[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill-grafana-cost-management":3,"mdc-50f3cq-key":34,"related-repo-grafana-cost-management":836,"related-org-grafana-cost-management":952},{"slug":4,"name":4,"fn":5,"description":6,"org":7,"tags":11,"stars":23,"repoUrl":24,"updatedAt":25,"license":26,"forks":27,"topics":28,"repo":29,"sourceUrl":32,"mdContent":33},"cost-management","optimize Grafana Cloud billing and spend","Cut your Grafana Cloud bill by attributing spend to teams and reducing telemetry volume. Covers FOCUS-compliant billing dashboards, cost-attribution labels in Alloy, Adaptive Metrics (cardinality reduction), Adaptive Logs (drop\u002Fsample), Adaptive Traces (tail sampling), usage alerts, and an optimization checklist. Use when investigating a high Grafana Cloud bill, attributing observability cost to a team or service, reducing active series \u002F log bytes \u002F trace spans, or setting up usage \u002F quota alerts — even when the user says \"our Grafana bill is too high\", \"who's burning the most metrics\", \"drop debug logs\", \"sample our traces\", or \"alert me before we hit quota\" without naming Cost Management.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},"grafana","Grafana","https:\u002F\u002Fpexgzepcugksgbtrxkhf.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Forg-logos\u002Fgrafana.jpg",[12,16,19,22],{"name":13,"slug":14,"type":15},"Performance","performance","tag",{"name":17,"slug":18,"type":15},"Reporting","reporting",{"name":20,"slug":21,"type":15},"Cost Optimization","cost-optimization",{"name":9,"slug":8,"type":15},189,"https:\u002F\u002Fgithub.com\u002Fgrafana\u002Fskills","2026-07-12T07:44:30.569533","Apache-2.0",16,[],{"repoUrl":24,"stars":23,"forks":27,"topics":30,"description":31},[],null,"https:\u002F\u002Fgithub.com\u002Fgrafana\u002Fskills\u002Ftree\u002FHEAD\u002Fskills\u002Fgrafana-cloud\u002Fcost-management","---\nname: cost-management\nlicense: Apache-2.0\ndescription: Cut your Grafana Cloud bill by attributing spend to teams and reducing telemetry volume. Covers FOCUS-compliant billing dashboards, cost-attribution labels in Alloy, Adaptive Metrics (cardinality reduction), Adaptive Logs (drop\u002Fsample), Adaptive Traces (tail sampling), usage alerts, and an optimization checklist. Use when investigating a high Grafana Cloud bill, attributing observability cost to a team or service, reducing active series \u002F log bytes \u002F trace spans, or setting up usage \u002F quota alerts — even when the user says \"our Grafana bill is too high\", \"who's burning the most metrics\", \"drop debug logs\", \"sample our traces\", or \"alert me before we hit quota\" without naming Cost Management.\n---\n\n# Grafana Cloud Cost Management\n\n> **Docs**: https:\u002F\u002Fgrafana.com\u002Fdocs\u002Fgrafana-cloud\u002Fcost-management-and-billing\u002F\n\nReduce metric \u002F log \u002F trace spend with Adaptive signals + cost-attribution labels.\n\n## Prerequisites\n\n- A Grafana Cloud stack with Adaptive Metrics \u002F Logs \u002F Traces enabled (visible under **Cost Management**)\n- Alloy (or Grafana Agent) ingesting telemetry, with API key in scope `metrics:write` + `logs:write` (+ `traces:write`)\n- Admin access to the stack to apply Adaptive recommendations\n\n## Common Workflows\n\n### 1. Attribute cost to a team \u002F service\n\n```alloy\n# 1. Add external labels in Alloy (metrics + logs configs)\nprometheus.remote_write \"cloud\" {\n  endpoint { url = sys.env(\"PROMETHEUS_URL\") \u002F* ... *\u002F }\n  external_labels = { team = \"platform\", project = \"checkout-service\" }\n}\n```\n\n```bash\n# 2. Reload Alloy\ncurl -X POST http:\u002F\u002Flocalhost:12345\u002F-\u002Freload\n\n# 3. Verify labels arrived in Grafana Cloud\n#    In Explore, run:  count by (team, project) ({__name__=~\".+\"})\n#    Then visit Cost Management → group by `team` \u002F `project`\n```\n\nSee [`references\u002Fadaptive-signals.md`](references\u002Fadaptive-signals.md) for the full Alloy snippet.\n\n### 2. Cut metric cardinality with Adaptive Metrics\n\n```bash\n# 1. Pull recommendations\ncurl https:\u002F\u002F\u003Cstack>.grafana.net\u002Fapi\u002Fplugins\u002Fgrafana-adaptive-metrics-app\u002Fresources\u002Fv1\u002Frecommendations \\\n  -H \"Authorization: Bearer \u003Ctoken>\" | jq '.recommendations | length'\n\n# 2. In the UI: Grafana Cloud → Adaptive Metrics → review rules sorted by series-reduction impact\n# 3. Test in \"Preview\" mode before applying\n# 4. Apply (takes effect within 5 min)\n\n# 5. Verify — series count should drop on the affected metrics\n#    Before applying, capture baseline:\n#      count({__name__=\"http_request_duration_seconds_bucket\"})\n#    Wait 10 min after apply, run again — expect 10x+ reduction for high-card metrics.\n\n# Rollback if needed: open the rule in the UI → Disable, or DELETE \u002Fv1\u002Frules\u002F\u003Cid>.\n```\n\n### 3. Drop noisy logs in Alloy\n\n```alloy\n# 1. Add a filter stage (see references\u002Fadaptive-signals.md for the full block)\nloki.process \"filter_logs\" {\n  forward_to = [loki.write.cloud.receiver]\n  stage.drop { expression = \".*GET \u002Fhealth.*\" }\n}\n```\n\n```bash\n# 2. Reload Alloy\ncurl -X POST http:\u002F\u002Flocalhost:12345\u002F-\u002Freload\n\n# 3. Verify the filter — health logs should NOT appear in Logs Drilldown\n#    LogQL check (should return 0):\n#      sum(rate({app=\"my-app\"} |= \"GET \u002Fhealth\" [5m]))\n#    Bytes-ingested should also drop. Compare 24h before\u002Fafter:\n#      sum(increase(loki_ingester_chunk_size_bytes_sum[24h])) by (namespace)\n```\n\n### 4. Set a usage alert before you hit quota\n\nSee [`references\u002Falerts-and-queries.md`](references\u002Falerts-and-queries.md) for ready-to-paste rules (`MetricsUsageHigh`, `LogsIngestionHigh`).\n\n## Optimization checklist\n\n- [ ] Apply Adaptive Metrics recommendations — typically reduces series 40-60%\n- [ ] Drop health\u002Freadiness probe logs in Alloy\n- [ ] Tail-sample traces to 5-10% + keep errors \u002F slow spans\n- [ ] Add `team` + `project` external labels to every Alloy config\n- [ ] Set usage alerts at 80% of quota\n- [ ] Replace expensive ad-hoc queries with recording rules\n\n## References\n\n- [`references\u002Fadaptive-signals.md`](references\u002Fadaptive-signals.md) — Adaptive Metrics \u002F Logs \u002F Traces config; cost-attribution labels\n- [`references\u002Falerts-and-queries.md`](references\u002Falerts-and-queries.md) — usage alert rules, cost-finding PromQL, billing-unit table\n\n## Resources\n\n- [Cost Management docs](https:\u002F\u002Fgrafana.com\u002Fdocs\u002Fgrafana-cloud\u002Fcost-management-and-billing\u002F)\n- [Adaptive Metrics](https:\u002F\u002Fgrafana.com\u002Fdocs\u002Fgrafana-cloud\u002Fcost-management-and-billing\u002Freduce-costs\u002Fmetrics-costs\u002Fadaptive-metrics\u002F)\n- [Adaptive