[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill-datadog-labs-k9-ownership-byod-setup":3,"mdc-yhjpw8-key":36,"related-org-datadog-labs-k9-ownership-byod-setup":860,"related-repo-datadog-labs-k9-ownership-byod-setup":1040},{"slug":4,"name":4,"fn":5,"description":6,"org":7,"tags":11,"stars":25,"repoUrl":26,"updatedAt":27,"license":28,"forks":29,"topics":30,"repo":31,"sourceUrl":34,"mdContent":35},"k9-ownership-byod-setup","generate BYOD ownership preference tables","Generate a BYOD ownership preferences reference table for a customer. Walks through preference types, generates CSV, and provides upload instructions (UI, API, cloud storage, or Terraform). Use when asked about BYOD setup, preferences reference table, k9_ownership_preferences, or ownership customization.\n",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},"datadog-labs","Datadog Labs","https:\u002F\u002Fpexgzepcugksgbtrxkhf.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Forg-logos\u002Fdatadog-labs.png",[12,16,19,22],{"name":13,"slug":14,"type":15},"Security","security","tag",{"name":17,"slug":18,"type":15},"Operations","operations",{"name":20,"slug":21,"type":15},"Compliance","compliance",{"name":23,"slug":24,"type":15},"Datadog","datadog",145,"https:\u002F\u002Fgithub.com\u002Fdatadog-labs\u002Fagent-skills","2026-04-28T05:42:48.773242",null,19,[],{"repoUrl":26,"stars":25,"forks":29,"topics":32,"description":33},[],"Public repository for Datadog Agent Skills","https:\u002F\u002Fgithub.com\u002Fdatadog-labs\u002Fagent-skills\u002Ftree\u002FHEAD\u002Fdd-security\u002Fcsm\u002Fownership-agent","---\nname: k9-ownership-byod-setup\ndescription: >\n  Generate a BYOD ownership preferences reference table for a customer.\n  Walks through preference types, generates CSV, and provides upload instructions (UI, API, cloud storage, or Terraform).\n  Use when asked about BYOD setup, preferences reference table, k9_ownership_preferences, or ownership customization.\nargument-hint: \"[csv|api|help]\"\nmodel: sonnet\nallowed-tools: Read, Bash\n---\n\n# BYOD Preferences Reference Table Setup\n\nHelp customers create and upload a `k9_ownership_preferences` reference table to customize how the Ownership Agent determines resource owners.\n\n## Read first\n\n- `references\u002Fschema.md` — full schema, column details, validation rules, and per-type examples\n- `assets\u002Fexample.csv` — complete working CSV with all three preference types\n\n## Overview\n\nThe Ownership Agent infers owners for cloud resources with security findings. **Ownership preferences** let customers customize this by providing rules in a Datadog reference table. The agent reads them automatically.\n\nWith preferences you can:\n- **Map tags to owners**: Resources with specific tag values belong to a particular team or person\n- **Exclude accounts**: Prevent bot accounts or shared infrastructure from appearing as owners\n- **Provide custom guidance**: Give the AI engine organization-specific context\n\n## Reference Table Details\n\n- **Table name**: `k9_ownership_preferences` (exact name, must match)\n- **Effect delay**: Changes take effect within 24 hours of upload\n- **Schema**: 12 columns, all STRING — see `references\u002Fschema.md` for details\n\n## Workflow\n\n### Step 1: Determine Needs\n\nAsk the customer:\n- **Tag mappings**: \"Do you have tags on your cloud resources that indicate ownership? (e.g., `cost-center`, `team`, `project`)\"\n- **Exclusions**: \"Are there bot accounts, service accounts, or shared accounts that should never appear as owners?\"\n- **Prompt text**: \"Any organization-specific context that would help determine ownership? (e.g., naming conventions, team structure)\"\n\n### Step 2: Generate CSV\n\nRead `references\u002Fschema.md` for the full column spec and `assets\u002Fexample.csv` for a working template. Build a CSV with all 12 column headers. Each row gets a unique sequential `id` and fills columns relevant to its `preference_type`, leaving the rest empty.\n\n### Step 3: Upload Instructions\n\n**Option A — CSV Upload (UI):**\n1. Go to **Integrations > Reference Tables** in Datadog\n2. Click **New Reference Table**\n3. Upload the CSV\n4. Set table name to `k9_ownership_preferences`\n5. Choose primary key: `preference_type, tag_key, tag_value, handle`\n6. Save\n\nManual uploads support files up to 4 MB.