[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M7DKQP7T1AJGE5G2TZGS183M":3,"skill-same:01M7DKQP7T1AJGE5G2TZGS183M":76},{"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":19,"publishedAt":20,"linkOnly":21,"markdownContent":22,"markdownBody":23,"frontMatter":24,"refName":25,"commitSha":26,"skillDirectory":27,"forksCount":14,"repositoryHtmlUrl":28,"repositoryHomepageUrl":6,"ownerHtmlUrl":29,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":30,"install":31,"resources":44},"01M7DKQP7T1AJGE5G2TZGS183M","tdd",null,"Test-driven development. Use when the user wants to build features or fix bugs test-first, mentions \"red-green-refactor\", or wants integration tests.","vc999999999\u002Fskillflux-catalog","vc999999999","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F104196942?v=4","01M7DKFQK2JRAY2C9EMD7FHBWG","jiangbing","01M7DKFQKQA5E0BWZ1WQY4G9WA",0,"MIT","https:\u002F\u002Fgithub.com\u002Fvc999999999\u002Fskillflux-catalog\u002Fblob\u002Fmain\u002Fskills\u002Fdevelopment\u002Ftdd\u002F1.0.0\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002Fvc999999999\u002Fskillflux-catalog\u002Fblob\u002Fmain\u002Fskills\u002Fdevelopment\u002Ftdd\u002F1.0.0\u002FSKILL.md",[],[],"2026-11-15 14:19:17",false,"---\nname: tdd\ndescription: Test-driven development. Use when the user wants to build features or fix bugs test-first, mentions \"red-green-refactor\", or wants integration tests.\n---\n\n# Test-Driven Development\n\nTDD is the red → green loop. This skill is the reference that makes that loop produce tests worth keeping: what a good test is, where tests go, the anti-patterns, and the rules of the loop. Every section applies on every cycle: consult them before and during the loop, not after.\n\nWhen exploring the codebase, read `GLOSSARY.md` (if it exists) so test names and interface vocabulary match the project's domain language, and respect ADRs in the area you're touching.\n\n## What a good test is\n\nTests verify behavior through public interfaces, not implementation details. Code can change entirely; tests shouldn't. A good test reads like a specification: \"user can checkout with valid cart\" tells you exactly what capability exists, and it survives refactors because it doesn't care about internal structure.\n\nSee [tests.md](tests.md) for examples and [mocking.md](mocking.md) for mocking guidelines.\n\n## Seams: where tests go\n\nA **seam** is the public boundary you test at: the interface where you observe behavior without reaching inside. Tests live at seams, never against internals.\n\n**Test only at pre-agreed seams.** Before writing any test, write down the seams under test and confirm them with the user. No test is written at an unconfirmed seam. You can't test everything, so agreeing the seams up front is how testing effort lands on the critical paths and complex logic instead of every edge case.\n\nAsk: \"What's the public interface, and which seams should we test?\" Give each proposed seam a one-line note on what it catches and what it misses.\n\nWhen the shape of that interface is itself in question (how deep the module is, where the seam belongs, what the interface should expose), call the Skill tool with \"codebase-design\" for the vocabulary. It is the shared source of the module, interface, depth, seam, adapter, leverage and locality terms, and it is a reference to consult, not a session to run.\n\n## Anti-patterns\n\n- **Implementation-coupled**: mocks internal collaborators, tests private methods, or verifies through a side channel (querying the database instead of using the interface). The tell: the test breaks when you refactor but behavior hasn't changed.\n- **Tautological**: the assertion recomputes the expected value the way the code does (`expect(add(a, b)).toBe(a + b)`, a snapshot derived by hand the same way, a constant asserted equal to itself), so it passes by construction and can never disagree with the code. Expected values must come from an independent source of truth: a known-good literal, a worked example, the spec.