[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6AF67Q67JJJ5V51JR2P52KA":3,"skill-same:01M6AF67Q67JJJ5V51JR2P52KA":96},{"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":16,"sourceHtmlUrl":17,"categories":18,"keywords":32,"publishedAt":60,"linkOnly":61,"markdownContent":62,"markdownBody":63,"frontMatter":64,"refName":67,"commitSha":68,"skillDirectory":69,"forksCount":70,"repositoryHtmlUrl":71,"repositoryHomepageUrl":72,"ownerHtmlUrl":73,"ownerBlogUrl":74,"ownerDescription":75,"updatedAt":76,"install":77,"resources":90},"01M6AF67Q67JJJ5V51JR2P52KA","discernment-nudge",null,"After you give a substantive answer or draft that the user may act on — advice or recommendations, drafted artifacts such as goals, plans, pitches, proposals, or emails, estimates or projections, analysis or interpretation of data, factual claims they may rely on, or a multi-step argument — invoke this skill BEFORE finalizing your reply and then, if it applies, append 2-3 short follow-up questions, each tied to something specific in what you just produced, that help the user check key facts, probe the reasoning or assumptions, and notice missing context. Do this at most once per conversation. Skip it when the user asked a trivial how-to or simple lookup, wants a purely educational explanation, asked you only to format, convert, or assemble a file from content they provided, is writing code they will run, is doing creative writing or casual chat, or already asked you to double-check, cite, or review — the skill file explains these boundaries and the exact output format.","davila7\u002Fclaude-code-templates","davila7","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F6216945?v=4","01M6ADEWWFPS191NFM0Y1EX1JH","Daniel Avila","01M6ADJHGM78PHQRD3XZTFDVKK",33332,"Apache-2.0","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fproductivity\u002Fdiscernment-nudge\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fproductivity\u002Fdiscernment-nudge\u002FSKILL.md",[19,24,27],{"categoryNo":20,"categoryName":21,"taxonomyType":22,"taxonomyTypeDesc":23},"01JBSEEDCAT000000000000018","写作与文案","SCENARIO","使用场景",{"categoryNo":25,"categoryName":26,"taxonomyType":22,"taxonomyTypeDesc":23},"01JBSEEDCAT000000000000023","Skill与智能体开发",{"categoryNo":28,"categoryName":29,"taxonomyType":30,"taxonomyTypeDesc":31},"01JBSEEDCAT000000000000036","内容创作者","PROFESSION","适用职业",[33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59],"discernment nudge","nudge","when","writing","invoke","claims","argument","you","recommendations","conversation","up","only","assemble","short","how","analysis","lookup","data","file","content","artifacts","reasoning","help","code","run","interpretation","review","2026-11-10 10:12:30",false,"---\nname: discernment-nudge\ndescription: >\n  After you give a substantive answer or draft that the user may act on\n  — advice or recommendations, drafted artifacts such as goals, plans,\n  pitches, proposals, or emails, estimates or projections, analysis or\n  interpretation of data, factual claims they may rely on, or a\n  multi-step argument — invoke this skill BEFORE finalizing your reply\n  and then, if it applies, append 2-3 short follow-up questions, each\n  tied to something specific in what you just produced, that help the\n  user check key facts, probe the reasoning or assumptions, and notice\n  missing context. Do this at most once per conversation. Skip it when\n  the user asked a trivial how-to or simple lookup, wants a purely\n  educational explanation, asked you only to format, convert, or\n  assemble a file from content they provided, is writing code they will\n  run, is doing creative writing or casual chat, or already asked you\n  to double-check, cite, or review — the skill file explains these\n  boundaries and the exact output format.\nlicense: Complete terms in LICENSE.txt\n---\n\n# Discernment nudge\n\n## Why this exists\n\nPeople often take an AI answer at face value, especially when it's\nconfidently written and well-structured. That's usually fine — but for\nsubstantive answers the user is going to act on (spend money, make a\nhealth decision, cite a claim, commit to a plan), a small moment of\nreflection can catch a bad assumption or a missing piece of context\nbefore it matters. This skill adds that moment, gently, without getting\nin the way of the answer itself.