[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill-braintrust-braintrust-define-eval-objective":3,"mdc-ltxy4e-key":38,"related-org-braintrust-braintrust-define-eval-objective":322,"related-repo-braintrust-braintrust-define-eval-objective":489},{"slug":4,"name":4,"fn":5,"description":6,"org":7,"tags":12,"stars":27,"repoUrl":28,"updatedAt":29,"license":30,"forks":31,"topics":32,"repo":33,"sourceUrl":36,"mdContent":37},"braintrust-define-eval-objective","define LLM evaluation objectives","Create, edit, or audit an eval objective by working backward from a product decision to the target outcome, construct, population, intended claim, and verification-versus-validation questions. Use when a team is unsure what an eval should establish, asks \"what are we actually trying to measure,\" \"is this eval measuring the right thing,\" \"does this benchmark support our claim,\" or needs to turn a product goal into an eval objective and state which claims are out of scope. Do not use to select detailed metrics, design datasets, or implement scorers.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},"braintrust","Braintrust","https:\u002F\u002Fpexgzepcugksgbtrxkhf.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Forg-logos\u002Fbraintrust.png","braintrustdata",[13,17,20,23,26],{"name":14,"slug":15,"type":16},"LLM","llm","tag",{"name":18,"slug":19,"type":16},"Evals","evals",{"name":21,"slug":22,"type":16},"Product Management","product-management",{"name":24,"slug":25,"type":16},"Strategy","strategy",{"name":9,"slug":8,"type":16},7,"https:\u002F\u002Fgithub.com\u002Fbraintrustdata\u002Feval-library","2026-08-20T03:53:00.07097",null,0,[],{"repoUrl":28,"stars":27,"forks":31,"topics":34,"description":35},[],"Braintrust eval skills library","https:\u002F\u002Fgithub.com\u002Fbraintrustdata\u002Feval-library\u002Ftree\u002FHEAD\u002Fskills\u002Fbraintrust-define-eval-objective","---\nname: braintrust-define-eval-objective\ndescription: >-\n  Create, edit, or audit an eval objective by working backward from a product decision to the\n  target outcome, construct, population, intended claim, and verification-versus-validation\n  questions. Use when a team is unsure what an eval should establish, asks \"what are we\n  actually trying to measure,\" \"is this eval measuring the right thing,\" \"does this benchmark\n  support our claim,\" or needs to turn a product goal into an eval objective and state which\n  claims are out of scope. Do not use to select detailed metrics, design datasets, or implement\n  scorers.\n---\n\n# Define the eval objective\n\nContract: `references\u002Finteraction-contract.md`. Calibration, templates, provenance: `references\u002Fobjective-card.md`.\n\n## Trigger\n\n- Nobody can name the decision the eval supports.\n- A benchmark score being used to argue a claim it may not license.\n- A product goal (\"make the agent more helpful\") that needs to become measurable.\n\n## Do\n\n1. State the decision in one sentence. If several are plausible, ask which one the eval must\n   support — the highest-information question available here.\n2. Write one sentence each: **construct**, **population** (and its weighting), **intended use\n   and non-use**.\n3. Answer both questions, not just the first. **Verification:** do the scorers hold the spec?\n   **Validation:** do these scorers reflect outcomes users actually need? Validation is the\n   half teams skip.\n4. Where validation has no evidence behind it, say so and elicit criteria from experts and\n   real user signals rather than asserting what users want. Label unevidenced desires `Assumed`.\n5. Write **supported** and **unsupported** claims as separate lists, then name the evidence\n   that would change the decision. An objective no result could overturn is not an objective.\n\n## Avoid\n\n- Do not pick metrics, thresholds, or scoring methods.\n- Do not let the population drift toward whatever data is easy to collect; most bad datasets\n  fail here, before a single item exists.\n- Do not accept a benchmark name in place of a product property.\n- Do not imply model-level claims when the object under test is a system — harness, tools, and\n  context are part of what you measure.\n\n## Check\n\n- Decision, construct, population, use, and non-use each in a sentence.\n- Supported and unsupported claims listed separately.\n- Both V&V questions answered, with validation's evidence source named.\n\n## Risk\n\n- An ambiguous decision produces an eval that is locally measurable and strategically\n  irrelevant.