What's your current CAC from other channels?\n\n### 2. Current State\n- Existing referral\u002Faffiliate program?\n- Current referral rate (% who refer)?\n- What incentives have you tried?\n\n### 3. Product Fit\n- Is your product shareable?\n- Does it have network effects?\n- Do customers naturally talk about it?\n\n### 4. Resources\n- Tools\u002Fplatforms you use or consider?\n- Budget for referral incentives?\n\n---\n\n## Referral vs. Affiliate\n\n### Customer Referral Programs\n\n**Best for:**\n- Existing customers recommending to their network\n- Products with natural word-of-mouth\n- Lower-ticket or self-serve products\n\n**Characteristics:**\n- Referrer is an existing customer\n- One-time or limited rewards\n- Higher trust, lower volume\n\n### Affiliate Programs\n\n**Best for:**\n- Reaching audiences you don't have access to\n- Content creators, influencers, bloggers\n- Higher-ticket products that justify commissions\n\n**Characteristics:**\n- Affiliates may not be customers\n- Ongoing commission relationship\n- Higher volume, variable trust\n\n---\n\n## Referral Program Design\n\n### The Referral Loop\n\n```\nTrigger Moment → Share Action → Convert Referred → Reward → (Loop)\n```\n\n### Step 1: Identify Trigger Moments\n\n**High-intent moments:**\n- Right after first \"aha\" moment\n- After achieving a milestone\n- After exceptional support\n- After renewing or upgrading\n\n**Prompt cadence for customers who have not referred:** day 7, day 30, day 60,\nand after any milestone. The timing is this skill's call; the message copy is\nnot: hand that to `suede-emails`.\n\n### Step 2: Design Share Mechanism\n\n**Ranked by effectiveness:**\n1. In-product sharing (highest conversion)\n2. Personalized link\n3. Email invitation\n4. Social sharing\n5. Referral code (works offline)\n\n### Step 3: Choose Incentive Structure\n\n**Single-sided rewards** (referrer only): Simpler, works for high-value products\n\n**Double-sided rewards** (both parties): Higher conversion, win-win framing\n\n**Tiered rewards**: Gamifies referral process, increases engagement\n\n**For program patterns and incentive sizing**: See [references\u002Fprogram-examples.md](references\u002Fprogram-examples.md)\n\n---\n\n## Program Optimization\n\n### Improving Referral Rate\n\n**If few customers are referring:**\n- Ask at better moments\n- Simplify sharing process\n- Test different incentive types\n- Make referral prominent in product\n\n**If referrals aren't converting:**\n- Improve landing experience for referred users\n- Strengthen incentive for new users\n- Ensure referrer's endorsement is visible\n\n### A\u002FB Tests to Run\n\n**Incentive tests:** Amount, type, single vs. double-sided, timing\n\n**Messaging tests:** Program description, CTA copy, landing page copy\n\n**Placement tests:** Where and when the referral prompt appears\n\n### Common Problems & Fixes\n\n| Problem | Fix |\n|---------|-----|\n| Low awareness | Add prominent in-app prompts |\n| Low share rate | Simplify to one click |\n| Low conversion | Optimize referred user experience |\n| Fraud\u002Fabuse | Apply the fraud controls below |\n| One-time referrers | Add tiered\u002Fgamified rewards |\n\n### Fraud Controls\n\nRead [references\u002Faffiliate-programs.md](references\u002Faffiliate-programs.md)\n§Fraud Prevention before designing rewards or writing program terms: it carries\nthe technical, policy, and structural control set (delayed payout after\nactivation, device and IP signals, clawback on refunds, per-period caps,\nmanual review of suspicious patterns). Every program in this skill uses it,\ncustomer referral programs included, not only affiliate programs.\n\nSet these thresholds explicitly, because the reference leaves them open:\n- Name the qualifying downstream event that releases a reward (a paid conversion\n  or day-N retention, N stated), never signup alone.\n- Hold payouts until the refund\u002Fchargeback window has closed, and state that\n  window in days.\n- State the dollar amount above which a payout goes to manual review before it\n  is released.\n\n---\n\n## Measuring Success\n\n### Key Metrics\n\n**Program health:**\n- Active referrers (referred someone in last 30 days)\n- Referral conversion rate\n- Rewards earned\u002Fpaid\n\n**Business impact:**\n- % of new customers from referrals\n- CAC via referral vs. other channels\n- LTV of referred customers\n- Referral program ROI\n\n### Typical Findings\n\nPeer-reviewed field research (for example \"Referral Programs and Customer\nValue,\" Journal of Marketing, 2011, a study of bank customers) found referred\ncustomers more valuable and less likely to churn than other customers; vendor\nreports claim a wide range of larger effects with undisclosed methods. Treat\nany figure as a hypothesis: compare referred and non-referred cohorts on\nretention, LTV, and onward referral rate in this product's own data before\nclaiming an effect.