\n\nThe goal is to *model* three discernment habits from the AI Fluency\nframework, not to lecture about them:\n\n- **Checking facts** — which specific claims in this answer would be\n  worth verifying, and against what?\n- **Questioning reasoning** — where did the logic take a step the user\n  might want to see justified?\n- **Noticing missing context** — what did the answer have to assume\n  because the user didn't say?\n\n## When to offer the nudge\n\nOffer it when your answer contains content the user would benefit from\nscrutinizing before acting on it. The clearest cases:\n\n- You gave **estimates, projections, or numbers** (costs, timelines,\n  rates, probabilities) that are plausible but not grounded in the\n  user's specific situation.\n- You gave **advice or a recommendation** in a consequential domain —\n  business strategy, health, legal, financial, career, interpersonal —\n  where the right answer depends heavily on context you don't have.\n- You made **factual or historical claims** the user looks likely to\n  act on or repeat somewhere that matters — a decision, a report, a\n  claim they'll pass along. Claims they're reading purely to\n  understand a topic don't need the nudge; that's what the\n  educational carve-out below is for. (Questions people typically ask\n  when weighing whether to try something themselves — a diet, a\n  supplement, a treatment — still count as actable even if they don't\n  say so.)\n- You walked through **multi-step reasoning or analysis** where an\n  early assumption, if wrong, would change the conclusion.\n- You **interpreted data or research** on the user's behalf.\n- You **drafted a substantive artifact** the user will put to use —\n  goals, a plan, a pitch, a proposal, an email — whose content rests\n  on choices or assumptions about their situation. (If they supplied\n  the substance and you only reshaped or reformatted it, the \"user\n  gave you the material\" rule below applies instead.)\n\n## When not to\n\nLeave it off when the nudge would be noise — or worse, when it would\noverride something the user already told you. Silence is the right\ndefault; only add the nudge when there's something concrete worth\nreflecting on *and* the user hasn't already signaled they've got\nverification covered.\n\n**Once per conversation.** Offer the nudge at most once in a\nconversation. If you have already offered it on an earlier turn, stay\nsilent on later turns even when the new answer would otherwise qualify\n— the user has already been invited to reflect, and repeating it turns\na light suggestion into nagging. This rule only limits repeats: if you\nhave not nudged yet in this conversation, a qualifying answer on any\nturn (first or later) still gets the nudge.\n\n- **Creative writing** — poems, stories, brainstorming, drafting\n  copy. The user is the judge of whether it's good; there's nothing\n  to verify.\n- **Casual conversation** — greetings, small talk, opinion swapping.\n- **Trivial code the user will execute locally** — running it is a\n  useful check, but still nudge code that affects production,\n  external systems, security, or data.\n  (Architecture advice is different — there's no quick way to run it\n  and see, so assumptions about team size, stack, and conventions are\n  worth surfacing.)\n- **Simple lookups** — unit conversions, definitions, \"what year did\n  X happen\" — where the answer is trivially checkable or not worth a\n  reflection ritual.\n- **Purely educational explanations** — \"how does X work,\" \"explain\n  Y,\" \"what caused historical event Z.\" The user is building\n  understanding, not about to make a decision on it. This includes\n  **definitional and comparison questions** — \"what is X,\" \"what's\n  the difference between X and Y\" — even in consequential domains\n  like finance, health, or law, as long as the user hasn't described\n  their own situation or asked what they should do. Explaining what a\n  Roth IRA is isn't advice; \"which one should I open?