[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill-braintrust-braintrust-design-eval-metric-bundle":3,"mdc-1mbxbh-key":38,"related-org-braintrust-braintrust-design-eval-metric-bundle":318,"related-repo-braintrust-braintrust-design-eval-metric-bundle":485},{"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-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},"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},"Performance","performance","tag",{"name":18,"slug":19,"type":16},"LLM","llm",{"name":21,"slug":22,"type":16},"Evals","evals",{"name":24,"slug":25,"type":16},"Agents","agents",{"name":9,"slug":8,"type":16},7,"https:\u002F\u002Fgithub.com\u002Fbraintrustdata\u002Feval-library","2026-08-20T03:53:16.078609",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-design-eval-metric-bundle","---\nname: braintrust-design-eval-metric-bundle\ndescription: >-\n  Create, edit, audit, or compare a multi-objective eval metric bundle covering product\n  quality, safety, reliability, latency, and cost. Use to choose metrics for an eval, define a\n  goodness bundle, distinguish optimization metrics from non-regression guardrails, expose\n  tradeoffs, audit a KPI or single composite score for Goodhart and metric-gaming risk, or\n  answer \"what should improve and what must not regress.\" Do not use to design trace schemas,\n  build datasets, or implement scoring methods.\n---\n\n# Design the metric bundle\n\nContract: `references\u002Finteraction-contract.md`. Calibration, templates, provenance: `references\u002Fmetric-bundle.md`.\n\n## Trigger\n\n- Requests to choose metrics, define \"good,\" or set targets.\n- A single headline KPI or composite score driving a ship decision.\n- A targeted change being judged by a whole-response average.\n\n## Do\n\n1. Draft the bundle from the objective before asking anything. Cover quality, safety,\n   reliability, latency, and cost as **separate** measures — never averaged.\n2. Assign each metric a **decision role**: *improve* (the target) or *guardrail* (must not\n   regress). Guardrails are constraints, never terms in a weighted sum.\n3. Give each metric a direction, threshold type (point estimate vs. upper confidence bound —\n   safety rates need the bound), proxy limitation, and named gaming path.\n4. Derive each metric from the **causal path** of the change. If the intervention can only\n   alter part of the output, the primary metric is restricted to the items and fields it can\n   touch, with the aggregate beside it as blast radius.\n5. Make cost the production-honest number: cost per **resolved** request, not token price.\n   Retries, fallbacks, and human cleanup mean the cheapest model is rarely the cheapest system.\n\n## Avoid\n\n- Do not collapse distinct constructs into one score.\n- Do not treat a proxy as the outcome; keep the limitation attached.\n- Do not adopt a metric whose scope cannot show the effect under test — a diluted metric\n  manufactures confident null results.\n- Do not set thresholds from current numbers; derive them from product tolerances and fix them\n  before the run.\n- Do not make metrics observable or implement them.\n\n## Check\n\n- Every metric has direction, decision role, threshold type, proxy limitation, gaming path.\n- All five dimensions represented or explicitly waived with a reason.\n- At least two proxies for any outcome consequential enough to gate on.\n- Targeted changes carry a narrow primary metric plus the aggregate.\n\n## Risk\n\n- Under optimization pressure the system finds the shortest path to the score, not the outcome:\n  unit-test pass rate yields trivially passing suggestions, judge scores yield prompt gaming.\n- A single dominant KPI hides regressions by construction.\n- Thresholds without uncertainty are decorative — a point estimate crossing a line is not\n  evidence the underlying rate did.\n\n## Braintrust\n\n**One scorer per metric** (`references\u002Fplatform-mechanics.md` §5) — the bundle only works if a\nregression in one dimension is visible as its own column. Scores in native `scores` (0–1),\ntokens\u002Flatency\u002Fcost in **native metrics** so cost and quality stay separable. Guardrails become\n**experiment-level regression gates**, not charts someone reads; a guardrail in a chart gets\ntraded away. Upper-bound thresholds (safety) need the bound in a custom column so the gate\nreads the right value. Cost per *resolved* request is derived, not native: a resolution scorer\ngating the non-negotiables, native cost metrics, then the ratio as a custom column. Run the\nsame scorers on production traffic via online scoring — a bundle that exists only offline\ncannot answer the validation question.