Logs](https:\u002F\u002Fgrafana.com\u002Fdocs\u002Fgrafana-cloud\u002Fcost-management-and-billing\u002Freduce-costs\u002Flogs-costs\u002F)\n",{"data":35,"body":36},{"name":4,"license":26,"description":6},{"type":37,"children":38},"root",[39,48,72,77,84,135,141,148,206,283,299,305,505,511,557,637,643,674,680,757,763,792,798,830],{"type":40,"tag":41,"props":42,"children":44},"element","h1",{"id":43},"grafana-cloud-cost-management",[45],{"type":46,"value":47},"text","Grafana Cloud Cost Management",{"type":40,"tag":49,"props":50,"children":51},"blockquote",{},[52],{"type":40,"tag":53,"props":54,"children":55},"p",{},[56,62,64],{"type":40,"tag":57,"props":58,"children":59},"strong",{},[60],{"type":46,"value":61},"Docs",{"type":46,"value":63},": ",{"type":40,"tag":65,"props":66,"children":70},"a",{"href":67,"rel":68},"https:\u002F\u002Fgrafana.com\u002Fdocs\u002Fgrafana-cloud\u002Fcost-management-and-billing\u002F",[69],"nofollow",[71],{"type":46,"value":67},{"type":40,"tag":53,"props":73,"children":74},{},[75],{"type":46,"value":76},"Reduce metric \u002F log \u002F trace spend with Adaptive signals + cost-attribution labels.",{"type":40,"tag":78,"props":79,"children":81},"h2",{"id":80},"prerequisites",[82],{"type":46,"value":83},"Prerequisites",{"type":40,"tag":85,"props":86,"children":87},"ul",{},[88,101,130],{"type":40,"tag":89,"props":90,"children":91},"li",{},[92,94,99],{"type":46,"value":93},"A Grafana Cloud stack with Adaptive Metrics \u002F Logs \u002F Traces enabled (visible under ",{"type":40,"tag":57,"props":95,"children":96},{},[97],{"type":46,"value":98},"Cost Management",{"type":46,"value":100},")",{"type":40,"tag":89,"props":102,"children":103},{},[104,106,113,115,121,123,129],{"type":46,"value":105},"Alloy (or Grafana Agent) ingesting telemetry, with API key in scope ",{"type":40,"tag":107,"props":108,"children":110},"code",{"className":109},[],[111],{"type":46,"value":112},"metrics:write",{"type":46,"value":114}," + ",{"type":40,"tag":107,"props":116,"children":118},{"className":117},[],[119],{"type":46,"value":120},"logs:write",{"type":46,"value":122}," (+ ",{"type":40,"tag":107,"props":124,"children":126},{"className":125},[],[127],{"type":46,"value":128},"traces:write",{"type":46,"value":100},{"type":40,"tag":89,"props":131,"children":132},{},[133],{"type":46,"value":134},"Admin access to the stack to apply Adaptive recommendations",{"type":40,"tag":78,"props":136,"children":138},{"id":137},"common-workflows",[139],{"type":46,"value":140},"Common Workflows",{"type":40,"tag":142,"props":143,"children":145},"h3",{"id":144},"_1-attribute-cost-to-a-team-service",[146],{"type":46,"value":147},"1. Attribute cost to a team \u002F service",{"type":40,"tag":149,"props":150,"children":155},"pre",{"className":151,"code":152,"language":153,"meta":154,"style":154},"language-alloy shiki shiki-themes material-theme-lighter material-theme material-theme-palenight","# 1. Add external labels in Alloy (metrics + logs configs)\nprometheus.remote_write \"cloud\" {\n  endpoint { url = sys.env(\"PROMETHEUS_URL\") \u002F* ... *\u002F }\n  external_labels = { team = \"platform\", project = \"checkout-service\" }\n}\n","alloy","",[156],{"type":40,"tag":107,"props":157,"children":158},{"__ignoreMap":154},[159,170,179,188,197],{"type":40,"tag":160,"props":161,"children":164},"span",{"class":162,"line":163},"line",1,[165],{"type":40,"tag":160,"props":166,"children":167},{},[168],{"type":46,"value":169},"# 1. Add external labels in Alloy (metrics + logs configs)\n",{"type":40,"tag":160,"props":171,"children":173},{"class":162,"line":172},2,[174],{"type":40,"tag":160,"props":175,"children":176},{},[177],{"type":46,"value":178},"prometheus.remote_write \"cloud\" {\n",{"type":40,"tag":160,"props":180,"children":182},{"class":162,"line":181},3,[183],{"type":40,"tag":160,"props":184,"children":185},{},[186],{"type":46,"value":187},"  endpoint { url = sys.env(\"PROMETHEUS_URL\") \u002F* ... *\u002F }\n",{"type":40,"tag":160,"props":189,"children":191},{"class":162,"line":190},4,[192],{"type":40,"tag":160,"props":193,"children":194},{},[195],{"type":46,"value":196},"  external_labels = { team = \"platform\", project = \"checkout-service\" }\n",{"type":40,"tag":160,"props":198,"children":200},{"class":162,"line":199},5,[201],{"type":40,"tag":160,"props":202,"children":203},{},[204],{"type":46,"value":205},"}\n",{"type":40,"tag":149,"props":207,"children":211},{"className":208,"code":209,"language":210,"meta":154,"style":154},"language-bash shiki shiki-themes material-theme-lighter material-theme material-theme-palenight","# 2. Reload Alloy\ncurl -X POST http:\u002F\u002Flocalhost:12345\u002F-\u002Freload\n\n# 3. Verify labels arrived in Grafana Cloud\n#    In Explore, run:  count by (team, project) ({__name__=~\".+\"})\n#    Then visit Cost Management → group by `team` \u002F `project`\n","bash",[212],{"type":40,"tag":107,"props":213,"children":214},{"__ignoreMap":154},[215,224,249,258,266,274],{"type":40,"tag":160,"props":216,"children":217},{"class":162,"line":163},[218],{"type":40,"tag":160,"props":219,"children":221},{"style":220},"--shiki-light:#90A4AE;--shiki-light-font-style:italic;--shiki-default:#546E7A;--shiki-default-font-style:italic;--shiki-dark:#676E95;--shiki-dark-font-style:italic",[222],{"type":46,"value":223},"# 2. Reload Alloy\n",{"type":40,"tag":160,"props":225,"children":226},{"class":162,"line":172},[227,233,239,244],{"type":40,"tag":160,"props":228,"children":230},{"style":229},"--shiki-light:#E2931D;--shiki-default:#FFCB6B;--shiki-dark:#FFCB6B",[231],{"type":46,"value":232},"curl",{"type":40,"tag":160,"props":234,"children":236},{"style":235},"--shiki-light:#91B859;--shiki-default:#C3E88D;--shiki-dark:#C3E88D",[237],{"type":46,"value":238}," -X",{"type":40,"tag":160,"props":240,"children":241},{"style":235},[242],{"type":46,"value":243}," POST",{"type":40,"tag":160,"props":245,"children":246},{"style":235},[247],{"type":46,"value":248}," http:\u002F\u002Flocalhost:12345\u002F-\u002Freload\n",{"type":40,"tag":160,"props":250,"children":251},{"class":162,"line":181},[252],{"type":40,"tag":160,"props":253,"children":255},{"emptyLinePlaceholder":254},true,[256],{"type":46,"value":257},"\n",{"type":40,"tag":160,"props":259,"children":260},{"class":162,"line":190},[261],{"type":40,"tag":160,"props":262,"children":263},{"style":220},[264],{"type":46,"value":265},"# 3. Verify labels arrived in Grafana Cloud\n",{"type":40,"tag":160,"props":267,"children":268},{"class":162,"line":199},[269],{"type":40,"tag":160,"props":270,"children":271},{"style":220},[272],{"type":46,"value":273},"#    In Explore, run:  count by (team, project) ({__name__=~\".