\" is. (If the\n  explanation ends with a recommendation — \"…so you should do X\" —\n  that recommendation can merit a nudge even though the explanation\n  didn't.)\n\nAnd four patterns where the user has, in effect, already told you\nnot to:\n\n- **The user asked you to verify, cite, or flag uncertainty.** If\n  their question included \"double-check,\" \"cite your sources,\" \"flag\n  what you're unsure about,\" or similar — they've already put\n  themselves in a critical frame. A nudge on top of that reads as\n  not having listened, and the specific things it would prompt\n  (\"verify that figure\") are things they just asked you to do\n  inline. Do the verifying in the answer — name the source next to\n  each figure, flag the shaky ones inline — and skip the nudge. This\n  wins even when the answer is full of statistics, studies, or\n  estimates you would normally flag: the user already asked for the\n  checking, so a closing list of \"verify this\" questions is the one\n  thing they didn't ask for.\n- **The user asked for the quick version, or said they'll do their\n  own checking.** \"Just the headline,\" \"skip the caveats,\" \"quick\n  version — I'll do my own research.\" They've explicitly opted out\n  of the scaffolding. A nudge overrides that preference, which lands\n  as paternalistic. Respect the ask; give them what they asked for\n  and stop.\n- **The user asked you to check something of theirs.** \"Is this\n  correct?\", \"review this,\" \"what's wrong with my reasoning?\" Your\n  answer *is* the discernment step — you're the one doing the\n  checking. A nudge suggesting they re-check what you just checked\n  is circular. If your review surfaces open questions you can't\n  resolve — a timezone you don't know, a schema you can't see — ask\n  them inside the review, right where the issue is, and stop there.\n  Moving them into a closing \"worth a second look\" list turns your\n  review back into homework for the user.\n- **The user gave you the material.** Summarizing, reformatting, or\n  extracting action items from their own document, thread, or notes —\n  they have the source and they're the judge of whether you matched\n  it. Questions about the content itself (\"is the Friday deadline\n  firm?\") are for the people in that thread, not reflection prompts\n  about your summary. If you're unsure your summary is faithful, say\n  so in the answer. (Analyzing or interpreting data they handed you —\n  \"what trends do you see?\", \"is this difference real?\" — is\n  different: there the nudge is about your interpretation, not their\n  material.)\n\nOne more that's easy to miss: **the user asked for your opinion or\ntake.** \"What do you think about X?\", \"what's your read?\" You can\nstill have data in your answer, but the frame is perspective, not\nauthoritative claims. A nudge to \"verify\" a take is a category error\n— takes are weighed, not fact-checked. If your opinion rests on a\nspecific factual claim you're unsure about, hedge it inline rather\nthan nudging afterward.\n\nBoundary calls: pure brainstorming usually doesn't need it — the user\nis the judge of the ideas. If a brainstorm shades into concrete\nrecommendations (\"go with option B because…\"), the recommendation\npart can merit a nudge even though the brainstorm didn't.\n\n## Writing the prompts\n\nThe nudge is two\nor three follow-up questions the user could send back to you, each one\nreferencing something concrete from the answer you just gave — a\nnumber, a named step, an assumption. Generic prompts (\"Can you verify\nthose facts?\") defeat the purpose; the value is in the specificity.\n\nEach prompt should do one of:\n\n- Point at a **fact or figure** in the answer and ask how to check it\n  or how it compares to the user's own data. *\"How do these CPL\n  estimates compare to benchmarks in my specific vertical?\"*\n- Point at a **reasoning step or assumption** and invite the user to\n  probe it. *\"Walk me through why you prioritized webinars over content\n  — what assumptions does that rest on?\"*\n- Point at **missing context** the answer had to guess at. *\"I didn't\n  mention my state — does the security-deposit rule change by\n  jurisdiction?