\n",{"data":39,"body":40},{"name":4,"description":6},{"type":41,"children":42},"root",[43,52,75,82,102,108,181,187,215,221,244,250,268,272],{"type":44,"tag":45,"props":46,"children":48},"element","h1",{"id":47},"design-the-metric-bundle",[49],{"type":50,"value":51},"text","Design the metric bundle",{"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\u002Fmetric-bundle.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},"Requests to choose metrics, define \"good,\" or set targets.",{"type":44,"tag":87,"props":93,"children":94},{},[95],{"type":50,"value":96},"A single headline KPI or composite score driving a ship decision.",{"type":44,"tag":87,"props":98,"children":99},{},[100],{"type":50,"value":101},"A targeted change being judged by a whole-response average.",{"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",{},[112,125,152,157,169],{"type":44,"tag":87,"props":113,"children":114},{},[115,117,123],{"type":50,"value":116},"Draft the bundle from the objective before asking anything. Cover quality, safety,\nreliability, latency, and cost as ",{"type":44,"tag":118,"props":119,"children":120},"strong",{},[121],{"type":50,"value":122},"separate",{"type":50,"value":124}," measures — never averaged.",{"type":44,"tag":87,"props":126,"children":127},{},[128,130,135,137,143,145,150],{"type":50,"value":129},"Assign each metric a ",{"type":44,"tag":118,"props":131,"children":132},{},[133],{"type":50,"value":134},"decision role",{"type":50,"value":136},": ",{"type":44,"tag":138,"props":139,"children":140},"em",{},[141],{"type":50,"value":142},"improve",{"type":50,"value":144}," (the target) or ",{"type":44,"tag":138,"props":146,"children":147},{},[148],{"type":50,"value":149},"guardrail",{"type":50,"value":151}," (must not\nregress). Guardrails are constraints, never terms in a weighted sum.",{"type":44,"tag":87,"props":153,"children":154},{},[155],{"type":50,"value":156},"Give each metric a direction, threshold type (point estimate vs. upper confidence bound —\nsafety rates need the bound), proxy limitation, and named gaming path.",{"type":44,"tag":87,"props":158,"children":159},{},[160,162,167],{"type":50,"value":161},"Derive each metric from the ",{"type":44,"tag":118,"props":163,"children":164},{},[165],{"type":50,"value":166},"causal path",{"type":50,"value":168}," of the change. If the intervention can only\nalter part of the output, the primary metric is restricted to the items and fields it can\ntouch, with the aggregate beside it as blast radius.",{"type":44,"tag":87,"props":170,"children":171},{},[172,174,179],{"type":50,"value":173},"Make cost the production-honest number: cost per ",{"type":44,"tag":118,"props":175,"children":176},{},[177],{"type":50,"value":178},"resolved",{"type":50,"value":180}," request, not token price.\nRetries, fallbacks, and human cleanup mean the cheapest model is rarely the cheapest system.",{"type":44,"tag":76,"props":182,"children":184},{"id":183},"avoid",[185],{"type":50,"value":186},"Avoid",{"type":44,"tag":83,"props":188,"children":189},{},[190,195,200,205,210],{"type":44,"tag":87,"props":191,"children":192},{},[193],{"type":50,"value":194},"Do not collapse distinct constructs into one score.",{"type":44,"tag":87,"props":196,"children":197},{},[198],{"type":50,"value":199},"Do not treat a proxy as the outcome; keep the limitation attached.",{"type":44,"tag":87,"props":201,"children":202},{},[203],{"type":50,"value":204},"Do not adopt a metric whose scope cannot show the effect under test — a diluted metric\nmanufactures confident null results.",{"type":44,"tag":87,"props":206,"children":207},{},[208],{"type":50,"value":209},"Do not set thresholds from current numbers; derive them from product tolerances and fix them\nbefore the run.",{"type":44,"tag":87,"props":211,"children":212},{},[213],{"type":50,"value":214},"Do not make metrics observable or implement them.",{"type":44,"tag":76,"props":216,"children":218},{"id":217},"check",[219],{"type":50,"value":220},"Check",{"type":44,"tag":83,"props":222,"children":223},{},[224,229,234,239],{"type":44,"tag":87,"props":225,"children":226},{},[227],{"type":50,"value":228},"Every metric has direction, decision role, threshold type, proxy limitation, gaming path.",{"type":44,"tag":87,"props":230,"children":231},{},[232],{"type":50,"value":233},"All five dimensions represented or explicitly waived with a