\n- **Horizontal slicing**: writing all tests first, then all implementation. Bulk tests verify _imagined_ behavior: you test the _shape_ of things rather than user-facing behavior, the tests go insensitive to real changes, and you commit to test structure before understanding the implementation. Work in **vertical slices** instead: one test → one implementation → repeat, each test a **tracer bullet** that responds to what the last cycle taught you.\n\n## Rules of the loop\n\n- **Red before green.** Write the failing test first, then only enough code to pass it. Don't anticipate future tests or add speculative features.\n- **One slice at a time.** One seam, one test, one minimal implementation per cycle.\n- **Refactoring is not part of the loop.** It belongs to the review stage (see the `code-review` skill), not the red → green implementation cycle.\n","# Test-Driven Development\n\nTDD is the red → green loop. This skill is the reference that makes that loop produce tests worth keeping: what a good test is, where tests go, the anti-patterns, and the rules of the loop. Every section applies on every cycle: consult them before and during the loop, not after.\n\nWhen exploring the codebase, read `GLOSSARY.md` (if it exists) so test names and interface vocabulary match the project's domain language, and respect ADRs in the area you're touching.\n\n## What a good test is\n\nTests verify behavior through public interfaces, not implementation details. Code can change entirely; tests shouldn't. A good test reads like a specification: \"user can checkout with valid cart\" tells you exactly what capability exists, and it survives refactors because it doesn't care about internal structure.\n\nSee [tests.md](tests.md) for examples and [mocking.md](mocking.md) for mocking guidelines.\n\n## Seams: where tests go\n\nA **seam** is the public boundary you test at: the interface where you observe behavior without reaching inside. Tests live at seams, never against internals.\n\n**Test only at pre-agreed seams.** Before writing any test, write down the seams under test and confirm them with the user. No test is written at an unconfirmed seam. You can't test everything, so agreeing the seams up front is how testing effort lands on the critical paths and complex logic instead of every edge case.\n\nAsk: \"What's the public interface, and which seams should we test?\" Give each proposed seam a one-line note on what it catches and what it misses.\n\nWhen the shape of that interface is itself in question (how deep the module is, where the seam belongs, what the interface should expose), call the Skill tool with \"codebase-design\" for the vocabulary. It is the shared source of the module, interface, depth, seam, adapter, leverage and locality terms, and it is a reference to consult, not a session to run.\n\n## Anti-patterns\n\n- **Implementation-coupled**: mocks internal collaborators, tests private methods, or verifies through a side channel (querying the database instead of using the interface). The tell: the test breaks when you refactor but behavior hasn't changed.\n- **Tautological**: the assertion recomputes the expected value the way the code does (`expect(add(a, b)).toBe(a + b)`, a snapshot derived by hand the same way, a constant asserted equal to itself), so it passes by construction and can never disagree with the code. Expected values must come from an independent source of truth: a known-good literal, a worked example, the spec.\n- **Horizontal slicing**: writing all tests first, then all implementation. Bulk tests verify _imagined_ behavior: you test the _shape_ of things rather than user-facing behavior, the tests go insensitive to real changes, and you commit to test structure before understanding the implementation. Work in **vertical slices** instead: one test → one implementation → repeat, each test a **tracer bullet** that responds to what the last cycle taught you.\n\n## Rules of the loop\n\n- **Red before green.** Write the failing test first, then only enough code to pass it. Don't anticipate future tests or add speculative features.\n- **One slice at a time.