\n\n**Option B — Cloud Storage Sync (S3, Azure Blob, GCS):**\nBest for automated, recurring updates. Store your CSV in a cloud storage bucket and Datadog periodically imports it.\n1. Upload CSV to S3 \u002F Azure Blob \u002F GCS\n2. In Datadog, go to **Integrations > Reference Tables**\n3. Click **New Reference Table**, select **Cloud Storage** as source\n4. Provide storage path and credentials\n5. Set table name to `k9_ownership_preferences`\n6. Datadog re-imports the file periodically\n\nCloud storage uploads support files up to 200 MB.\n\n**Option C — Terraform:**\nUse the `datadog_reference_table` resource in the Datadog Terraform provider to manage the table as infrastructure-as-code.\n\n**Option D — API:**\nYou can manage reference tables programmatically through the Reference Tables API. See the [API documentation](https:\u002F\u002Fdocs.datadoghq.com\u002Fapi\u002Flatest\u002Freference-tables\u002F) for available endpoints. Replace the API domain with your Datadog site URL if applicable.\n\n### Step 4: Verify\n\nChanges take effect within 24 hours. To verify:\n1. Identify a resource that matches one of your tag mappings\n2. After 24 hours, check the ownership suggestion for that resource in the Datadog UI\n3. The suggested owner should reflect your configured mapping\n\n## Key Behaviors\n\n- **Case-insensitive matching**: Tag keys, tag values, handles, exclusion types, and resource types are all matched case-insensitively\n- **AND-logic exclusions**: All non-empty exclusion fields must match. Empty fields act as wildcards\n- **Tag mappings complement, not override**: Direct ownership indicators (like `team:` or `service:` tags) take precedence. Tag mappings augment, not replace\n- **Graceful degradation**: If the table doesn't exist or is empty, ownership detection works normally without preferences\n- **Empty table clears preferences**: Deleting all rows or deleting the table causes cached preferences to expire and be left empty\n- **All-or-nothing validation**: Any validation failure rejects the entire preference set for that cycle\n\n## Troubleshooting\n\n| Problem | Likely cause | Fix |\n|---|---|---|\n| Preferences not taking effect after 24h | Table name is wrong | Must be exactly `k9_ownership_preferences` |\n| Preferences not taking effect after 24h | Missing column headers | All 12 columns must exist as CSV headers |\n| Preferences not taking effect after 24h | Feature not enabled for org | Contact support to enable ownership preferences |\n| All preferences rejected | Invalid characters | See `references\u002Fschema.md` Allowed Characters. No angle brackets, curly braces, or pipes |\n| All preferences rejected | Missing required field | Check required fields for each preference type in `references\u002Fschema.md` |\n| All preferences rejected | Duplicate or conflicting rows | See Duplicate Detection in `references\u002Fschema.md` |\n| All preferences rejected | Size limit exceeded | 50 tag mappings, 20 exclusions, 3 prompt texts. 1024 bytes\u002Ffield, 4096\u002Fprompt |\n| Tag mapping not matching | Spelling mismatch | Matching is case-insensitive but verify exact tag key\u002Fvalue on resource |\n| Exclusion not applying | Scoping too narrow | All non-empty fields must match (AND). Leave filters empty for broad exclusions |\n| Preferences cleared unexpectedly | Table emptied or deleted | Both cause cached preferences to expire. 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No angle brackets, curly braces, or pipes",{"type":45,"tag":639,"props":746,"children":747},{},[748,752,757],{"type":45,"tag":666,"props":749,"children":750},{},[751],{"type":51,"value":727},{"type":45,"tag":666,"props":753,"children":754},{},[755],{"type":51,"value":756},"Missing required field",{"type":45,"tag":666,"props":758,"children":759},{},[760,762],{"type":51,"value":761},"Check required fields for each preference type in ",{"type":45,"tag":60,"props":763,"children":765},{"className":764},[],[766],{"type":51,"value":88},{"type":45,"tag":639,"props":768,"children":769},{},[770,774,779],{"type":45,"tag":666,"props":771,"children":772},{},[773],{"type":51,"value":727},{"type":45,"tag":666,"props":775,"children":776},{},[777],{"type":51,"value":778},"Duplicate