+\"})\n",{"type":40,"tag":160,"props":275,"children":277},{"class":162,"line":276},6,[278],{"type":40,"tag":160,"props":279,"children":280},{"style":220},[281],{"type":46,"value":282},"#    Then visit Cost Management → group by `team` \u002F `project`\n",{"type":40,"tag":53,"props":284,"children":285},{},[286,288,297],{"type":46,"value":287},"See ",{"type":40,"tag":65,"props":289,"children":291},{"href":290},"references\u002Fadaptive-signals.md",[292],{"type":40,"tag":107,"props":293,"children":295},{"className":294},[],[296],{"type":46,"value":290},{"type":46,"value":298}," for the full Alloy snippet.",{"type":40,"tag":142,"props":300,"children":302},{"id":301},"_2-cut-metric-cardinality-with-adaptive-metrics",[303],{"type":46,"value":304},"2. Cut metric cardinality with Adaptive Metrics",{"type":40,"tag":149,"props":306,"children":308},{"className":208,"code":307,"language":210,"meta":154,"style":154},"# 1. Pull recommendations\ncurl https:\u002F\u002F\u003Cstack>.grafana.net\u002Fapi\u002Fplugins\u002Fgrafana-adaptive-metrics-app\u002Fresources\u002Fv1\u002Frecommendations \\\n  -H \"Authorization: Bearer \u003Ctoken>\" | jq '.recommendations | length'\n\n# 2. In the UI: Grafana Cloud → Adaptive Metrics → review rules sorted by series-reduction impact\n# 3. Test in \"Preview\" mode before applying\n# 4. Apply (takes effect within 5 min)\n\n# 5. Verify — series count should drop on the affected metrics\n#    Before applying, capture baseline:\n#      count({__name__=\"http_request_duration_seconds_bucket\"})\n#    Wait 10 min after apply, run again — expect 10x+ reduction for high-card metrics.\n\n# Rollback if needed: open the rule in the UI → Disable, or DELETE \u002Fv1\u002Frules\u002F\u003Cid>.\n",[309],{"type":40,"tag":107,"props":310,"children":311},{"__ignoreMap":154},[312,320,364,412,419,427,435,444,452,461,470,479,488,496],{"type":40,"tag":160,"props":313,"children":314},{"class":162,"line":163},[315],{"type":40,"tag":160,"props":316,"children":317},{"style":220},[318],{"type":46,"value":319},"# 1. Pull recommendations\n",{"type":40,"tag":160,"props":321,"children":322},{"class":162,"line":172},[323,327,332,338,343,349,354,359],{"type":40,"tag":160,"props":324,"children":325},{"style":229},[326],{"type":46,"value":232},{"type":40,"tag":160,"props":328,"children":329},{"style":235},[330],{"type":46,"value":331}," https:\u002F\u002F",{"type":40,"tag":160,"props":333,"children":335},{"style":334},"--shiki-light:#39ADB5;--shiki-default:#89DDFF;--shiki-dark:#89DDFF",[336],{"type":46,"value":337},"\u003C",{"type":40,"tag":160,"props":339,"children":340},{"style":235},[341],{"type":46,"value":342},"stac",{"type":40,"tag":160,"props":344,"children":346},{"style":345},"--shiki-light:#90A4AE;--shiki-default:#EEFFFF;--shiki-dark:#BABED8",[347],{"type":46,"value":348},"k",{"type":40,"tag":160,"props":350,"children":351},{"style":334},[352],{"type":46,"value":353},">",{"type":40,"tag":160,"props":355,"children":356},{"style":235},[357],{"type":46,"value":358},".grafana.net\u002Fapi\u002Fplugins\u002Fgrafana-adaptive-metrics-app\u002Fresources\u002Fv1\u002Frecommendations",{"type":40,"tag":160,"props":360,"children":361},{"style":345},[362],{"type":46,"value":363}," \\\n",{"type":40,"tag":160,"props":365,"children":366},{"class":162,"line":181},[367,372,377,382,387,392,397,402,407],{"type":40,"tag":160,"props":368,"children":369},{"style":235},[370],{"type":46,"value":371},"  -H",{"type":40,"tag":160,"props":373,"children":374},{"style":334},[375],{"type":46,"value":376}," \"",{"type":40,"tag":160,"props":378,"children":379},{"style":235},[380],{"type":46,"value":381},"Authorization: Bearer \u003Ctoken>",{"type":40,"tag":160,"props":383,"children":384},{"style":334},[385],{"type":46,"value":386},"\"",{"type":40,"tag":160,"props":388,"children":389},{"style":334},[390],{"type":46,"value":391}," |",{"type":40,"tag":160,"props":393,"children":394},{"style":229},[395],{"type":46,"value":396}," jq",{"type":40,"tag":160,"props":398,"children":399},{"style":334},[400],{"type":46,"value":401}," '",{"type":40,"tag":160,"props":403,"children":404},{"style":235},[405],{"type":46,"value":406},".recommendations | length",{"type":40,"tag":160,"props":408,"children":409},{"style":334},[410],{"type":46,"value":411},"'\n",{"type":40,"tag":160,"props":413,"children":414},{"class":162,"line":190},[415],{"type":40,"tag":160,"props":416,"children":417},{"emptyLinePlaceholder":254},[418],{"type":46,"value":257},{"type":40,"tag":160,"props":420,"children":421},{"class":162,"line":199},[422],{"type":40,"tag":160,"props":423,"children":424},{"style":220},[425],{"type":46,"value":426},"# 2. In the UI: Grafana Cloud → Adaptive Metrics → review rules sorted by series-reduction impact\n",{"type":40,"tag":160,"props":428,"children":429},{"class":162,"line":276},[430],{"type":40,"tag":160,"props":431,"children":432},{"style":220},[433],{"type":46,"value":434},"# 3. Test in \"Preview\" mode before applying\n",{"type":40,"tag":160,"props":436,"children":438},{"class":162,"line":437},7,[439],{"type":40,"tag":160,"props":440,"children":441},{"style":220},[442],{"type":46,"value":443},"# 4. Apply (takes effect within 5 min)\n",{"type":40,"tag":160,"props":445,"children":447},{"class":162,"line":446},8,[448],{"type":40,"tag":160,"props":449,"children":450},{"emptyLinePlaceholder":254},[451],{"type":46,"value":257},{"type":40,"tag":160,"props":453,"children":455},{"class":162,"line":454},9,[456],{"type":40,"tag":160,"props":457,"children":458},{"style":220},[459],{"type":46,"value":460},"# 5. Verify — series count should drop on the affected metrics\n",{"type":40,"tag":160,"props":462,"children":464},{"class":162,"line":463},10,[465],{"type":40,"tag":160,"props":466,"children":467},{"style":220},[468],{"type":46,"value":469},"#    Before applying, capture baseline:\n",{"type":40,"tag":160,"props":471,"children":473},{"class":162,"line":472},11,[474],{"type":40,"tag":160,"props":475,"children":476},{"style":220},[477],{"type":46,"value":478},"#      count({__name__=\"http_request_duration_seconds_bucket\"})\n",{"type":40,"tag":160,"props":480,"children":482},{"class":162,"line":481},12,[483],{"type":40,"tag":160,"props":484,"children":485},{"style":220},[486],{"type":46,"value":487},"#    Wait 10 min after apply, run again — expect 10x+ reduction for high-card metrics.\n",{"type":40,"tag":160,"props":489,"children":491},{"class":162,"line":490},13,[492],{"type":40,"tag":160,"props":493,"children":494},{"emptyLinePlaceholder":254},[495],{"type":46,"value":257},{"type":40,"tag":160,"props":497,"children":499},{"class":162,"line":498},14,[500],{"type":40,"tag":160,"props":501,"children":502},{"style":220},[503],{"type":46,"value":504},"# Rollback if needed: open the rule in the UI → Disable, or DELETE \u002Fv1\u002Frules\u002F\u003Cid>.