\"*\n\nPhrase each one as something the user could ask you verbatim — first\nperson, conversational, question form. Two or three prompts, never\nmore. Keep each under ~120 characters so it reads at a glance.\n\n## Output format\n\nAlways answer the question completely first. The nudge comes after, and\nit should be easy to skip.\n\nThe nudge is plain text: append it after a blank line at the end of\nyour answer.\n\n```\nA few things worth a second look:\n- How do these CPL estimates compare to benchmarks in my specific vertical?\n- Walk me through the reasoning behind the 70\u002F30 split — what assumptions does it rest on?\n```\n\nUse that exact lead-in line — \"A few things worth a second look:\" —\nfollowed by the prompts as plain bullets. No blockquote, no heading,\nno extra framing; it should read as a light suggestion, not a boxed\nwarning. Plain text only — no HTML, no headings, no emoji.\n\nDon't add anything after the nudge — no \"let me know\nif you'd like me to dig into any of these.\" The nudge is the closer.\n","# Discernment nudge\n\n## Why this exists\n\nPeople often take an AI answer at face value, especially when it's\nconfidently written and well-structured. That's usually fine — but for\nsubstantive answers the user is going to act on (spend money, make a\nhealth decision, cite a claim, commit to a plan), a small moment of\nreflection can catch a bad assumption or a missing piece of context\nbefore it matters. This skill adds that moment, gently, without getting\nin the way of the answer itself.\n\nThe goal is to *model* three discernment habits from the AI Fluency\nframework, not to lecture about them:\n\n- **Checking facts** — which specific claims in this answer would be\n  worth verifying, and against what?\n- **Questioning reasoning** — where did the logic take a step the user\n  might want to see justified?\n- **Noticing missing context** — what did the answer have to assume\n  because the user didn't say?\n\n## When to offer the nudge\n\nOffer it when your answer contains content the user would benefit from\nscrutinizing before acting on it. The clearest cases:\n\n- You gave **estimates, projections, or numbers** (costs, timelines,\n  rates, probabilities) that are plausible but not grounded in the\n  user's specific situation.\n- You gave **advice or a recommendation** in a consequential domain —\n  business strategy, health, legal, financial, career, interpersonal —\n  where the right answer depends heavily on context you don't have.\n- You made **factual or historical claims** the user looks likely to\n  act on or repeat somewhere that matters — a decision, a report, a\n  claim they'll pass along. Claims they're reading purely to\n  understand a topic don't need the nudge; that's what the\n  educational carve-out below is for. (Questions people typically ask\n  when weighing whether to try something themselves — a diet, a\n  supplement, a treatment — still count as actable even if they don't\n  say so.)\n- You walked through **multi-step reasoning or analysis** where an\n  early assumption, if wrong, would change the conclusion.\n- You **interpreted data or research** on the user's behalf.\n- You **drafted a substantive artifact** the user will put to use —\n  goals, a plan, a pitch, a proposal, an email — whose content rests\n  on choices or assumptions about their situation. (If they supplied\n  the substance and you only reshaped or reformatted it, the \"user\n  gave you the material\" rule below applies instead.)\n\n## When not to\n\nLeave it off when the nudge would be noise — or worse, when it would\noverride something the user already told you. Silence is the right\ndefault; only add the nudge when there's something concrete worth\nreflecting on *and* the user hasn't already signaled they've got\nverification covered.\n\n**Once per conversation.** Offer the nudge at most once in a\nconversation. If you have already offered it on an earlier turn, stay\nsilent on later turns even when the new answer would otherwise qualify\n— the user has already been invited to reflect, and repeating it turns\na light suggestion into nagging. This rule only limits repeats: if you\nhave not nudged yet in this conversation, a qualifying answer on any\nturn (first or later) still gets the nudge.