reason.",{"type":44,"tag":87,"props":235,"children":236},{},[237],{"type":50,"value":238},"At least two proxies for any outcome consequential enough to gate on.",{"type":44,"tag":87,"props":240,"children":241},{},[242],{"type":50,"value":243},"Targeted changes carry a narrow primary metric plus the aggregate.",{"type":44,"tag":76,"props":245,"children":247},{"id":246},"risk",[248],{"type":50,"value":249},"Risk",{"type":44,"tag":83,"props":251,"children":252},{},[253,258,263],{"type":44,"tag":87,"props":254,"children":255},{},[256],{"type":50,"value":257},"Under optimization pressure the system finds the shortest path to the score, not the outcome:\nunit-test pass rate yields trivially passing suggestions, judge scores yield prompt gaming.",{"type":44,"tag":87,"props":259,"children":260},{},[261],{"type":50,"value":262},"A single dominant KPI hides regressions by construction.",{"type":44,"tag":87,"props":264,"children":265},{},[266],{"type":50,"value":267},"Thresholds without uncertainty are decorative — a point estimate crossing a line is not\nevidence the underlying rate did.",{"type":44,"tag":76,"props":269,"children":270},{"id":8},[271],{"type":50,"value":9},{"type":44,"tag":53,"props":273,"children":274},{},[275,280,282,288,290,296,298,303,305,310,312,316],{"type":44,"tag":118,"props":276,"children":277},{},[278],{"type":50,"value":279},"One scorer per metric",{"type":50,"value":281}," (",{"type":44,"tag":59,"props":283,"children":285},{"className":284},[],[286],{"type":50,"value":287},"references\u002Fplatform-mechanics.md",{"type":50,"value":289}," §5) — the bundle only works if a\nregression in one dimension is visible as its own column. Scores in native ",{"type":44,"tag":59,"props":291,"children":293},{"className":292},[],[294],{"type":50,"value":295},"scores",{"type":50,"value":297}," (0–1),\ntokens\u002Flatency\u002Fcost in ",{"type":44,"tag":118,"props":299,"children":300},{},[301],{"type":50,"value":302},"native metrics",{"type":50,"value":304}," so cost and quality stay separable. Guardrails become\n",{"type":44,"tag":118,"props":306,"children":307},{},[308],{"type":50,"value":309},"experiment-level regression gates",{"type":50,"value":311},", not charts someone reads; a guardrail in a chart gets\ntraded away. Upper-bound thresholds (safety) need the bound in a custom column so the gate\nreads the right value. Cost per ",{"type":44,"tag":138,"props":313,"children":314},{},[315],{"type":50,"value":178},{"type":50,"value":317}," request is derived, not native: a resolution scorer\ngating the non-negotiables, native cost metrics, then the ratio as a custom column. Run the\nsame scorers on production traffic via online scoring — a bundle that exists only offline\ncannot answer the validation question.",{"items":319,"total":484},[320,336,352,364,378,394,408,420,434,450,458,470],{"slug":321,"name":321,"fn":322,"description":323,"org":324,"tags":325,"stars":333,"repoUrl":334,"updatedAt":335},"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},[326,327,330],{"name":9,"slug":8,"type":16},{"name":328,"slug":329,"type":16},"Debugging","debugging",{"name":331,"slug":332,"type":16},"MCP","mcp",18,"https:\u002F\u002Fgithub.com\u002Fbraintrustdata\u002Fbraintrust-claude-plugin","2026-07-12T08:36:13.889274",{"slug":337,"name":337,"fn":338,"description":339,"org":340,"tags":341,"stars":27,"repoUrl":28,"updatedAt":351},"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},[342,345,346,347,348],{"name":343,"slug":344,"type":16},"Analysis","analysis",{"name":9,"slug":8,"type":16},{"name":21,"slug":22,"type":16},{"name":18,"slug":19,"type":16},{"name":349,"slug":350,"type":16},"Statistics","statistics","2026-08-20T03:53:01.13806",{"slug":353,"name":353,"fn":354,"description":355,"org":356,"tags":357,"stars":27,"repoUrl":28,"updatedAt":363},"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},[358,359,360,361,362],{"name":343,"slug":344,"type":16},{"name":9,"slug":8,"type":16},{"name":21,"slug":22,"type":16},{"name":18,"slug":19,"type":16},{"name":14,"slug":15,"type":16},"2026-08-20T03:53:40.036077",{"slug":365,"name":365,"fn":366,"description":367,"org":368,"tags":369,"stars":27,"repoUrl":28,"updatedAt":377},"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},[370,371,372,375,376],{"name":24,"slug":25,"type":16},{"name":9,"slug":8,"type":16},{"name":373,"slug":374,"type":16},"Datasets","datasets",{"name":21,"slug":22,"type":16},{"name":18,"slug":19,"type":16},"2026-08-20T03:53:33.304815",{"slug":379,"name":379,"fn":380,"description":381,"org":382,"tags":383,"stars":27,"repoUrl":28,"updatedAt":393},"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},[384,385,386,387,390],{"name":9,"slug":8,"type":16},{"name":21,"slug":22,"type":16},{"name":18,"slug":19,"type":16},{"name":388,"slug":389,"type":16},"Product