\n",{"type":40,"tag":142,"props":506,"children":508},{"id":507},"_3-drop-noisy-logs-in-alloy",[509],{"type":46,"value":510},"3. Drop noisy logs in Alloy",{"type":40,"tag":149,"props":512,"children":514},{"className":151,"code":513,"language":153,"meta":154,"style":154},"# 1. Add a filter stage (see references\u002Fadaptive-signals.md for the full block)\nloki.process \"filter_logs\" {\n  forward_to = [loki.write.cloud.receiver]\n  stage.drop { expression = \".*GET \u002Fhealth.*\" }\n}\n",[515],{"type":40,"tag":107,"props":516,"children":517},{"__ignoreMap":154},[518,526,534,542,550],{"type":40,"tag":160,"props":519,"children":520},{"class":162,"line":163},[521],{"type":40,"tag":160,"props":522,"children":523},{},[524],{"type":46,"value":525},"# 1. Add a filter stage (see references\u002Fadaptive-signals.md for the full block)\n",{"type":40,"tag":160,"props":527,"children":528},{"class":162,"line":172},[529],{"type":40,"tag":160,"props":530,"children":531},{},[532],{"type":46,"value":533},"loki.process \"filter_logs\" {\n",{"type":40,"tag":160,"props":535,"children":536},{"class":162,"line":181},[537],{"type":40,"tag":160,"props":538,"children":539},{},[540],{"type":46,"value":541},"  forward_to = [loki.write.cloud.receiver]\n",{"type":40,"tag":160,"props":543,"children":544},{"class":162,"line":190},[545],{"type":40,"tag":160,"props":546,"children":547},{},[548],{"type":46,"value":549},"  stage.drop { expression = \".*GET \u002Fhealth.*\" }\n",{"type":40,"tag":160,"props":551,"children":552},{"class":162,"line":199},[553],{"type":40,"tag":160,"props":554,"children":555},{},[556],{"type":46,"value":205},{"type":40,"tag":149,"props":558,"children":560},{"className":208,"code":559,"language":210,"meta":154,"style":154},"# 2. Reload Alloy\ncurl -X POST http:\u002F\u002Flocalhost:12345\u002F-\u002Freload\n\n# 3. Verify the filter — health logs should NOT appear in Logs Drilldown\n#    LogQL check (should return 0):\n#      sum(rate({app=\"my-app\"} |= \"GET \u002Fhealth\" [5m]))\n#    Bytes-ingested should also drop. Compare 24h before\u002Fafter:\n#      sum(increase(loki_ingester_chunk_size_bytes_sum[24h])) by (namespace)\n",[561],{"type":40,"tag":107,"props":562,"children":563},{"__ignoreMap":154},[564,571,590,597,605,613,621,629],{"type":40,"tag":160,"props":565,"children":566},{"class":162,"line":163},[567],{"type":40,"tag":160,"props":568,"children":569},{"style":220},[570],{"type":46,"value":223},{"type":40,"tag":160,"props":572,"children":573},{"class":162,"line":172},[574,578,582,586],{"type":40,"tag":160,"props":575,"children":576},{"style":229},[577],{"type":46,"value":232},{"type":40,"tag":160,"props":579,"children":580},{"style":235},[581],{"type":46,"value":238},{"type":40,"tag":160,"props":583,"children":584},{"style":235},[585],{"type":46,"value":243},{"type":40,"tag":160,"props":587,"children":588},{"style":235},[589],{"type":46,"value":248},{"type":40,"tag":160,"props":591,"children":592},{"class":162,"line":181},[593],{"type":40,"tag":160,"props":594,"children":595},{"emptyLinePlaceholder":254},[596],{"type":46,"value":257},{"type":40,"tag":160,"props":598,"children":599},{"class":162,"line":190},[600],{"type":40,"tag":160,"props":601,"children":602},{"style":220},[603],{"type":46,"value":604},"# 3. Verify the filter — health logs should NOT appear in Logs Drilldown\n",{"type":40,"tag":160,"props":606,"children":607},{"class":162,"line":199},[608],{"type":40,"tag":160,"props":609,"children":610},{"style":220},[611],{"type":46,"value":612},"#    LogQL check (should return 0):\n",{"type":40,"tag":160,"props":614,"children":615},{"class":162,"line":276},[616],{"type":40,"tag":160,"props":617,"children":618},{"style":220},[619],{"type":46,"value":620},"#      sum(rate({app=\"my-app\"} |= \"GET \u002Fhealth\" [5m]))\n",{"type":40,"tag":160,"props":622,"children":623},{"class":162,"line":437},[624],{"type":40,"tag":160,"props":625,"children":626},{"style":220},[627],{"type":46,"value":628},"#    Bytes-ingested should also drop. Compare 24h before\u002Fafter:\n",{"type":40,"tag":160,"props":630,"children":631},{"class":162,"line":446},[632],{"type":40,"tag":160,"props":633,"children":634},{"style":220},[635],{"type":46,"value":636},"#      sum(increase(loki_ingester_chunk_size_bytes_sum[24h])) by (namespace)\n",{"type":40,"tag":142,"props":638,"children":640},{"id":639},"_4-set-a-usage-alert-before-you-hit-quota",[641],{"type":46,"value":642},"4. Set a usage alert before you hit quota",{"type":40,"tag":53,"props":644,"children":645},{},[646,647,656,658,664,666,672],{"type":46,"value":287},{"type":40,"tag":65,"props":648,"children":650},{"href":649},"references\u002Falerts-and-queries.md",[651],{"type":40,"tag":107,"props":652,"children":654},{"className":653},[],[655],{"type":46,"value":649},{"type":46,"value":657}," for ready-to-paste rules (",{"type":40,"tag":107,"props":659,"children":661},{"className":660},[],[662],{"type":46,"value":663},"MetricsUsageHigh",{"type":46,"value":665},", ",{"type":40,"tag":107,"props":667,"children":669},{"className":668},[],[670],{"type":46,"value":671},"LogsIngestionHigh",{"type":46,"value":673},").",{"type":40,"tag":78,"props":675,"children":677},{"id":676},"optimization-checklist",[678],{"type":46,"value":679},"Optimization checklist",{"type":40,"tag":85,"props":681,"children":684},{"className":682},[683],"contains-task-list",[685,697,706,715,739,748],{"type":40,"tag":89,"props":686,"children":689},{"className":687},[688],"task-list-item",[690,695],{"type":40,"tag":691,"props":692,"children":694},"input",{"disabled":254,"type":693},"checkbox",[],{"type":46,"value":696}," Apply Adaptive Metrics recommendations — typically reduces series 40-60%",{"type":40,"tag":89,"props":698,"children":700},{"className":699},[688],[701,704],{"type":40,"tag":691,"props":702,"children":703},{"disabled":254,"type":693},[],{"type":46,"value":705}," Drop health\u002Freadiness probe logs in Alloy",{"type":40,"tag":89,"props":707,"children":709},{"className":708},[688],[710,713],{"type":40,"tag":691,"props":711,"children":712},{"disabled":254,"type":693},[],{"type":46,"value":714}," Tail-sample traces to 5-10% + keep errors \u002F slow spans",{"type":40,"tag":89,"props":716,"children":718},{"className":717},[688],[719,722,724,730,731,737],{"type":40,"tag":691,"props":720,"children":721},{"disabled":254,"type":693},[],{"type":46,"value":723}," Add ",{"type":40,"tag":107,"props":725,"children":727},{"className":726},[],[728],{"type":46,"value":729},"team",{"type":46,"value":114},{"type":40,"tag":107,"props":732,"children":734},{"className":733},[],[735],{"type":46,"value":736},"project",{"type":46,"value":738}," external labels to every Alloy config",{"type":40,"tag":89,"props":740,"children":742},{"className":741},[688],[743,746],{"type":40,"tag":691,"props":744,"children":745},{"disabled":254,"type":693},[],{"type":46,"value":747}," Set usage alerts at 80% of quota",{"type":40,"tag":89,"props":749,"children":751},{"className":750},[688],[752,755],{"type":40,"tag":691,"props":753,"children":754},{"disabled":254,"type":693},[],{"type":46,"value":756}," Replace