\n\n### Disclosure and incentive rules\n\n- Referrers and affiliates who are paid or rewarded must clearly disclose that\n  connection where they recommend you (FTC Endorsement Guides, revised 2023:\n  [FTC guidance](https:\u002F\u002Fwww.ftc.gov\u002Fbusiness-guidance\u002Fresources\u002Fftcs-endorsement-guides-what-people-are-asking)).\n- Do not reward reviews conditioned on positive sentiment, and do not suppress\n  negative ones (FTC Consumer Reviews and Testimonials Rule, effective October\n  21, 2024).\n- Contests and milestone giveaways can fall under sweepstakes law; payouts can\n  create tax reporting duties. Flag both for the user's counsel and finance owner.\n\n---\n\n## Launch Checklist\n\n### Before Launch\n- [ ] Define program goals and success metrics\n- [ ] Design incentive structure\n- [ ] Build or configure referral tool\n- [ ] Create referral landing page\n- [ ] Set up tracking and attribution\n- [ ] Define fraud prevention rules\n- [ ] Create terms and conditions\n- [ ] Test complete referral flow\n\n### Launch\n- [ ] Announce to existing customers\n- [ ] Add in-app referral prompts\n- [ ] Update website with program details\n- [ ] Brief support team\n\n### Post-Launch (First 30 Days)\n- [ ] Review conversion funnel\n- [ ] Identify top referrers\n- [ ] Gather feedback\n- [ ] Fix friction points\n- [ ] Send reminder emails to non-referrers\n\n---\n\n## Affiliate Programs\n\n**For affiliate program design, commission structures, recruitment, fraud\nprevention, and tools**: See [references\u002Faffiliate-programs.md](references\u002Faffiliate-programs.md)\n\n---\n\n## Tool Integrations\n\nThese are evaluation examples, not guaranteed integrations. Verify current\nvendor documentation, pricing, account access, attribution behavior, payout\ncontrols, tax support, and data-export terms before recommending a platform.\n\n| Tool | Best For | Verify Before Use |\n|------|----------|-------------------|\n| **Rewardful \u002F Tolt** | SaaS affiliate programs | Billing integration, attribution, payouts |\n| **Mention Me** | Enterprise referral programs | Identity, fraud, and reporting controls |\n| **Dub.co** | Link tracking and attribution | Attribution window and privacy settings |\n| **Stripe** | Commission-related payment records | Live objects, fees, approvals, tax workflow |\n| **Introw** | Tiered channel partner operations | Deal registration and payout governance |\n| **PartnerStack** | Enterprise partner ecosystems | Fees, attribution, approval, data export |\n\n---\n\n## Boundaries\n\n- Do not enable a program, create affiliate accounts, issue links, execute\n  payouts, or change billing and commission objects without verified live state\n  and explicit approval.\n- Do not invent attribution, conversion, fraud, or viral-coefficient results;\n  distinguish modeled economics from observed data.\n- Do not decide tax, labor, privacy, contest, endorsement, or incentive\n  compliance. Surface the jurisdiction-specific review needed before launch.\n\n## Routing\n\n- Use `suede-launch-packaging` to coordinate the approved program launch.\n- Use `suede-emails` for referral invitation and nurture sequences.\n- Use `suede-offers` (behavioral-models catalog) to frame incentive tests as evidence-tiered hypotheses.\n- Use `suede-ab-testing` to design and evaluate the incentive, messaging, and placement tests above.\n- Use `suede-analytics` to define and read referral attribution.