Management","product-management",{"name":391,"slug":392,"type":16},"Strategy","strategy","2026-08-20T03:53:00.07097",{"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":24,"slug":25,"type":16},{"name":9,"slug":8,"type":16},{"name":403,"slug":404,"type":16},"CI\u002FCD","ci-cd",{"name":21,"slug":22,"type":16},{"name":18,"slug":19,"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":21,"slug":22,"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":21,"slug":22,"type":16},{"name":429,"slug":430,"type":16},"Experiments","experiments",{"name":18,"slug":19,"type":16},{"name":391,"slug":392,"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":373,"slug":374,"type":16},{"name":21,"slug":22,"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":4,"name":4,"fn":5,"description":6,"org":451,"tags":452,"stars":27,"repoUrl":28,"updatedAt":29},{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[453,454,455,456,457],{"name":24,"slug":25,"type":16},{"name":9,"slug":8,"type":16},{"name":21,"slug":22,"type":16},{"name":18,"slug":19,"type":16},{"name":14,"slug":15,"type":16},{"slug":459,"name":459,"fn":460,"description":461,"org":462,"tags":463,"stars":27,"repoUrl":28,"updatedAt":469},"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},[464,465,466,467,468],{"name":24,"slug":25,"type":16},{"name":9,"slug":8,"type":16},{"name":373,"slug":374,"type":16},{"name":21,"slug":22,"type":16},{"name":18,"slug":19,"type":16},"2026-08-20T03:53:36.900129",{"slug":471,"name":471,"fn":472,"description":473,"org":474,"tags":475,"stars":27,"repoUrl":28,"updatedAt":483},"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},[476,477,478,479,480],{"name":24,"slug":25,"type":16},{"name":9,"slug":8,"type":16},{"name":328,"slug":329,"type":16},{"name":21,"slug":22,"type":16},{"name":481,"slug":482,"type":16},"Triage","triage","2026-08-20T03:53:00.423941",27,{"items":486,"total":541},[487,495,503,511,519,527,533],{"slug":337,"name":337,"fn":338,"description":339,"org":488,"tags":489,"stars":27,"repoUrl":28,"updatedAt":351},{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[490,491,492,493,494],{"name":343,"slug":344,"type":16},{"name":9,"slug":8,"type":16},{"name":21,"slug":22,"type":16},{"name":18,"slug":19,"type":16},{"name":349,"slug":350,"type":16},{"slug":353,"name":353,"fn":354,"description":355,"org":496,"tags":497,"stars":27,"repoUrl":28,"updatedAt":363},{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[498,499,500,501,502],{"name":343,"slug":344,"type":16},{"name":9,"slug":8,"type":16},{"name":21,"slug":22,"type":16},{"name":18,"slug":19,"type":16},{"name":14,"slug":15,"type":16},{"slug":365,"name":365,"fn":366,"description":367,"org":504,"tags":505,"stars":27,"repoUrl":28,"updatedAt":377},{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[506,507,508,509,510],{"name":24,"slug":25,"type":16},{"name":9,"slug":8,"type":16},{"name":373,"slug":374,"type":16},{"name":21,"slug":22,"type":16},{"name":18,"slug":19,"type":16},{"slug":379,"name":379,"fn":380,"description":381,"org":512,"tags":513,"stars":27,"repoUrl":28,"updatedAt":393},{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[514,515,516,517,518],{"name":9,"slug":8,"type":16},{"name":21,"slug":22,"type":16},{"name":18,"slug":19,"type":16},{"name":388,"slug":389,"type":16},{"name":391,"slug":392,"type":16},{"slug":395,"name":395,"fn":396,"description":397,"org":520,"tags":521,"stars":27,"repoUrl":28,"updatedAt":407},{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[522,523,524,525,526],{"name":24,"slug":25,"type":16},{"name":9,"slug":8,"type":16},{"name":403,"slug":404,"type":16},{"name":21,"slug":22,"type":16},{"name":18,"slug":19,"type":16},{"slug":409,"name":409,"fn":410,"description":411,"org":528,"tags":529,"stars":27,"repoUrl":28,"updatedAt":419},{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[530,531,532],{"name":9,"slug":8,"type":16},{"name":416,"slug":417,"type":16},{"name":21,"slug":22,"type":16},{"slug":421,"name":421,"fn":422,"description":423,"org":534,"tags":535,"stars":27,"repoUrl":28,"updatedAt":433},{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[536,537,538,539,540],{"name":9,"slug":8,"type":16},{"name":21,"slug":22,"type":16},{"name":429,"slug":430,"type":16},{"name":18,"slug":19,"type":16},{"name":391,"slug":392,"type":16},24]