expensive ad-hoc queries with recording rules",{"type":40,"tag":78,"props":758,"children":760},{"id":759},"references",[761],{"type":46,"value":762},"References",{"type":40,"tag":85,"props":764,"children":765},{},[766,779],{"type":40,"tag":89,"props":767,"children":768},{},[769,777],{"type":40,"tag":65,"props":770,"children":771},{"href":290},[772],{"type":40,"tag":107,"props":773,"children":775},{"className":774},[],[776],{"type":46,"value":290},{"type":46,"value":778}," — Adaptive Metrics \u002F Logs \u002F Traces config; cost-attribution labels",{"type":40,"tag":89,"props":780,"children":781},{},[782,790],{"type":40,"tag":65,"props":783,"children":784},{"href":649},[785],{"type":40,"tag":107,"props":786,"children":788},{"className":787},[],[789],{"type":46,"value":649},{"type":46,"value":791}," — usage alert rules, cost-finding PromQL, billing-unit table",{"type":40,"tag":78,"props":793,"children":795},{"id":794},"resources",[796],{"type":46,"value":797},"Resources",{"type":40,"tag":85,"props":799,"children":800},{},[801,810,820],{"type":40,"tag":89,"props":802,"children":803},{},[804],{"type":40,"tag":65,"props":805,"children":807},{"href":67,"rel":806},[69],[808],{"type":46,"value":809},"Cost Management docs",{"type":40,"tag":89,"props":811,"children":812},{},[813],{"type":40,"tag":65,"props":814,"children":817},{"href":815,"rel":816},"https:\u002F\u002Fgrafana.com\u002Fdocs\u002Fgrafana-cloud\u002Fcost-management-and-billing\u002Freduce-costs\u002Fmetrics-costs\u002Fadaptive-metrics\u002F",[69],[818],{"type":46,"value":819},"Adaptive Metrics",{"type":40,"tag":89,"props":821,"children":822},{},[823],{"type":40,"tag":65,"props":824,"children":827},{"href":825,"rel":826},"https:\u002F\u002Fgrafana.com\u002Fdocs\u002Fgrafana-cloud\u002Fcost-management-and-billing\u002Freduce-costs\u002Flogs-costs\u002F",[69],[828],{"type":46,"value":829},"Adaptive Logs",{"type":40,"tag":831,"props":832,"children":833},"style",{},[834],{"type":46,"value":835},"html .light .shiki span {color: var(--shiki-light);background: var(--shiki-light-bg);font-style: var(--shiki-light-font-style);font-weight: var(--shiki-light-font-weight);text-decoration: var(--shiki-light-text-decoration);}html.light .shiki span {color: var(--shiki-light);background: var(--shiki-light-bg);font-style: var(--shiki-light-font-style);font-weight: var(--shiki-light-font-weight);text-decoration: var(--shiki-light-text-decoration);}html .default .shiki span {color: var(--shiki-default);background: var(--shiki-default-bg);font-style: var(--shiki-default-font-style);font-weight: var(--shiki-default-font-weight);text-decoration: var(--shiki-default-text-decoration);}html .shiki span {color: var(--shiki-default);background: var(--shiki-default-bg);font-style: var(--shiki-default-font-style);font-weight: var(--shiki-default-font-weight);text-decoration: var(--shiki-default-text-decoration);}html .dark .shiki span {color: var(--shiki-dark);background: var(--shiki-dark-bg);font-style: var(--shiki-dark-font-style);font-weight: var(--shiki-dark-font-weight);text-decoration: var(--shiki-dark-text-decoration);}html.dark .shiki span {color: var(--shiki-dark);background: var(--shiki-dark-bg);font-style: var(--shiki-dark-font-style);font-weight: var(--shiki-dark-font-weight);text-decoration: var(--shiki-dark-text-decoration);}",{"items":837,"total":951},[838,853,870,886,903,918,936],{"slug":839,"name":839,"fn":840,"description":841,"org":842,"tags":843,"stars":23,"repoUrl":24,"updatedAt":852},"adaptive-metrics","optimize Grafana Cloud metrics costs","Cut Grafana Cloud Metrics cost by shrinking active-series count with Adaptive Metrics aggregation rules — auto-recommendations from query history, custom exact\u002Fregex rules, label-drop config, unused-metric detection, and Alloy remote_write fallback. Use when investigating a high Mimir\u002FGrafana Cloud bill, hunting high-cardinality labels (`pod_uid`, `service_instance_id`, `version`), pre-aggregating counters\u002Fgauges, dropping unused metrics, or measuring `grafanacloud_instance_active_series` before\u002Fafter — even when the user says \"reduce cardinality\", \"too many series\", \"metrics spend\", \"active series count is exploding\", or \"drop the version label\" without naming Adaptive Metrics.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[844,845,846,849],{"name":20,"slug":21,"type":15},{"name":9,"slug":8,"type":15},{"name":847,"slug":848,"type":15},"Metrics","metrics",{"name":850,"slug":851,"type":15},"Observability","observability","2026-07-12T07:44:27.451068",{"slug":854,"name":854,"fn":855,"description":856,"org":857,"tags":858,"stars":23,"repoUrl":24,"updatedAt":869},"admin","manage Grafana Cloud accounts and RBAC","Manage Grafana Cloud accounts — organizations, stacks, RBAC roles and assignments, SSO\u002FSAML\u002FOAuth\u002FGitHub auth, service accounts for CI\u002FCD, user invites, team membership, and API-driven provisioning. Creates stacks via the Cloud API, mints service-account tokens, applies role assignments, configures SSO providers, and provisions teams\u002Ffolders\u002Fdashboards via Terraform. Use when managing Grafana Cloud access, configuring SSO\u002FSAML\u002FOAuth, setting up service accounts for Terraform\u002FCI\u002FCD, assigning RBAC roles, inviting users, managing multiple stacks or organizations, provisioning cloud resources via API or Terraform, or auditing admin actions — even when the user says \"set up SSO\", \"create a stack\", \"make a service account\", or \"onboard a team\" without explicitly saying \"admin\".",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[859,862,865,866],{"name":860,"slug":861,"type":15},"Access Control","access-control",{"name":863,"slug":864,"type":15},"Auth","auth",{"name":9,"slug":8,"type":15},{"name":867,"slug":868,"type":15},"Operations","operations","2026-07-12T07:44:12.078436",{"slug":871,"name":871,"fn":872,"description":873,"org":874,"tags":875,"stars":23,"repoUrl":24,"updatedAt":885},"admission-control","implement admission control webhooks","Use when the user asks to \"write a validator\", \"add validation\", \"implement admission control\", \"write a mutating webhook\", \"add a mutation handler\", \"validate incoming resources\", \"implement admission logic\", \"add admission webhooks\", \"write ingress validation\", or asks how to validate or mutate resources before they are persisted in a grafana-app-sdk app. Provides guidance on implementing validation and mutation admission handlers for grafana-app-sdk apps.