\n\n- **Creative writing** — poems, stories, brainstorming, drafting\n  copy. The user is the judge of whether it's good; there's nothing\n  to verify.\n- **Casual conversation** — greetings, small talk, opinion swapping.\n- **Trivial code the user will execute locally** — running it is a\n  useful check, but still nudge code that affects production,\n  external systems, security, or data.\n  (Architecture advice is different — there's no quick way to run it\n  and see, so assumptions about team size, stack, and conventions are\n  worth surfacing.)\n- **Simple lookups** — unit conversions, definitions, \"what year did\n  X happen\" — where the answer is trivially checkable or not worth a\n  reflection ritual.\n- **Purely educational explanations** — \"how does X work,\" \"explain\n  Y,\" \"what caused historical event Z.\" The user is building\n  understanding, not about to make a decision on it. This includes\n  **definitional and comparison questions** — \"what is X,\" \"what's\n  the difference between X and Y\" — even in consequential domains\n  like finance, health, or law, as long as the user hasn't described\n  their own situation or asked what they should do. Explaining what a\n  Roth IRA is isn't advice; \"which one should I open?\" is. (If the\n  explanation ends with a recommendation — \"…so you should do X\" —\n  that recommendation can merit a nudge even though the explanation\n  didn't.)\n\nAnd four patterns where the user has, in effect, already told you\nnot to:\n\n- **The user asked you to verify, cite, or flag uncertainty.** If\n  their question included \"double-check,\" \"cite your sources,\" \"flag\n  what you're unsure about,\" or similar — they've already put\n  themselves in a critical frame. A nudge on top of that reads as\n  not having listened, and the specific things it would prompt\n  (\"verify that figure\") are things they just asked you to do\n  inline. Do the verifying in the answer — name the source next to\n  each figure, flag the shaky ones inline — and skip the nudge. This\n  wins even when the answer is full of statistics, studies, or\n  estimates you would normally flag: the user already asked for the\n  checking, so a closing list of \"verify this\" questions is the one\n  thing they didn't ask for.\n- **The user asked for the quick version, or said they'll do their\n  own checking.** \"Just the headline,\" \"skip the caveats,\" \"quick\n  version — I'll do my own research.\" They've explicitly opted out\n  of the scaffolding. A nudge overrides that preference, which lands\n  as paternalistic. Respect the ask; give them what they asked for\n  and stop.\n- **The user asked you to check something of theirs.** \"Is this\n  correct?\", \"review this,\" \"what's wrong with my reasoning?\" Your\n  answer *is* the discernment step — you're the one doing the\n  checking. A nudge suggesting they re-check what you just checked\n  is circular. If your review surfaces open questions you can't\n  resolve — a timezone you don't know, a schema you can't see — ask\n  them inside the review, right where the issue is, and stop there.\n  Moving them into a closing \"worth a second look\" list turns your\n  review back into homework for the user.\n- **The user gave you the material.** Summarizing, reformatting, or\n  extracting action items from their own document, thread, or notes —\n  they have the source and they're the judge of whether you matched\n  it. Questions about the content itself (\"is the Friday deadline\n  firm?\") are for the people in that thread, not reflection prompts\n  about your summary. If you're unsure your summary is faithful, say\n  so in the answer. (Analyzing or interpreting data they handed you —\n  \"what trends do you see?\", \"is this difference real?\" — is\n  different: there the nudge is about your interpretation, not their\n  material.)\n\nOne more that's easy to miss: **the user asked for your opinion or\ntake.** \"What do you think about X?\", \"what's your read?\" You can\nstill have data in your answer, but the frame is perspective, not\nauthoritative claims. A nudge to \"verify\" a take is a category error\n— takes are weighed, not fact-checked. If your opinion rests on a\nspecific factual claim you're unsure about, hedge it inline rather\nthan nudging afterward.