\n- A construct defined by convenience gets optimized: the score improves, the product does not.\n- Loose population statements (\"our users\") license claims far broader than the sample\n  supports, and nobody notices until the number goes external.\n\n## Braintrust\n\n**Write the decision and intended claim into the experiment description** so the\npre-registered claim travels with its results — a claim in a doc gets reinterpreted after the\nfact. Record the population and its weighting in **dataset metadata**. Make non-use\nenforceable: name datasets for their population (`prod-weighted-py-backend`, not `eval-set-2`)\nso quoting a number out of scope is visibly wrong, and keep benchmark scaffolding in\n**separate, clearly named datasets** — once mixed with production-derived items, \"measures our\nconstruct\" vs. \"measures the benchmark's\" is unrecoverable. Validation cannot be answered\noffline: it needs production scoring sliced by escalation, reopen, and abandonment. If nothing\nscores production yet, validation is `Not yet measurable`, and standing up online scoring is the\nblocker.\n",{"data":39,"body":40},{"name":4,"description":6},{"type":41,"children":42},"root",[43,52,75,82,102,108,193,199,222,228,246,252,270,274],{"type":44,"tag":45,"props":46,"children":48},"element","h1",{"id":47},"define-the-eval-objective",[49],{"type":50,"value":51},"text","Define the eval objective",{"type":44,"tag":53,"props":54,"children":55},"p",{},[56,58,65,67,73],{"type":50,"value":57},"Contract: ",{"type":44,"tag":59,"props":60,"children":62},"code",{"className":61},[],[63],{"type":50,"value":64},"references\u002Finteraction-contract.md",{"type":50,"value":66},". Calibration, templates, provenance: ",{"type":44,"tag":59,"props":68,"children":70},{"className":69},[],[71],{"type":50,"value":72},"references\u002Fobjective-card.md",{"type":50,"value":74},".",{"type":44,"tag":76,"props":77,"children":79},"h2",{"id":78},"trigger",[80],{"type":50,"value":81},"Trigger",{"type":44,"tag":83,"props":84,"children":85},"ul",{},[86,92,97],{"type":44,"tag":87,"props":88,"children":89},"li",{},[90],{"type":50,"value":91},"Nobody can name the decision the eval supports.",{"type":44,"tag":87,"props":93,"children":94},{},[95],{"type":50,"value":96},"A benchmark score being used to argue a claim it may not license.",{"type":44,"tag":87,"props":98,"children":99},{},[100],{"type":50,"value":101},"A product goal (\"make the agent more helpful\") that needs to become measurable.",{"type":44,"tag":76,"props":103,"children":105},{"id":104},"do",[106],{"type":50,"value":107},"Do",{"type":44,"tag":109,"props":110,"children":111},"ol",{},[112,117,143,162,174],{"type":44,"tag":87,"props":113,"children":114},{},[115],{"type":50,"value":116},"State the decision in one sentence. If several are plausible, ask which one the eval must\nsupport — the highest-information question available here.",{"type":44,"tag":87,"props":118,"children":119},{},[120,122,128,130,135,137,142],{"type":50,"value":121},"Write one sentence each: ",{"type":44,"tag":123,"props":124,"children":125},"strong",{},[126],{"type":50,"value":127},"construct",{"type":50,"value":129},", ",{"type":44,"tag":123,"props":131,"children":132},{},[133],{"type":50,"value":134},"population",{"type":50,"value":136}," (and its weighting), ",{"type":44,"tag":123,"props":138,"children":139},{},[140],{"type":50,"value":141},"intended use\nand non-use",{"type":50,"value":74},{"type":44,"tag":87,"props":144,"children":145},{},[146,148,153,155,160],{"type":50,"value":147},"Answer both questions, not just the first. ",{"type":44,"tag":123,"props":149,"children":150},{},[151],{"type":50,"value":152},"Verification:",{"type":50,"value":154}," do the scorers hold the spec?\n",{"type":44,"tag":123,"props":156,"children":157},{},[158],{"type":50,"value":159},"Validation:",{"type":50,"value":161}," do these scorers reflect outcomes users actually need? Validation is the\nhalf teams skip.",{"type":44,"tag":87,"props":163,"children":164},{},[165,167,173],{"type":50,"value":166},"Where validation has no evidence behind it, say so and elicit criteria from experts and\nreal user signals rather than asserting what users want. Label unevidenced desires ",{"type":44,"tag":59,"props":168,"children":170},{"className":169},[],[171],{"type":50,"value":172},"Assumed",{"type":50,"value":74},{"type":44,"tag":87,"props":175,"children":176},{},[177,179,184,186,191],{"type":50,"value":178},"Write ",{"type":44,"tag":123,"props":180,"children":181},{},[182],{"type":50,"value":183},"supported",{"type":50,"value":185}," and ",{"type":44,"tag":123,"props":187,"children":188},{},[189],{"type":50,"value":190},"unsupported",{"type":50,"value":192}," claims as separate lists, then name the evidence\nthat would change the decision. An objective no result could overturn is not an objective.",{"type":44,"tag":76,"props":194,"children":196},{"id":195},"avoid",[197],{"type":50,"value":198},"Avoid",{"type":44,"tag":83,"props":200,"children":201},{},[202,207,212,217],{"type":44,"tag":87,"props":203,"children":204},{},[205],{"type":50,"value":206},"Do not pick metrics, thresholds, or scoring methods.",{"type":44,"tag":87,"props":208,"children":209},{},[210],{"type":50,"value":211},"Do not let the population drift toward whatever data is easy to collect; most bad datasets\nfail here, before a single item exists.",{"type":44,"tag":87,"props":213,"children":214},{},[215],{"type":50,"value":216},"Do not accept a benchmark name in place of a product property.",{"type":44,"tag":87,"props":218,"children":219},{},[220],{"type":50,"value":221},"Do not imply model-level claims when the object under test is a system — harness, tools, and\ncontext are part of what you measure.",{"type":44,"tag":76,"props":223,"children":225},{"id":224},"check",[226],{"type":50,"value":227},"Check",{"type":44,"tag":83,"props":229,"children":230},{},[231,236,241],{"type":44,"tag":87,"props":232,"children":233},{},[234],{"type":50,"value":235},"Decision, construct, population, use, and non-use each in a sentence.",{"type":44,"tag":87,"props":237,"children":238},{},[239],{"type":50,"value":240},"Supported and unsupported claims listed separately.",{"type":44,"tag":87,"props":242,"children":243},{},[244],{"type":50,"value":245},"Both V&V questions answered, with validation's evidence source named.",{"type":44,"tag":76,"props":247,"children":249},{"id":248},"risk",[250],{"type":50,"value":251},"Risk",{"type":44,"tag":83,"props":253,"children":254},{},[255,260,265],{"type":44,"tag":87,"props":256,"children":257},{},[258],{"type":50,"value":259},"An ambiguous decision produces an eval that is locally measurable and strategically\nirrelevant.",{"type":44,"tag":87,"props":261,"children":262},{},[263],{"type":50,"value":264},"A construct defined by convenience gets optimized: the score improves, the product does not.",{"type":44,"tag":87,"props":266,"children":267},{},[268],{"type":50,"value":269},"Loose population statements (\"our users\") license claims far broader than the sample\nsupports, and nobody notices until the number goes external.",{"type":44,"tag":76,"props":271,"children":272},{"id":8},[273],{"type":50,"value":9},{"type":44,"tag":53,"props":275,"children":276},{},[277,282,284,289,291,297,299,305,307,312,314,320],{"type":44,"tag":123,"props":278,"children":279},{},[280],{"type":50,"value":281},"Write the decision and intended claim into the experiment description",{"type":50,"value":283}," so the\npre-registered claim travels with its results — a claim in a doc gets reinterpreted after the\nfact. Record the population and its weighting in ",{"type":44,"tag":123,"props":285,"children":286},{},[287],{"type":50,"value":288},"dataset metadata",{"type":50,"value":290},". Make non-use\nenforceable: name datasets for their population (",{"type":44,"tag":59,"props":292,"children":294},{"className":293},[],[295],{"type":50,"value":296},"prod-weighted-py-backend",{"type":50,"value":298},", not ",{"type":44,"tag":59,"props":300,"children":302},{"className":301},[],[303],{"type":50,"value":304},"eval-set-2",{"type":50,"value":306},")\nso quoting a number out of scope is visibly wrong, and keep benchmark scaffolding in\n",{"type":44,"tag":123,"props":308,"children":309},{},[310],{"type":50,"value":311},"separate, clearly named datasets",{"type":50,"value":313}," — once mixed with production-derived items, \"measures our\nconstruct\" vs. \"measures the benchmark's\" is unrecoverable. Validation cannot be answered\noffline: it needs production scoring sliced by escalation, reopen, and abandonment. If nothing\nscores production yet, validation is ",{"type":44,"tag":59,"props":315,"children":317},{"className":316},[],[318],{"type":50,"value":319},"Not yet measurable",{"type":50,"value":321},", and standing up online scoring is the\nblocker.",{"items":323,"total":488},[324,340,356,370,386,394,408,420,434,450,462,474],{"slug":325,"name":325,"fn":326,"description":327,"org":328,"tags":329,"stars":337,"repoUrl":338,"updatedAt":339},"troubleshoot-braintrust-mcp","configure