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[876,879,882],{"name":877,"slug":878,"type":15},"Architecture","architecture",{"name":880,"slug":881,"type":15},"Security","security",{"name":883,"slug":884,"type":15},"Validation","validation","2026-07-12T07:45:06.148973",{"slug":887,"name":887,"fn":888,"description":889,"org":890,"tags":891,"stars":23,"repoUrl":24,"updatedAt":902},"alerting-irm","configure Grafana Alerting and Incident Management","Configure Grafana Alerting, Incident Response Management (IRM), and SLOs end-to-end — provisions Grafana-managed and data-source-managed alert rules, contact points (Slack\u002FPagerDuty\u002Femail\u002Fwebhook), notification policies with hierarchical matchers, silences, mute timings, on-call schedules and escalation chains, incident-management integrations, and SLOs with multi-window burn-rate alerts. Use when configuring alerts, debugging notification routing, setting up on-call rotations, declaring or managing incidents, defining SLOs, provisioning alerting via YAML or API, picking matchers for a notification policy, building a PagerDuty\u002FSlack webhook receiver, or troubleshooting why an alert isn't firing — even when the user says \"page me on errors\", \"alert me when X happens\", \"route this to the platform team\", or \"set up an SLO\" without naming Alerting or IRM.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[892,895,896,899],{"name":893,"slug":894,"type":15},"Alerting","alerting",{"name":9,"slug":8,"type":15},{"name":897,"slug":898,"type":15},"Incident Response","incident-response",{"name":900,"slug":901,"type":15},"Monitoring","monitoring","2026-07-12T07:44:02.393397",{"slug":153,"name":153,"fn":904,"description":905,"org":906,"tags":907,"stars":23,"repoUrl":24,"updatedAt":917},"build unified telemetry pipelines with Grafana Alloy","Build a unified telemetry pipeline with Grafana Alloy — one OpenTelemetry-compatible binary that collects metrics, logs, traces, and profiles and ships to Grafana Cloud \u002F Prometheus \u002F Loki \u002F Tempo \u002F Pyroscope. Covers the Alloy config language (blocks, `sys.env`, component refs), `prometheus.scrape` → `remote_write`, `loki.source.file` + `loki.process` → `loki.write`, `otelcol.receiver.otlp` → `otelcol.exporter.otlp`, `pyroscope.scrape`, K8s \u002F Docker \u002F EC2 discovery, relabeling, modules (`import.file\u002Fgit\u002Fhttp`), clustering, Fleet Management `remotecfg`, the Alloy UI at `:12345`, and `alloy fmt` \u002F `alloy validate`. Use when writing a `config.alloy`, replacing Grafana Agent \u002F OTel Collector, scraping K8s pods, parsing logs, ingesting OTLP, or debugging \"Alloy isn't sending anything\" — even when the user says \"set up the agent\", \"write me a scrape config\", \"drop these logs before sending\", or \"OTel collector config\" without naming Alloy.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[908,909,912,913,914],{"name":9,"slug":8,"type":15},{"name":910,"slug":911,"type":15},"Logs","logs",{"name":847,"slug":848,"type":15},{"name":850,"slug":851,"type":15},{"name":915,"slug":916,"type":15},"OpenTelemetry","opentelemetry","2026-07-12T07:43:54.817139",{"slug":919,"name":919,"fn":920,"description":921,"org":922,"tags":923,"stars":23,"repoUrl":24,"updatedAt":935},"app-observability","monitor application performance in Grafana Cloud","Get RED metrics + service maps + frontend RUM + AI\u002FLLM monitoring out of Grafana Cloud — Application Observability (`traces_spanmetrics_*` from OTel traces, p50\u002Fp95\u002Fp99 latency, exemplar-to-trace, traces-to-logs \u002F profiles), Frontend Observability with the Faro Web SDK (Core Web Vitals, session replay, `pushError`, React + router integration, `TracingInstrumentation` for browser → backend trace correlation), and AI Observability via OpenLIT (token \u002F cost \u002F latency, GPU, hallucination + toxicity evals). Use when standing up APM for a service, wiring an Alloy OTLP receiver + forwarding to Cloud, instrumenting a React frontend for RUM, debugging why service-map edges are missing, monitoring LLM cost drift, or correlating a frontend error to its backend trace — even when the user says \"set up APM\", \"show service map\", \"monitor browser perf\", \"session replay\", \"RUM SDK\", or \"watch our OpenAI bill\" without naming App \u002F Frontend \u002F AI Observability.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[924,927,930,931,934],{"name":925,"slug":926,"type":15},"APM","apm",{"name":928,"slug":929,"type":15},"Distributed Tracing","distributed-tracing",{"name":9,"slug":8,"type":15},{"name":932,"slug":933,"type":15},"LLM","llm",{"name":850,"slug":851,"type":15},"2026-07-12T07:44:34.500406",{"slug":937,"name":937,"fn":938,"description":939,"org":940,"tags":941,"stars":23,"repoUrl":24,"updatedAt":950},"app-sdk-concepts","scaffold and configure Grafana apps","Use when starting any grafana-app-sdk work — scaffolding a Grafana app, initializing a Grafana App Platform app, picking a deployment mode (standalone operator \u002F grafana\u002Fapps \u002F frontend-only), wiring app-specific config, or onboarding to the SDK. Covers `grafana-app-sdk` CLI install, `project init` per deployment mode, project layout, the schema-centric workflow (CUE kinds → generated code → reconciler\u002Fadmission logic), and `SpecificConfig` for env-driven app configuration. Use even if the user just says \"make a new app\", \"Grafana app platform\", \"kinds and watchers\", \"operator scaffold\", or asks about `apps\u002F` inside the Grafana repo, without naming the SDK explicitly.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[942,943,946,949],{"name":877,"slug":878,"type":15},{"name":944,"slug":945,"type":15},"Deployment","deployment",{"name":947,"slug":948,"type":15},"Engineering","engineering",{"name":9,"slug":8,"type":15},"2026-07-12T07:45:08.595757",48,{"items":953,"total":1133},[954,969,988,1008,1023,1037,1050,1065,1082,1095,1108,1121],{"slug":955,"name":955,"fn":956,"description":957,"org":958,"tags":959,"stars":966,"repoUrl":967,"updatedAt":968},"faro-setup-web","instrument web apps with Grafana Faro","Instruments a web app with Grafana Faro Web SDK for frontend observability. Use when setting up error tracking, Web Vitals, session monitoring, or distributed tracing in a browser app.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[960,961,964,965],{"name":928,"slug":929,"type":15},{"name":962,"slug":963,"type":15},"Frontend","frontend",{"name":900,"slug":901,"type":15},{"name":850,"slug":851,"type":15},1103,"https:\u002F\u002Fgithub.com\u002Fgrafana\u002Ffaro-web-sdk","2026-07-12T07:43:24.63314",{"slug":970,"name":970,"fn":971,"description":972,"org":973,"tags":974,"stars":985,"repoUrl":986,"updatedAt":987},"configuring-yesoreyeram-infinity-datasource","configure Grafana Infinity data source","Configure the Infinity data source — base URL and allowed hosts, the authentication methods (basic, bearer token, API key, digest, OAuth passthrough, OAuth 2.0 client credentials\u002FJWT, Azure, Azure Blob, AWS), TLS, custom HTTP headers, network and security settings, the custom health check, and provisioning with a config file. Use when a user asks how to set up, configure, or change settings for the Infinity data source; how to authenticate to an API; how to allow hosts; how to provision it as YAML; or how to troubleshoot connection, authentication, or health-check issues.