** One seam, one test, one minimal implementation per cycle.\n- **Refactoring is not part of the loop.** It belongs to the review stage (see the `code-review` skill), not the red → green implementation cycle.\n",{"name":5,"description":7},"main","18f52829480a4e8df7dd5d9c13ce3b5284311891","skills\u002Fdevelopment\u002Ftdd\u002F1.0.0","https:\u002F\u002Fgithub.com\u002Fvc999999999\u002Fskillflux-catalog","https:\u002F\u002Fgithub.com\u002Fvc999999999","2026-10-11 20:16:46",{"source":8,"skillName":5,"commands":32,"prompt":42,"zipAvailable":43},[33,36,39],{"tool":34,"command":35},"npx","npx skills add vc999999999\u002Fskillflux-catalog --skill tdd",{"tool":37,"command":38},"bunx","bunx skills add vc999999999\u002Fskillflux-catalog --skill tdd",{"tool":40,"command":41},"pnpm","pnpm dlx skills add vc999999999\u002Fskillflux-catalog --skill tdd","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fvc999999999\u002Fskillflux-catalog\u002Fblob\u002Fmain\u002Fskills\u002Fdevelopment\u002Ftdd\u002F1.0.0\u002FSKILL.md",true,[45,51,55,60,64,68,72],{"path":46,"resourceType":47,"resourceTypeDesc":48,"sizeBytes":49,"sourceHtmlUrl":50},"agents\u002Fopenai.yaml","REFERENCE","参考文档",87,"https:\u002F\u002Fgithub.com\u002Fvc999999999\u002Fskillflux-catalog\u002Fblob\u002Fmain\u002Fskills\u002Fdevelopment\u002Ftdd\u002F1.0.0\u002Fagents\u002Fopenai.yaml",{"path":52,"resourceType":47,"resourceTypeDesc":48,"sizeBytes":53,"sourceHtmlUrl":54},"GLOSSARY.md",1768,"https:\u002F\u002Fgithub.com\u002Fvc999999999\u002Fskillflux-catalog\u002Fblob\u002Fmain\u002Fskills\u002Fdevelopment\u002Ftdd\u002F1.0.0\u002FGLOSSARY.md",{"path":56,"resourceType":57,"resourceTypeDesc":58,"sizeBytes":59,"sourceHtmlUrl":16},"LICENSE.txt","LICENSE","许可证",1068,{"path":61,"resourceType":47,"resourceTypeDesc":48,"sizeBytes":62,"sourceHtmlUrl":63},"mocking.md",1481,"https:\u002F\u002Fgithub.com\u002Fvc999999999\u002Fskillflux-catalog\u002Fblob\u002Fmain\u002Fskills\u002Fdevelopment\u002Ftdd\u002F1.0.0\u002Fmocking.md",{"path":65,"resourceType":47,"resourceTypeDesc":48,"sizeBytes":66,"sourceHtmlUrl":67},"skillflux.json",1010,"https:\u002F\u002Fgithub.com\u002Fvc999999999\u002Fskillflux-catalog\u002Fblob\u002Fmain\u002Fskills\u002Fdevelopment\u002Ftdd\u002F1.0.0\u002Fskillflux.json",{"path":69,"resourceType":47,"resourceTypeDesc":48,"sizeBytes":70,"sourceHtmlUrl":71},"skillflux.review.json",2343,"https:\u002F\u002Fgithub.com\u002Fvc999999999\u002Fskillflux-catalog\u002Fblob\u002Fmain\u002Fskills\u002Fdevelopment\u002Ftdd\u002F1.0.0\u002Fskillflux.review.json",{"path":73,"resourceType":47,"resourceTypeDesc":48,"sizeBytes":74,"sourceHtmlUrl":75},"tests.md",2214,"https:\u002F\u002Fgithub.com\u002Fvc999999999\u002Fskillflux-catalog\u002Fblob\u002Fmain\u002Fskills\u002Fdevelopment\u002Ftdd\u002F1.0.0\u002Ftests.md",[77,85,93,101,109,117],{"skillNo":78,"name":79,"nameZh":6,"description":80,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":81,"sourceHtmlUrl":82,"categories":83,"keywords":84,"publishedAt":20},"01M7DKTKB8RM9NFNA9VD5QNM59","scientific-schematics","Generates scientific diagram drafts using Nano Banana 2 AI with smart iterative refinement. Uses Gemini 3.7 Flash for quality review. Refines when the review requests improvement, with at most two generations. Specialized in neural network architectures, system diagrams, flowcharts, biological pathways, and complex scientific visualizations.","https:\u002F\u002Fgithub.com\u002Fvc999999999\u002Fskillflux-catalog\u002Fblob\u002Fmain\u002Fskills\u002Fresearch\u002Fscientific-schematics\u002F1.0.0\u002FLICENSE.md","https:\u002F\u002Fgithub.com\u002Fvc999999999\u002Fskillflux-catalog\u002Fblob\u002Fmain\u002Fskills\u002Fresearch\u002Fscientific-schematics\u002F1.0.0\u002FSKILL.md",[],[],{"skillNo":86,"name":87,"nameZh":6,"description":88,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":89,"sourceHtmlUrl":90,"categories":91,"keywords":92,"publishedAt":20},"01M7DKTE1BMWDNRQ6JHSB8YZVM","scientific-critical-thinking","Evaluates scientific claims and evidence quality. Applies to experimental design validity, biases and confounders, statistical