or conflicting rows",{"type":45,"tag":666,"props":780,"children":781},{},[782,784],{"type":51,"value":783},"See Duplicate Detection in ",{"type":45,"tag":60,"props":785,"children":787},{"className":786},[],[788],{"type":51,"value":88},{"type":45,"tag":639,"props":790,"children":791},{},[792,796,801],{"type":45,"tag":666,"props":793,"children":794},{},[795],{"type":51,"value":727},{"type":45,"tag":666,"props":797,"children":798},{},[799],{"type":51,"value":800},"Size limit exceeded",{"type":45,"tag":666,"props":802,"children":803},{},[804],{"type":51,"value":805},"50 tag mappings, 20 exclusions, 3 prompt texts. 1024 bytes\u002Ffield, 4096\u002Fprompt",{"type":45,"tag":639,"props":807,"children":808},{},[809,814,819],{"type":45,"tag":666,"props":810,"children":811},{},[812],{"type":51,"value":813},"Tag mapping not matching",{"type":45,"tag":666,"props":815,"children":816},{},[817],{"type":51,"value":818},"Spelling mismatch",{"type":45,"tag":666,"props":820,"children":821},{},[822],{"type":51,"value":823},"Matching is case-insensitive but verify exact tag key\u002Fvalue on resource",{"type":45,"tag":639,"props":825,"children":826},{},[827,832,837],{"type":45,"tag":666,"props":828,"children":829},{},[830],{"type":51,"value":831},"Exclusion not applying",{"type":45,"tag":666,"props":833,"children":834},{},[835],{"type":51,"value":836},"Scoping too narrow",{"type":45,"tag":666,"props":838,"children":839},{},[840],{"type":51,"value":841},"All non-empty fields must match (AND). Leave filters empty for broad exclusions",{"type":45,"tag":639,"props":843,"children":844},{},[845,850,855],{"type":45,"tag":666,"props":846,"children":847},{},[848],{"type":51,"value":849},"Preferences cleared unexpectedly",{"type":45,"tag":666,"props":851,"children":852},{},[853],{"type":51,"value":854},"Table emptied or deleted",{"type":45,"tag":666,"props":856,"children":857},{},[858],{"type":51,"value":859},"Both cause cached preferences to expire. Upload a valid CSV to restore",{"items":861,"total":1039},[862,879,897,908,924,938,954,968,980,992,1004,1024],{"slug":863,"name":863,"fn":864,"description":865,"org":866,"tags":867,"stars":25,"repoUrl":26,"updatedAt":878},"agent-install","install Datadog Agent on Kubernetes","Install the Datadog Agent on Kubernetes using the Datadog Operator — required before enabling Single Step Instrumentation (SSI), which automatically instruments applications for APM without code changes. Only use if no Datadog Agent is deployed on the cluster yet.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[868,869,872,875],{"name":23,"slug":24,"type":15},{"name":870,"slug":871,"type":15},"Deployment","deployment",{"name":873,"slug":874,"type":15},"Kubernetes","kubernetes",{"name":876,"slug":877,"type":15},"Observability","observability","2026-04-15T04:57:27.489805",{"slug":880,"name":880,"fn":881,"description":882,"org":883,"tags":884,"stars":25,"repoUrl":26,"updatedAt":896},"agent-observability-auto-experiment","run iterative code improvements using Datadog data","Run an iterative code-improvement hill-climb against real Datadog LLM-Obs data, locally, with Claude Code as the agent. Establishes a baseline eval, makes one focused change, re-scores with the same harness, keeps the change if it improves the score in the goal's direction (labeling within-noise gains tentative), and repeats. Use when the user says \"run an auto experiment\", \"hill-climb this code\", \"iteratively improve X and measure the delta\", \"optimize this prompt\u002Ffile against my traces\", \"auto-optimize against LLM-Obs\", or wants the local equivalent of the auto_experiments worker. Works from a local dataset file, an ml_app, a dataset_id, or a list of trace_ids.