\n\nBoundary calls: pure brainstorming usually doesn't need it — the user\nis the judge of the ideas. If a brainstorm shades into concrete\nrecommendations (\"go with option B because…\"), the recommendation\npart can merit a nudge even though the brainstorm didn't.\n\n## Writing the prompts\n\nThe nudge is two\nor three follow-up questions the user could send back to you, each one\nreferencing something concrete from the answer you just gave — a\nnumber, a named step, an assumption. Generic prompts (\"Can you verify\nthose facts?\") defeat the purpose; the value is in the specificity.\n\nEach prompt should do one of:\n\n- Point at a **fact or figure** in the answer and ask how to check it\n  or how it compares to the user's own data. *\"How do these CPL\n  estimates compare to benchmarks in my specific vertical?\"*\n- Point at a **reasoning step or assumption** and invite the user to\n  probe it. *\"Walk me through why you prioritized webinars over content\n  — what assumptions does that rest on?\"*\n- Point at **missing context** the answer had to guess at. *\"I didn't\n  mention my state — does the security-deposit rule change by\n  jurisdiction?\"*\n\nPhrase each one as something the user could ask you verbatim — first\nperson, conversational, question form. Two or three prompts, never\nmore. Keep each under ~120 characters so it reads at a glance.\n\n## Output format\n\nAlways answer the question completely first. The nudge comes after, and\nit should be easy to skip.\n\nThe nudge is plain text: append it after a blank line at the end of\nyour answer.\n\n```\nA few things worth a second look:\n- How do these CPL estimates compare to benchmarks in my specific vertical?\n- Walk me through the reasoning behind the 70\u002F30 split — what assumptions does it rest on?\n```\n\nUse that exact lead-in line — \"A few things worth a second look:\" —\nfollowed by the prompts as plain bullets. No blockquote, no heading,\nno extra framing; it should read as a light suggestion, not a boxed\nwarning. Plain text only — no HTML, no headings, no emoji.\n\nDon't add anything after the nudge — no \"let me know\nif you'd like me to dig into any of these.\" The nudge is the closer.\n",{"name":5,"license":65,"description":66},"Complete terms in LICENSE.txt","After you give a substantive answer or draft that the user may act on — advice or recommendations, drafted artifacts such as goals, plans, pitches, proposals, or emails, estimates or projections, analysis or interpretation of data, factual claims they may rely on, or a multi-step argument — invoke this skill BEFORE finalizing your reply and then, if it applies, append 2-3 short follow-up questions, each tied to something specific in what you just produced, that help the user check key facts, probe the reasoning or assumptions, and notice missing context. Do this at most once per conversation. Skip it when the user asked a trivial how-to or simple lookup, wants a purely educational explanation, asked you only to format, convert, or assemble a file from content they provided, is writing code they will run, is doing creative writing or casual chat, or already asked you to double-check, cite, or review — the skill file explains these boundaries and the exact output format.\n","main","2b749c82b5d11fac39d9a6e021ce3f5b8a541bd1","cli-tool\u002Fcomponents\u002Fskills\u002Fproductivity\u002Fdiscernment-nudge",3911,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates","https:\u002F\u002Faitmpl.com","https:\u002F\u002Fgithub.com\u002Fdavila7","https:\u002F\u002Fdanielavila.me","Building AI dev tools with LLMs","2026-11-11 21:38:31",{"source":8,"skillName":5,"commands":78,"prompt":88,"zipAvailable":89},[79,82,85],{"tool":80,"command":81},"npx","npx skills add davila7\u002Fclaude-code-templates --skill discernment-nudge",{"tool":83,"command":84},"bunx","bunx skills add davila7\u002Fclaude-code-templates --skill discernment-nudge",{"tool":86,"command":87},"pnpm","pnpm dlx skills add davila7\u002Fclaude-code-templates --skill