interpretation, evidence grading frameworks (GRADE, Cochrane Risk of Bias), and teaching critical analysis. Supports evidence appraisal and identifying flaws; formal peer review writing belongs to peer-review.","https:\u002F\u002Fgithub.com\u002Fvc999999999\u002Fskillflux-catalog\u002Fblob\u002Fmain\u002Fskills\u002Fresearch\u002Fscientific-critical-thinking\u002F1.0.0\u002FLICENSE.md","https:\u002F\u002Fgithub.com\u002Fvc999999999\u002Fskillflux-catalog\u002Fblob\u002Fmain\u002Fskills\u002Fresearch\u002Fscientific-critical-thinking\u002F1.0.0\u002FSKILL.md",[],[],{"skillNo":94,"name":95,"nameZh":6,"description":96,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":97,"sourceHtmlUrl":98,"categories":99,"keywords":100,"publishedAt":20},"01M7DKT74MD9SFF7K4ZGQJ5PAY","scientific-brainstorming","Facilitates evidence-aware scientific ideation with independent generation, structured discussion, explicit assumptions, transparent evaluation, adversarial review, and decision logs. Use for early-stage research brainstorming or prioritizing candidate directions; hand off empirical validation, study design, ethics or regulatory review, and clinical questions to appropriate experts or skills.","https:\u002F\u002Fgithub.com\u002Fvc999999999\u002Fskillflux-catalog\u002Fblob\u002Fmain\u002Fskills\u002Fresearch\u002Fscientific-brainstorming\u002F1.0.0\u002FLICENSE.md","https:\u002F\u002Fgithub.com\u002Fvc999999999\u002Fskillflux-catalog\u002Fblob\u002Fmain\u002Fskills\u002Fresearch\u002Fscientific-brainstorming\u002F1.0.0\u002FSKILL.md",[],[],{"skillNo":102,"name":103,"nameZh":6,"description":104,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":105,"sourceHtmlUrl":106,"categories":107,"keywords":108,"publishedAt":20},"01M7DKT1837E2PSV5Y6EQV4A4B","aer-tables-figures","Use when constructing or revising regression tables, descriptive statistics tables, or figures after results are estimated and before submission for an AER, AER:Insights, or AEJ manuscript. Implements AER booktabs house style, regression-table layout, and figure-note conventions.","https:\u002F\u002Fgithub.com\u002Fvc999999999\u002Fskillflux-catalog\u002Fblob\u002Fmain\u002Fskills\u002Fresearch\u002Faer-tables-figures\u002F1.0.0\u002FLICENSE","https:\u002F\u002Fgithub.com\u002Fvc999999999\u002Fskillflux-catalog\u002Fblob\u002Fmain\u002Fskills\u002Fresearch\u002Faer-tables-figures\u002F1.0.0\u002FSKILL.md",[],[],{"skillNo":110,"name":111,"nameZh":6,"description":112,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":113,"sourceHtmlUrl":114,"categories":115,"keywords":116,"publishedAt":20},"01M7DKSW7MJQ8A88TJC1H293PB","aer-statspai","Use when aer-identification has fixed the design, after methodology choice and before aer-robustness or aer-tables-figures, to run an AER-track analysis with StatsPAI — the agent-native Python engine and MCP server for causal inference, robustness, sensitivity, and publication-ready table export.","https:\u002F\u002Fgithub.com\u002Fvc999999999\u002Fskillflux-catalog\u002Fblob\u002Fmain\u002Fskills\u002Fresearch\u002Faer-statspai\u002F1.0.0\u002FLICENSE","https:\u002F\u002Fgithub.com\u002Fvc999999999\u002Fskillflux-catalog\u002Fblob\u002Fmain\u002Fskills\u002Fresearch\u002Faer-statspai\u002F1.0.0\u002FSKILL.md",[],[],{"skillNo":118,"name":119,"nameZh":6,"description":120,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":121,"sourceHtmlUrl":122,"categories":123,"keywords":124,"publishedAt":20},"01M7DKSQKV1429VSPVVHAM1QQM","aer-robustness","Use when the main empirical results exist but the manuscript lacks the robustness, heterogeneity, mechanism, and placebo checks that AER referees will demand. Apply after aer-identification and before aer-introduction so that the value-added paragraph can reference these tests.","https:\u002F\u002Fgithub.com\u002Fvc999999999\u002Fskillflux-catalog\u002Fblob\u002Fmain\u002Fskills\u002Fresearch\u002Faer-robustness\u002F1.0.0\u002FLICENSE","https:\u002F\u002Fgithub.com\u002Fvc999999999\u002Fskillflux-catalog\u002Fblob\u002Fmain\u002Fskills\u002Fresearch\u002Faer-robustness\u002F1.0.0\u002FSKILL.md",[],[]]