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[885,886,889,892,895],{"name":23,"slug":24,"type":15},{"name":887,"slug":888,"type":15},"Debugging","debugging",{"name":890,"slug":891,"type":15},"Evals","evals",{"name":893,"slug":894,"type":15},"LLM","llm",{"name":876,"slug":877,"type":15},"2026-07-31T05:52:13.711906",{"slug":898,"name":898,"fn":899,"description":900,"org":901,"tags":902,"stars":25,"repoUrl":26,"updatedAt":907},"agent-observability-eval-bootstrap","bootstrap evaluators from production traces","Bootstrap evaluators from production traces — by default propose online LLM-judge evaluators and, after you confirm, create them in Datadog as disabled drafts (never auto-enabled); on request emit Python SDK code or a framework-agnostic JSON spec instead. Use when user says \"bootstrap evaluators\", \"generate evaluators\", \"create evals from traces\", \"eval bootstrap\", \"write evaluators\", \"build eval suite\", \"publish evaluators\", or wants to generate BaseEvaluator\u002FLLMJudge code or online judge configs from production LLM trace data. Works with ml_app and optional RCA report or failure hypothesis.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[903,904,905,906],{"name":23,"slug":24,"type":15},{"name":890,"slug":891,"type":15},{"name":893,"slug":894,"type":15},{"name":876,"slug":877,"type":15},"2026-06-19T09:04:19.259734",{"slug":909,"name":909,"fn":910,"description":911,"org":912,"tags":913,"stars":25,"repoUrl":26,"updatedAt":923},"agent-observability-eval-pipeline","run agent observability and evaluation pipelines","End-to-end Agent Observability pipeline for an instrumented ml_app — classify production traces, root-cause failures, bootstrap evaluators, then (optionally) sample + publish a dataset, generate + run an experiment, and analyze results. Six narrated phases with a standardized banner and a \"continue\" checkpoint between each. Pure orchestration over the agent-observability sub-skills (`agent-observability-session-classify`, `agent-observability-trace-rca`, `agent-observability-eval-bootstrap`, `agent-observability-experiment-py-bootstrap`, `agent-observability-experiment-analyzer`). Use when user says \"run the eval pipeline\", \"go from traces to evals\", \"bootstrap evals end to end\", \"classify then RCA then bootstrap\", \"build an eval set from scratch\", \"onboard me to datasets and experiments\", \"walk me through experiments\", \"I have an ml_app, now what\", \"Agent Observability onboarding\", \"guided experiment setup\", \"from traces to experiments\", or wants a deterministic, narrated tour from production data through evaluators, datasets, and experiments. Stop early with `--stop-after \u003Cphase>` to short-circuit at evaluators or dataset, or resume mid-flow with `--start-at \u003Cphase>`.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[914,917,920,921,922],{"name":915,"slug":916,"type":15},"Agents","agents",{"name":918,"slug":919,"type":15},"Data Pipeline","data-pipeline",{"name":23,"slug":24,"type":15},{"name":890,"slug":891,"type":15},{"name":876,"slug":877,"type":15},"2026-06-19T09:04:17.231423",{"slug":925,"name":925,"fn":926,"description":927,"org":928,"tags":929,"stars":25,"repoUrl":26,"updatedAt":937},"agent-observability-experiment-analyzer","analyze LLM experiment results","Analyze LLM experiment results. Handles single or comparative experiments, exploratory or Q&A modes. Use when user says \"analyze experiment\", \"compare experiments\", \"analyze against baseline\", or provides one or two experiment IDs for analysis.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[930,933,934,935,936],{"name":931,"slug":932,"type":15},"Analytics","analytics",{"name":23,"slug":24,"type":15},{"name":890,"slug":891,"type":15},{"name":893,"slug":894,"type":15},{"name":876,"slug":877,"type":15},"2026-06-19T09:04:21.212498",{"slug":939,"name":939,"fn":940,"description":941,"org":942,"tags":943,"stars":25,"repoUrl":26,"updatedAt":953},"agent-observability-experiment-py-bootstrap","generate Python experiment clients for LLM observability","Generates a self-contained Python experiment client that uses the ddtrace.llmobs SDK. Emits either a runnable .py script or a Jupyter .ipynb notebook matching the canonical DataDog reference notebook style. Use when the user says \"generate Python experiment\", \"write an SDK experiment\", \"create a ddtrace experiment\", \"Python notebook experiment\", \"use the Agent Observability SDK\", or has `ddtrace` installed and wants idiomatic SDK code.