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[975,978,981,984],{"name":976,"slug":977,"type":15},"API Development","api-development",{"name":979,"slug":980,"type":15},"Authentication","authentication",{"name":982,"slug":983,"type":15},"Configuration","configuration",{"name":9,"slug":8,"type":15},1056,"https:\u002F\u002Fgithub.com\u002Fgrafana\u002Fgrafana-infinity-datasource","2026-07-12T07:43:25.939136",{"slug":989,"name":989,"fn":990,"description":991,"org":992,"tags":993,"stars":985,"repoUrl":986,"updatedAt":1007},"querying-yesoreyeram-infinity-datasource","query data with Infinity datasource","Build queries with the Infinity data source — the query types (JSON, CSV, TSV, XML, GraphQL, HTML, UQL, GROQ, Google Sheets, Series, Transformations), the parsers (Frontend\u002Fsimple, Backend JSONata, JQ, UQL, GROQ) and which support alerting, the sources (URL, Inline, Reference, Azure Blob, Random walk), output formats (table, timeseries, logs, trace, node graph, dataframe), root selector and columns, computed columns\u002Ffilters\u002Fsummarize, pagination, and template variables. Use when a user asks how to query Infinity; how to fetch JSON\u002FCSV\u002FXML\u002FGraphQL\u002FHTML from a URL or inline; how to select rows and columns; how to transform data with UQL\u002FGROQ\u002FJSONata\u002FJQ; how to make a query work with alerting; how to paginate; or how to use variables in a query.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[994,997,1000,1001,1004],{"name":995,"slug":996,"type":15},"CSV","csv",{"name":998,"slug":999,"type":15},"Data Analysis","data-analysis",{"name":9,"slug":8,"type":15},{"name":1002,"slug":1003,"type":15},"GraphQL","graphql",{"name":1005,"slug":1006,"type":15},"JSON","json","2026-07-15T05:34:05.773947",{"slug":1009,"name":1009,"fn":1010,"description":1011,"org":1012,"tags":1013,"stars":1020,"repoUrl":1021,"updatedAt":1022},"agento11y","manage Grafana Agent Observability resources","Inspects and manages Grafana Agent Observability resources via gcx: conversations, generations, evaluators, rules, scores, and templates. Use when the user wants to list or search conversations, inspect generations, manage evaluators (upsert, test, delete), set up evaluation rules, check scores, or browse evaluator templates. Trigger on phrases like \"list conversations\", \"search generations\", \"what did the agent do\", \"debug LLM conversation\", \"create evaluator\", \"set up evaluation rule\", \"test evaluator\", \"check scores\", \"evaluate generation quality\", or \"set up online evaluation\".\n",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1014,1017,1018,1019],{"name":1015,"slug":1016,"type":15},"Agents","agents",{"name":9,"slug":8,"type":15},{"name":900,"slug":901,"type":15},{"name":850,"slug":851,"type":15},430,"https:\u002F\u002Fgithub.com\u002Fgrafana\u002Fgcx","2026-07-25T05:30:40.29622",{"slug":1024,"name":1024,"fn":1025,"description":1026,"org":1027,"tags":1028,"stars":1020,"repoUrl":1021,"updatedAt":1036},"agento11y-instrument","instrument LLM apps for agent observability","Sets up and instruments a developer's own LLM app or agent to send generations and agentic workflow to Grafana Agent Observability (the Agent Observability SDKs) — greenfield setup, fixing broken instrumentation, or filling gaps in existing instrumentation. Uses gcx for the parts a static prompt can't do: `gcx login` \u002F `gcx cloud stacks` to find the stack, and `gcx agento11y agents|conversations|generations` to VERIFY that data actually lands — so it iterates (instrument → run → verify → fix) until generations arrive, not blindly. Reads the app's code, detects language\u002Fframework, classifies instrumentation state (none \u002F partial \u002F broken), then runs a fixed gap checklist whose #1 item is the silent failure no other prompt catches: the SDK emits OTel spans\u002Fmetrics but never creates a TracerProvider\u002FMeterProvider, so without them all metrics go to a no-op and are lost. Also checks agent_version (required for per-version Performance charts), set_result completeness, SYNC vs STREAM, parent_generation_ids DAG links, and workflow-step coverage. Recommends changes citing file:line and, only with explicit confirmation, applies minimal diffs that don't change app behavior. Pulls SDK reference from agento11y's llms.txt rather than restating it, and hands off to `agento11y-test-starter` once data flows. It does NOT write test suites or set up tenant evaluations, rules, or guards — offline test suites are `agento11y-test-starter`, tenant eval rules + guards are `agento11y-prod-setup`; does NOT install coding-agent telemetry plugins (that is llms.txt \"Path A\"); does NOT mint or store credentials or invent endpoints. Trigger on phrases like \"instrument my app\", \"send my agent's traces to Grafana\", \"set up AI observability for my app\", \"my generations aren't showing up\", \"why is Performance empty\", \"add Agent Observability to my code\", \"fix my instrumentation\".\n",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1029,1030,1031,1034,1035],{"name":1015,"slug":1016,"type":15},{"name":9,"slug":8,"type":15},{"name":1032,"slug":1033,"type":15},"Instrumentation","instrumentation",{"name":932,"slug":933,"type":15},{"name":850,"slug":851,"type":15},"2026-07-31T05:53:52.580237",{"slug":1038,"name":1038,"fn":1039,"description":1040,"org":1041,"tags":1042,"stars":1020,"repoUrl":1021,"updatedAt":1049},"agento11y-prod-setup","setup production evaluation for AI agents","Sets up production evaluation and guardrails for a DEPLOYED AI agent in Grafana Agent Observability, grounded in the agent's own code and its real ingested traffic. The judgment layer on top of the `agento11y` skill: it reads the agent's source (system prompt, tools, entrypoint) AND samples its live traffic via gcx, checks what evaluators\u002Frules\u002Fguards already exist, then recommends only what's missing — online eval rules (score live conversations for regressions) and guards (warn-first request-path policies that redact \u002F tool-filter and may later be promoted to deny). It drafts reviewable YAML and, only with explicit confirmation, applies via `gcx agento11y`. New guards are drafted in warn mode (safe on live traffic — warn records but never blocks). It DOES create stack-level objects — that is the point — but every write is confirmed. It never rewrites or redeploys the agent. Trigger on phrases like \"set up production evaluation\", \"my agent is in prod what should I evaluate\", \"catch quality regressions\", \"add guardrails to my agent\", \"redact PII from my agent\", \"block dangerous tools\", \"set up online evals and guards\".