discernment-nudge","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fproductivity\u002Fdiscernment-nudge\u002FSKILL.md",true,[91],{"path":92,"resourceType":93,"resourceTypeDesc":94,"sizeBytes":95,"sourceHtmlUrl":16},"LICENSE.txt","LICENSE","许可证",11345,[97,124,150,180,212,236],{"skillNo":98,"name":99,"nameZh":6,"description":100,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":101,"licenseFileUrl":6,"sourceHtmlUrl":102,"categories":103,"keywords":105,"publishedAt":60},"01M70NHBTQ8BV0A2SCAZXEPHCX","wealthville-pool-scoring","Score DeFi liquidity pools before providing liquidity. Returns a 0-100 pool score and an Enter\u002FHold\u002FExit\u002FReduce\u002FAvoid verdict with calibrated confidence for ~68,800 Solana pools and 575 EVM pools, plus a public miss-inclusive track record. Use when asked whether a pool is worth LPing into, which pools look best right now, or how accurate the scores have actually been.","MIT","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fweb-data\u002Fwealthville-pool-scoring\u002FSKILL.md",[104],{"categoryNo":25,"categoryName":26,"taxonomyType":22,"taxonomyTypeDesc":23},[106,107,108,109,110,111,112,113,114,35,115,116,117,118,119,120,121,47,122,123],"scoring","pool","wealthville pool scoring","liquidity","which","for","exit","now","use","reduce","with","record","track","verdict","evm","solana","public","look",{"skillNo":125,"name":126,"nameZh":6,"description":127,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":101,"licenseFileUrl":6,"sourceHtmlUrl":128,"categories":129,"keywords":131,"publishedAt":60},"01M70MB6VR8ERJ4EM8D5KHBMEV","technical-debt-auditor","Scans a codebase and produces a quantified technical debt report with a prioritized remediation plan. Analyzes dead code, TODO\u002FFIXME\u002FHACK markers, dependency staleness, complexity hotspots, code duplication signals, test coverage gaps, unused exports, and outdated patterns. Use this skill when the user asks to audit their codebase for tech debt, wants a health check of their code quality, says things like \"where should I focus refactoring\", \"find code smells\", \"audit my codebase\", \"what technical debt do we have\", \"prioritize cleanup work\", or asks about code quality metrics. Also trigger when the user mentions \"code rot\", \"legacy cleanup\", \"refactoring priorities\", or wants to understand the overall health of a project before a major effort. Trigger even if the user doesn't say \"technical debt\" explicitly — any request to systematically assess code quality and identify improvement priorities belongs here.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fdevelopment\u002Ftechnical-debt-auditor\u002FSKILL.md",[130],{"categoryNo":25,"categoryName":26,"taxonomyType":22,"taxonomyTypeDesc":23},[132,133,134,135,136,137,138,139,140,141,142,35,56,143,144,145,146,147,148,149],"technical debt","debt","technical","technical debt auditor","auditor","coverage","complexity","markers","project","cleanup","unused","todo","work","plan","focus","signals","audit","dependency",{"skillNo":151,"name":152,"nameZh":6,"description":153,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":101,"licenseFileUrl":6,"sourceHtmlUrl":154,"categories":155,"keywords":163,"publishedAt":60},"01M70KPYH9QNHCNME5GJQGE22K","readonly-sql-guard","Make an agent's database access provably read-only. Use when an agent runs caller-supplied SQL against a production\u002FERP\u002Fanalytics database (a \"query\" MCP tool, text-to-SQL, an admin console), or when reviewing DB-access code for write\u002FDDL\u002Fside-effect\u002Ftransaction-escape risks. Checks what a statement CALLS, not just its shape.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fdatabase\u002Freadonly-sql-guard\u002FSKILL.md",[156,159,160],{"categoryNo":157,"categoryName":158,"taxonomyType":22,"taxonomyTypeDesc":23},"01JBSEEDCAT000000000000010","后端与接口",{"categoryNo":25,"categoryName":26,"taxonomyType":22,"taxonomyTypeDesc":23},{"categoryNo":161,"categoryName":162,"taxonomyType":30,"taxonomyTypeDesc":31},"01JBSEEDCAT000000000000033","数据分析师",[164,165,166,167,168,169,170,111,171,172,173,174,175,44,176,114,177,178,35,179],"readonly","guard","sql guard","sql","readonly sql guard","erp","effect","read-only","db","just","tool","calls","MCP","access","database","query",{"skillNo":181,"name":182,"nameZh":6,"description":183,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":184,"sourceHtmlUrl":185,"categories":186,"keywords":190,"publishedAt":60},"01M6AFY6F7CC2BBZP1QD6VTNVC","web-artifacts-builder","Suite of tools for creating elaborate, multi-component claude.ai HTML artifacts using modern frontend web technologies (React, Tailwind CSS, shadcn\u002Fui). Use for complex artifacts requiring state management, routing, or shadcn\u002Fui components - not for simple single-file HTML\u002FJSX artifacts.