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[944,945,948,949,950],{"name":23,"slug":24,"type":15},{"name":946,"slug":947,"type":15},"Jupyter","jupyter",{"name":893,"slug":894,"type":15},{"name":876,"slug":877,"type":15},{"name":951,"slug":952,"type":15},"Python","python","2026-06-19T09:04:22.640384",{"slug":955,"name":955,"fn":956,"description":957,"org":958,"tags":959,"stars":25,"repoUrl":26,"updatedAt":967},"agent-observability-replay-trace","iterate on LLM traces with local code","Use when a developer wants to iterate on ONE specific Agent Observability \u002F LLM Obs trace whose output they didn't like — re-running that trace against their LOCAL code, seeing a concise diff of the old vs new output, and looping (change code → replay → diff) until satisfied. Invoked as \u002Fagent-observability-replay-trace \u003Ctrace-id> [changes to test]. Signals: \"replay this trace\"; \"iterate on a trace\"; \"this trace's output is wrong, fix it and re-run\"; \"re-run trace \u003Cid> with \u003Cchange>\"; pasting a trace id from the Agent Observability UI with a description of what to fix. It fetches the trace via the datadog-llmo MCP or the pup CLI, edits code, re-runs the app to emit a NEW trace, and diffs the two — no local server, no browser. For agents traced with ddtrace \u002F LLM Obs (Python first-class), with JSON-serializable entry input. Do NOT use for: scored Experiments or the browser \"Replay\" button (that's agent-observability-replay-experiment), building an experiment from a dataset\u002FCSV, writing evaluators, root-causing failed traces, or RUM\u002FHTTP session replay.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[960,961,962,963,964],{"name":23,"slug":24,"type":15},{"name":887,"slug":888,"type":15},{"name":893,"slug":894,"type":15},{"name":876,"slug":877,"type":15},{"name":965,"slug":966,"type":15},"Tracing","tracing","2026-07-31T05:52:08.594182",{"slug":969,"name":969,"fn":970,"description":971,"org":972,"tags":973,"stars":25,"repoUrl":26,"updatedAt":979},"agent-observability-session-classify","evaluate user intent satisfaction in sessions","Classify whether user intent was satisfied in a Datadog Agent Observability trace or session. Three modes: (1) session_id — classify a single CMD+I assistant session with RUM; (2) trace_id — classify a single Agent Observability trace without RUM; (3) ml_app — sample and classify multiple sessions or traces from a given LLM app. Output is compact by default (verdict + one-sentence reason). Use when evaluating satisfaction, classifying sessions\u002Ftraces, labeling data, or generating signal for agent-observability-eval-pipeline or agent-observability-trace-rca.\n",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[974,975,976,977,978],{"name":931,"slug":932,"type":15},{"name":23,"slug":24,"type":15},{"name":890,"slug":891,"type":15},{"name":893,"slug":894,"type":15},{"name":876,"slug":877,"type":15},"2026-06-19T09:04:26.341497",{"slug":981,"name":981,"fn":982,"description":983,"org":984,"tags":985,"stars":25,"repoUrl":26,"updatedAt":991},"agent-observability-trace-rca","diagnose LLM application failures from traces","Root cause analysis on production LLM traces. Diagnoses why an LLM application is failing — works from eval judge verdicts, runtime errors, or structural anomalies depending on what signals are present. Walks the span tree from symptom to root cause. Use when user says \"what's wrong with my app\", \"why is my eval failing\", \"analyze errors\", \"root cause analysis\", \"diagnose failures\", or wants to understand production failure patterns.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[986,987,988,989,990],{"name":23,"slug":24,"type":15},{"name":887,"slug":888,"type":15},{"name":890,"slug":891,"type":15},{"name":893,"slug":894,"type":15},{"name":876,"slug":877,"type":15},"2026-06-19T09:04:24.316244",{"slug":993,"name":993,"fn":994,"description":995,"org":996,"tags":997,"stars":25,"repoUrl":26,"updatedAt":1003},"agent-skills","use Datadog observability skills","Datadog skills for AI agents. Essential monitoring, logging, tracing and observability.