\n",{"name":5,"metadata":56,"description":7},{"version":57,"display-name":58},"2.1.0","Suede AI Referral Skills","main","2b63f856ce19c2a797929cacabd9e0fc936812fa","plugins\u002FJasonColapietro\u002Fsuede-creator-skills\u002Fskills\u002Fsuede-referrals",490,"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.","2026-11-14 02:12:02",{"source":8,"skillName":5,"commands":70,"prompt":80,"zipAvailable":81},[71,74,77],{"tool":72,"command":73},"npx","npx skills add hashgraph-online\u002Fawesome-codex-plugins --skill suede-referrals",{"tool":75,"command":76},"bunx","bunx skills add hashgraph-online\u002Fawesome-codex-plugins --skill suede-referrals",{"tool":78,"command":79},"pnpm","pnpm dlx skills add hashgraph-online\u002Fawesome-codex-plugins --skill suede-referrals","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002FJasonColapietro\u002Fsuede-creator-skills\u002Fskills\u002Fsuede-referrals\u002FSKILL.md",true,[83,89,93,97,101,105],{"path":84,"resourceType":85,"resourceTypeDesc":86,"sizeBytes":87,"sourceHtmlUrl":88},"agents\u002Fopenai.yaml","REFERENCE","参考文档",592,"https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002FJasonColapietro\u002Fsuede-creator-skills\u002Fskills\u002Fsuede-referrals\u002Fagents\u002Fopenai.yaml",{"path":90,"resourceType":85,"resourceTypeDesc":86,"sizeBytes":91,"sourceHtmlUrl":92},"CARD.md",4433,"https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002FJasonColapietro\u002Fsuede-creator-skills\u002Fskills\u002Fsuede-referrals\u002FCARD.md",{"path":94,"resourceType":85,"resourceTypeDesc":86,"sizeBytes":95,"sourceHtmlUrl":96},"evals\u002Fevals.json",6416,"https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002FJasonColapietro\u002Fsuede-creator-skills\u002Fskills\u002Fsuede-referrals\u002Fevals\u002Fevals.json",{"path":98,"resourceType":85,"resourceTypeDesc":86,"sizeBytes":99,"sourceHtmlUrl":100},"references\u002Faffiliate-programs.md",4387,"https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002FJasonColapietro\u002Fsuede-creator-skills\u002Fskills\u002Fsuede-referrals\u002Freferences\u002Faffiliate-programs.md",{"path":102,"resourceType":85,"resourceTypeDesc":86,"sizeBytes":103,"sourceHtmlUrl":104},"references\u002Fprogram-examples.md",4026,"https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002FJasonColapietro\u002Fsuede-creator-skills\u002Fskills\u002Fsuede-referrals\u002Freferences\u002Fprogram-examples.md",{"path":106,"resourceType":85,"resourceTypeDesc":86,"sizeBytes":107,"sourceHtmlUrl":108},"references\u002Fworked-handoff.md",1692,"https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002FJasonColapietro\u002Fsuede-creator-skills\u002Fskills\u002Fsuede-referrals\u002Freferences\u002Fworked-handoff.md",[110,118,125,132,157,182],{"skillNo":111,"name":112,"nameZh":6,"description":113,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":114,"categories":115,"keywords":116,"publishedAt":117},"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.","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":119,"name":120,"nameZh":6,"description":121,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":122,"categories":123,"keywords":124,"publishedAt":117},"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":126,"name":127,"nameZh":6,"description":128,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":129,"categories":130,"keywords":131,"publishedAt":117},"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":133,"name":134,"nameZh":6,"description":135,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":136,"categories":137,"keywords":138,"publishedAt":51},"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",[],[139,140,141,142,143,144,145,146,147,32,148,33,149,150,151,152,153,154,155,156],"search","deja search","never","error","ready","memory","re","decide","fix","debugging","you","done","reviewer","io","change","cli","merge","past",{"skillNo":158,"name":159,"nameZh":6,"description":160,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":161,"categories":162,"keywords":163,"publishedAt":51},"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",[],[164,165,166,152,167,168,169,170,171,172,173,174,175,176,144,177,178,179,180,181],"deja reflect","reflect","review","with","record","github","next","https","node","Rules","decisions","brew","remember","project rules","project","session","end","reflection",{"skillNo":183,"name":184,"nameZh":6,"description":185,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":186,"categories":187,"keywords":197,"publishedAt":51},"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",[188,191,194],{"categoryNo":189,"categoryName":190,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000016","图像与多媒体",{"categoryNo":192,"categoryName":193,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000023","Skill与智能体开发",{"categoryNo":195,"categoryName":196,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000010","后端与接口",[184,198,199,200,201,202,203,178,204,145,205,206,207,208,33,209,210,211,212,213],"URL","Video","short-form","standalone","PDF","viral","tiktok","short","social","MCP","song","image","mcp server","schedule","api","publish"]