and troubleshoot Braintrust MCP servers","This plugin auto-configures a \"braintrust\" MCP server. If you can't see it or reach it, activate this skill\n",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[330,331,334],{"name":9,"slug":8,"type":16},{"name":332,"slug":333,"type":16},"Debugging","debugging",{"name":335,"slug":336,"type":16},"MCP","mcp",18,"https:\u002F\u002Fgithub.com\u002Fbraintrustdata\u002Fbraintrust-claude-plugin","2026-07-12T08:36:13.889274",{"slug":341,"name":341,"fn":342,"description":343,"org":344,"tags":345,"stars":27,"repoUrl":28,"updatedAt":355},"braintrust-analyze-eval-experiment","analyze LLM and agent eval experiments","Analyze completed LLM or agent eval experiments using uncertainty-aware and decision-relevant methods. Use to audit run completeness and pairing, calculate confidence intervals, run paired comparisons, report wins, losses, and ties, incorporate run-to-run variance, handle multiple comparisons, inspect subgroup performance, and test fragility to favorable slices. Use when results already exist and someone asks what they mean, whether a difference is real, or which model won. Do not use to design an experiment that has not yet collected results.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[346,349,350,351,352],{"name":347,"slug":348,"type":16},"Analysis","analysis",{"name":9,"slug":8,"type":16},{"name":18,"slug":19,"type":16},{"name":14,"slug":15,"type":16},{"name":353,"slug":354,"type":16},"Statistics","statistics","2026-08-20T03:53:01.13806",{"slug":357,"name":357,"fn":358,"description":359,"org":360,"tags":361,"stars":27,"repoUrl":28,"updatedAt":369},"braintrust-attribute-multi-variable-change","attribute performance changes to multiple variables","Attribute an observed change when several things moved at once — model plus prompt plus tools, a provider migration, a framework upgrade, or a vendor swap that bundles serving stack with model. Use when asked which part of a change caused the result, when a comparison's arms differ in more than one way, when a treatment has no uniform implementation across vendors, or when a serving-stack difference is confounded with a model difference. Do not use for a clean single-variable comparison, or to design an experiment that has not yet run.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[362,363,364,365,366],{"name":347,"slug":348,"type":16},{"name":9,"slug":8,"type":16},{"name":18,"slug":19,"type":16},{"name":14,"slug":15,"type":16},{"name":367,"slug":368,"type":16},"Performance","performance","2026-08-20T03:53:40.036077",{"slug":371,"name":371,"fn":372,"description":373,"org":374,"tags":375,"stars":27,"repoUrl":28,"updatedAt":385},"braintrust-build-eval-dataset","create and manage LLM eval datasets","Create, edit, audit, or compare eval datasets for LLM applications and agents, including target-population definition, case sourcing from production traces, stratified sampling, label provenance and label audits, expected values as constraints for open-ended tasks, dev\u002Ftest splits, contamination and leakage controls, headroom checks, refresh policy, and datasheets. Use when working on the content or lifecycle of an eval dataset. Do not use for sample-size or power calculations, scorer implementation, or open-ended adversarial failure discovery.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[376,379,380,383,384],{"name":377,"slug":378,"type":16},"Agents","agents",{"name":9,"slug":8,"type":16},{"name":381,"slug":382,"type":16},"Datasets","datasets",{"name":18,"slug":19,"type":16},{"name":14,"slug":15,"type":16},"2026-08-20T03:53:33.304815",{"slug":4,"name":4,"fn":5,"description":6,"org":387,"tags":388,"stars":27,"repoUrl":28,"updatedAt":29},{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[389,390,391,392,393],{"name":9,"slug":8,"type":16},{"name":18,"slug":19,"type":16},{"name":14,"slug":15,"type":16},{"name":21,"slug":22,"type":16},{"name":24,"slug":25,"type":16},{"slug":395,"name":395,"fn":396,"description":397,"org":398,"tags":399,"stars":27,"repoUrl":28,"updatedAt":407},"braintrust-define-eval-release-gate","configure release gates for LLM applications","Create, edit, audit, or apply release gates for LLM applications and agents. Use to combine minimum meaningful improvement, statistical significance, regression rate, subgroup consistency, worst-run stability, all-attempts reliability, safety upper bounds, latency, and cost into an explicit ship-or-hold policy, to turn metrics into a CI gate, or to explain why a candidate failed a gate and what evidence would justify reconsideration. Do not use for general result analysis without a deployment decision.