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[998,999,1002],{"name":23,"slug":24,"type":15},{"name":1000,"slug":1001,"type":15},"Monitoring","monitoring",{"name":876,"slug":877,"type":15},"2026-04-06T18:08:33.337476",{"slug":1005,"name":1005,"fn":1006,"description":1007,"org":1008,"tags":1009,"stars":25,"repoUrl":26,"updatedAt":1023},"datadog-app","build and manage Datadog applications","Guides developers building Datadog Apps with TypeScript, React, the @datadog\u002Fapps scaffolder, and @datadog\u002Fvite-plugin. Use when a user wants to scaffold, run, debug, upgrade, build, upload, publish, upload without publishing (draft upload), add an upload-no-publish script, set up CI\u002FCD, use OAuth or API\u002Fapplication key auth, trigger\u002Fpoll Workflow Automation, choose DDSQL or Action Catalog for backend data access, or query app datastores with DDSQL, including backend function troubleshooting.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1010,1011,1014,1017,1020],{"name":23,"slug":24,"type":15},{"name":1012,"slug":1013,"type":15},"Frontend","frontend",{"name":1015,"slug":1016,"type":15},"React","react",{"name":1018,"slug":1019,"type":15},"TypeScript","typescript",{"name":1021,"slug":1022,"type":15},"Vite","vite","2026-06-18T08:01:32.562331",{"slug":1025,"name":1025,"fn":1026,"description":1027,"org":1028,"tags":1029,"stars":25,"repoUrl":26,"updatedAt":1038},"dd-apm","query Datadog APM traces","APM - install, onboard, instrument, enable, set up, configure, traces, services, dependencies, performance analysis. Use for any request involving Datadog APM setup, instrumentation (SSI, ddtrace, agent install), or analysis.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1030,1031,1034,1035],{"name":23,"slug":24,"type":15},{"name":1032,"slug":1033,"type":15},"Distributed Tracing","distributed-tracing",{"name":876,"slug":877,"type":15},{"name":1036,"slug":1037,"type":15},"Performance","performance","2026-04-06T18:08:34.575282",35,{"items":1041,"total":1096},[1042,1049,1057,1064,1072,1080,1088],{"slug":863,"name":863,"fn":864,"description":865,"org":1043,"tags":1044,"stars":25,"repoUrl":26,"updatedAt":878},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1045,1046,1047,1048],{"name":23,"slug":24,"type":15},{"name":870,"slug":871,"type":15},{"name":873,"slug":874,"type":15},{"name":876,"slug":877,"type":15},{"slug":880,"name":880,"fn":881,"description":882,"org":1050,"tags":1051,"stars":25,"repoUrl":26,"updatedAt":896},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1052,1053,1054,1055,1056],{"name":23,"slug":24,"type":15},{"name":887,"slug":888,"type":15},{"name":890,"slug":891,"type":15},{"name":893,"slug":894,"type":15},{"name":876,"slug":877,"type":15},{"slug":898,"name":898,"fn":899,"description":900,"org":1058,"tags":1059,"stars":25,"repoUrl":26,"updatedAt":907},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1060,1061,1062,1063],{"name":23,"slug":24,"type":15},{"name":890,"slug":891,"type":15},{"name":893,"slug":894,"type":15},{"name":876,"slug":877,"type":15},{"slug":909,"name":909,"fn":910,"description":911,"org":1065,"tags":1066,"stars":25,"repoUrl":26,"updatedAt":923},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1067,1068,1069,1070,1071],{"name":915,"slug":916,"type":15},{"name":918,"slug":919,"type":15},{"name":23,"slug":24,"type":15},{"name":890,"slug":891,"type":15},{"name":876,"slug":877,"type":15},{"slug":925,"name":925,"fn":926,"description":927,"org":1073,"tags":1074,"stars":25,"repoUrl":26,"updatedAt":937},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1075,1076,1077,1078,1079],{"name":931,"slug":932,"type":15},{"name":23,"slug":24,"type":15},{"name":890,"slug":891,"type":15},{"name":893,"slug":894,"type":15},{"name":876,"slug":877,"type":15},{"slug":939,"name":939,"fn":940,"description":941,"org":1081,"tags":1082,"stars":25,"repoUrl":26,"updatedAt":953},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1083,1084,1085,1086,1087],{"name":23,"slug":24,"type":15},{"name":946,"slug":947,"type":15},{"name":893,"slug":894,"type":15},{"name":876,"slug":877,"type":15},{"name":951,"slug":952,"type":15},{"slug":955,"name":955,"fn":956,"description":957,"org":1089,"tags":1090,"stars":25,"repoUrl":26,"updatedAt":967},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1091,1092,1093,1094,1095],{"name":23,"slug":24,"type":15},{"name":887,"slug":888,"type":15},{"name":893,"slug":894,"type":15},{"name":876,"slug":877,"type":15},{"name":965,"slug":966,"type":15},34]