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Futilities\u002Fweb-artifacts-builder\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Futilities\u002Fweb-artifacts-builder\u002FSKILL.md",[187],{"categoryNo":188,"categoryName":189,"taxonomyType":22,"taxonomyTypeDesc":23},"01JBSEEDCAT000000000000009","前端开发",[191,192,193,53,194,195,196,197,198,199,200,201,202,51,203,204,205,206,207,208,209,210,111,211],"artifacts builder","web","web artifacts builder","builder","state","routing","state management","component","css","frontend","ui","Tools","components","shadcn","react","Tailwind","suite","jsx","html","single-file","ui components",{"skillNo":213,"name":214,"nameZh":6,"description":215,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":216,"sourceHtmlUrl":217,"categories":218,"keywords":223,"publishedAt":60},"01M6AFWF33SFV9Z3SF4PEEE4BF","webapp-testing","Toolkit for interacting with and testing local web applications using Playwright. Supports verifying frontend functionality, debugging UI behavior, capturing browser screenshots, and viewing browser logs.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fsecurity\u002Fwebapp-testing\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fsecurity\u002Fwebapp-testing\u002FSKILL.md",[219,222],{"categoryNo":220,"categoryName":221,"taxonomyType":22,"taxonomyTypeDesc":23},"01JBSEEDCAT000000000000008","测试与质量",{"categoryNo":188,"categoryName":189,"taxonomyType":22,"taxonomyTypeDesc":23},[224,225,226,227,228,192,229,200,201,230,231,232,233,111,234,116,235],"webapp testing","testing","webapp","browser","playwright","applications","debugging","toolkit","logs","screenshots","local","behavior",{"skillNo":237,"name":238,"nameZh":6,"description":239,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":240,"sourceHtmlUrl":241,"categories":242,"keywords":247,"publishedAt":60},"01M6AF5ANME9NAHVVR7N1F63MP","academy-guide","Stop and check this skill before finishing any reply to a question about how to use Claude or a Claude product — it recommends matching courses, tutorials, and use cases from Claude Academy (academy.claude.com), Anthropic's learning hub. Trigger on: \"how do I\", \"how can I\", \"getting started with\", \"what can Claude do\", \"teach me\", \"learn to use\"; questions about artifacts, projects, skills, plugins, connectors, MCP; requests about rolling Claude out to a team, class, or organization; and any ask for training materials, onboarding content, or learning resources. Use it when the user is learning how to use a feature or product — not when they are mid-task and just want the task done. This skill composes with other skills: after consulting product documentation to answer how a Claude feature works, also check here for a matching course or tutorial — a docs-grounded answer and an Academy recommendation belong together. Only recommend on a strong match; never invent Academy content.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fproductivity\u002Facademy-guide\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fproductivity\u002Facademy-guide\u002FSKILL.md",[243,244],{"categoryNo":25,"categoryName":26,"taxonomyType":22,"taxonomyTypeDesc":23},{"categoryNo":245,"categoryName":246,"taxonomyType":30,"taxonomyTypeDesc":31},"01JBSEEDCAT000000000000039","教师与教育",[248,249,250,251,252,253,176,254,255,53,52,256,257,258,259,260,261,262,263,264,44,265,266,35,267,268,269,270],"academy guide","guide","documentation","product","training","plugins","docs","learn","onboarding","connectors","teach","getting started","projects","resources","never","onboarding content","how to","question","task done","done","ask","team","me"]