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[400,401,402,405,406],{"name":377,"slug":378,"type":16},{"name":9,"slug":8,"type":16},{"name":403,"slug":404,"type":16},"CI\u002FCD","ci-cd",{"name":18,"slug":19,"type":16},{"name":14,"slug":15,"type":16},"2026-08-20T03:53:15.686158",{"slug":409,"name":409,"fn":410,"description":411,"org":412,"tags":413,"stars":27,"repoUrl":28,"updatedAt":419},"braintrust-deploy-evaluator","deploy evaluators to Braintrust","Take a validated scorer or classifier from definition to running instrument in Braintrust — scope selection, inline testing before saving, saving as an evaluator, attaching an online-scoring rule, activating it for new traffic, and backfilling history with a rewind. Use when a scorer needs to actually run against production logs, when an online-scoring rule needs to be created or changed, or when historical traces need scoring. Do not use to decide what the scorer should measure, to write its rubric, or to establish that it agrees with human judgment.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[414,415,418],{"name":9,"slug":8,"type":16},{"name":416,"slug":417,"type":16},"Deployment","deployment",{"name":18,"slug":19,"type":16},"2026-08-20T03:53:32.558937",{"slug":421,"name":421,"fn":422,"description":423,"org":424,"tags":425,"stars":27,"repoUrl":28,"updatedAt":433},"braintrust-design-eval-experiment","design controlled LLM eval experiments","Design or audit controlled eval experiments for model, prompt, retrieval, tool, guardrail, or agent-architecture changes. Use before data collection to state directional and minimum-effect hypotheses, name independent, dependent, and control variables including the serving environment and tool surface, choose paired designs, set repetitions and allocation, distinguish exploratory from confirmatory comparisons, and pre-specify stopping, exclusion, multiplicity, and analysis rules. Do not use primarily to analyze results already collected.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[426,427,428,431,432],{"name":9,"slug":8,"type":16},{"name":18,"slug":19,"type":16},{"name":429,"slug":430,"type":16},"Experiments","experiments",{"name":14,"slug":15,"type":16},{"name":24,"slug":25,"type":16},"2026-08-20T03:53:36.534554",{"slug":435,"name":435,"fn":436,"description":437,"org":438,"tags":439,"stars":27,"repoUrl":28,"updatedAt":449},"braintrust-design-eval-instrumentation","design trace and evaluation dataset schemas","Design the trace and eval-dataset schema for an LLM app or agent, and wire the system to emit it. Use when deciding what to log, designing a trace schema, setting up tracing or observability before evals, or when failures cannot be debugged or sliced from existing traces — covering inputs, outputs, spans for tool and LLM calls, state changes, metadata, resolved configuration, serving path, tool manifest, per-item status, attachments, and subgroup variables. Do not use to decide what the evidence should mean, or to write scorers.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[440,441,442,443,446],{"name":9,"slug":8,"type":16},{"name":381,"slug":382,"type":16},{"name":18,"slug":19,"type":16},{"name":444,"slug":445,"type":16},"Observability","observability",{"name":447,"slug":448,"type":16},"Tracing","tracing","2026-08-20T03:53:37.274703",{"slug":451,"name":451,"fn":452,"description":453,"org":454,"tags":455,"stars":27,"repoUrl":28,"updatedAt":461},"braintrust-design-eval-metric-bundle","create multi-objective evaluation metric bundles","Create, edit, audit, or compare a multi-objective eval metric bundle covering product quality, safety, reliability, latency, and cost. Use to choose metrics for an eval, define a goodness bundle, distinguish optimization metrics from non-regression guardrails, expose tradeoffs, audit a KPI or single composite score for Goodhart and metric-gaming risk, or answer \"what should improve and what must not regress.\" Do not use to design trace schemas, build datasets, or implement scoring methods.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[456,457,458,459,460],{"name":377,"slug":378,"type":16},{"name":9,"slug":8,"type":16},{"name":18,"slug":19,"type":16},{"name":14,"slug":15,"type":16},{"name":367,"slug":368,"type":16},"2026-08-20T03:53:16.078609",{"slug":463,"name":463,"fn":464,"description":465,"org":466,"tags":467,"stars":27,"repoUrl":28,"updatedAt":473},"braintrust-design-human-eval-review","design human evaluation and review workflows","Design or audit human evaluation workflows and golden datasets for LLM applications and agents. Use to set up expert review, select review cases, write reviewer instructions, assign raters, capture rationales and confidence, measure inter-rater agreement with kappa or alpha, adjudicate disagreements, and preserve reviewed examples with provenance as a versioned reference set. Do not use to elicit the criteria or rubric in the first place, to validate a scorer once reference labels exist, or to implement the scorer.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[468,469,470,471,472],{"name":377,"slug":378,"type":16},{"name":9,"slug":8,"type":16},{"name":381,"slug":382,"type":16},{"name":18,"slug":19,"type":16},{"name":14,"slug":15,"type":16},"2026-08-20T03:53:36.900129",{"slug":475,"name":475,"fn":476,"description":477,"org":478,"tags":479,"stars":27,"repoUrl":28,"updatedAt":487},"braintrust-discover-agent-failures","identify and classify agent failure modes","Search open-endedly for unanticipated agent failure modes and convert them into a named taxonomy and durable regression items. Use for requests to find out what goes wrong, surface unknown or silent failures, do error analysis over traces, cluster and triage production failures, or build a failure taxonomy — where the goal is discovering modes nobody thought to test rather than measuring a predefined criterion. Produces datasets and taxonomies, not headline scores. Do not use for adversarial attacks against a threat model, or to measure a known criterion.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[480,481,482,483,484],{"name":377,"slug":378,"type":16},{"name":9,"slug":8,"type":16},{"name":332,"slug":333,"type":16},{"name":18,"slug":19,"type":16},{"name":485,"slug":486,"type":16},"Triage","triage","2026-08-20T03:53:00.423941",27,{"items":490,"total":545},[491,499,507,515,523,531,537],{"slug":341,"name":341,"fn":342,"description":343,"org":492,"tags":493,"stars":27,"repoUrl":28,"updatedAt":355},{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[494,495,496,497,498],{"name":347,"slug":348,"type":16},{"name":9,"slug":8,"type":16},{"name":18,"slug":19,"type":16},{"name":14,"slug":15,"type":16},{"name":353,"slug":354,"type":16},{"slug":357,"name":357,"fn":358,"description":359,"org":500,"tags":501,"stars":27,"repoUrl":28,"updatedAt":369},{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[502,503,504,505,506],{"name":347,"slug":348,"type":16},{"name":9,"slug":8,"type":16},{"name":18,"slug":19,"type":16},{"name":14,"slug":15,"type":16},{"name":367,"slug":368,"type":16},{"slug":371,"name":371,"fn":372,"description":373,"org":508,"tags":509,"stars":27,"repoUrl":28,"updatedAt":385},{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[510,511,512,513,514],{"name":377,"slug":378,"type":16},{"name":9,"slug":8,"type":16},{"name":381,"slug":382,"type":16},{"name":18,"slug":19,"type":16},{"name":14,"slug":15,"type":16},{"slug":4,"name":4,"fn":5,"description":6,"org":516,"tags":517,"stars":27,"repoUrl":28,"updatedAt":29},{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[518,519,520,521,522],{"name":9,"slug":8,"type":16},{"name":18,"slug":19,"type":16},{"name":14,"slug":15,"type":16},{"name":21,"slug":22,"type":16},{"name":24,"slug":25,"type":16},{"slug":395,"name":395,"fn":396,"description":397,"org":524,"tags":525,"stars":27,"repoUrl":28,"updatedAt":407},{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[526,527,528,529,530],{"name":377,"slug":378,"type":16},{"name":9,"slug":8,"type":16},{"name":403,"slug":404,"type":16},{"name":18,"slug":19,"type":16},{"name":14,"slug":15,"type":16},{"slug":409,"name":409,"fn":410,"description":411,"org":532,"tags":533,"stars":27,"repoUrl":28,"updatedAt":419},{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[534,535,536],{"name":9,"slug":8,"type":16},{"name":416,"slug":417,"type":16},{"name":18,"slug":19,"type":16},{"slug":421,"name":421,"fn":422,"description":423,"org":538,"tags":539,"stars":27,"repoUrl":28,"updatedAt":433},{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[540,541,542,543,544],{"name":9,"slug":8,"type":16},{"name":18,"slug":19,"type":16},{"name":429,"slug":430,"type":16},{"name":14,"slug":15,"type":16},{"name":24,"slug":25,"type":16},24]