\n",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1043,1044,1047,1048],{"name":1015,"slug":1016,"type":15},{"name":1045,"slug":1046,"type":15},"Evals","evals",{"name":9,"slug":8,"type":15},{"name":850,"slug":851,"type":15},"2026-07-31T05:53:53.576347",{"slug":1051,"name":1051,"fn":1052,"description":1053,"org":1054,"tags":1055,"stars":1020,"repoUrl":1021,"updatedAt":1064},"agento11y-test-starter","build and run agent test suites","Use early in an AI-agent project — before ship, before real traffic — to build a starter test suite for the agent and run it offline. Reads the agent's own code (system prompt, tools, task), writes a labeled draft suite of test cases (happy\u002Fedge\u002Fadversarial) grounded in real lines, and recommends how to score each case (the evaluators\u002Fjudges the offline runner uses). Assesses how runnable the agent is: for an easily-invoked agent it generates a runner stub (run_experiment.py) with two holes to fill and can optionally run it (only with permission, only against the endpoint the developer configured); for agents needing a harness or full runtime it points to the existing eval infra. It runs OFFLINE and never creates tenant-level evaluators, rules, or guards — that is `agento11y-prod-setup`, for a deployed agent with real traffic. Trigger on phrases like \"how do I test my agent before shipping\", \"write test cases for my agent\", \"set up tests for my agent\", \"check my agent before prod\", \"I have no traffic yet, how do I evaluate it\", \"test my agent offline\".\n",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1056,1057,1058,1061],{"name":1015,"slug":1016,"type":15},{"name":9,"slug":8,"type":15},{"name":1059,"slug":1060,"type":15},"QA","qa",{"name":1062,"slug":1063,"type":15},"Testing","testing","2026-07-31T05:53:51.62785",{"slug":1066,"name":1066,"fn":1067,"description":1068,"org":1069,"tags":1070,"stars":1020,"repoUrl":1021,"updatedAt":1081},"create-dashboard","create Grafana dashboards with gcx","Designs and creates Grafana dashboards with gcx, using `gcx dashboards snapshot` as a visual feedback loop. Use when the user wants to create a new Grafana dashboard, add panels, variables, or annotations to an existing dashboard, design dashboard panels, variables, queries, or layout, or make a material visual redesign. Triggers on \"create dashboard\", \"new dashboard\", \"build dashboard\", \"dashboard for \u003Cservice>\", \"add panels\", \"add variable\", \"add annotation\", \"improve this dashboard\", or \"iterate on a dashboard\".\n",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1071,1074,1077,1080],{"name":1072,"slug":1073,"type":15},"Dashboards","dashboards",{"name":1075,"slug":1076,"type":15},"Data Visualization","data-visualization",{"name":1078,"slug":1079,"type":15},"Design","design",{"name":9,"slug":8,"type":15},"2026-07-25T05:30:46.289717",{"slug":1083,"name":1083,"fn":1084,"description":1085,"org":1086,"tags":1087,"stars":1020,"repoUrl":1021,"updatedAt":1094},"debug-with-grafana","investigate application issues with Grafana","Structured workflow for investigating application problems with Grafana observability data (metrics, logs, traces) via gcx. Covers live firefighting AND retrospective incident analysis: incident triage, root-cause analysis, blast-radius checks (did an incident spill into other services), verifying whether a deployment or rollout triggered an incident, finding which service, endpoint, or path owns the most errors or slow requests, checking whether retries or queue backlogs piled up, and quantifying error or latency shares over a time window. Trigger on: \"my API is returning 500 errors\", \"latency is spiking\", \"investigate why requests are failing\", \"triage the incident\", \"blast radius\", \"root cause\", \"did the rollout cause it\", \"which endpoint owns the most 5xx\", \"did retries pile up\", or any request to analyse an earlier incident window using telemetry. For authoring dashboards use create-dashboard; for dashboard inventory use manage-dashboards.\n",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1088,1091,1092,1093],{"name":1089,"slug":1090,"type":15},"Debugging","debugging",{"name":9,"slug":8,"type":15},{"name":897,"slug":898,"type":15},{"name":850,"slug":851,"type":15},"2026-07-18T05:11:10.445428",{"slug":1096,"name":1096,"fn":1097,"description":1098,"org":1099,"tags":1100,"stars":1020,"repoUrl":1021,"updatedAt":1107},"diagnose-entity-graph","diagnose Grafana Entity Graph issues","Diagnose Entity Graph problems: missing entities, missing edges, disconnected clusters, or filtering issues. Use when the user reports that Entity Graph doesn't look right, services are missing, edges aren't appearing, or environments can't be filtered. Triggers for: \"entity graph is empty\", \"services missing from entity graph\", \"no edges in entity graph\", \"disconnected services\", \"can't filter entity graph\", \"entity graph not working\", \"diagnose entity graph\", \"debug knowledge graph\".\n",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1101,1102,1103,1106],{"name":1089,"slug":1090,"type":15},{"name":9,"slug":8,"type":15},{"name":1104,"slug":1105,"type":15},"Graph Analysis","graph-analysis",{"name":850,"slug":851,"type":15},"2026-07-25T05:30:39.380934",{"slug":1109,"name":1109,"fn":1110,"description":1111,"org":1112,"tags":1113,"stars":1020,"repoUrl":1021,"updatedAt":1120},"gcx","manage Grafana Cloud resources via gcx","Manages Grafana Cloud resources via the gcx CLI. Trigger when the user wants to inspect, create, update, delete, query, or automate any Grafana resource - dashboards, datasources, alerts, SLOs, synthetic checks, oncall, incidents, fleet, k6, knowledge graph, or adaptive telemetry.\n",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1114,1117,1118,1119],{"name":1115,"slug":1116,"type":15},"CLI","cli",{"name":9,"slug":8,"type":15},{"name":900,"slug":901,"type":15},{"name":867,"slug":868,"type":15},"2026-07-31T05:53:50.587304",{"slug":1122,"name":1122,"fn":1123,"description":1124,"org":1125,"tags":1126,"stars":1020,"repoUrl":1021,"updatedAt":1132},"gcx-demo","present gcx demo tours","Run a narrated, read-only demo tour of gcx for customer or colleague presentations. Showcases the breadth of gcx across every Grafana Cloud product area — resources, datasources, metrics, logs, traces, SLOs, alerts, synthetic monitoring, IRM, k6, fleet, and more. All commands are strictly read-only. Trigger when the user says \"demo gcx\", \"show off gcx\", \"customer demo\", \"gcx tour\", or \"\u002Fgcx-demo\".\n",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1127,1128,1129],{"name":1115,"slug":1116,"type":15},{"name":9,"slug":8,"type":15},{"name":1130,"slug":1131,"type":15},"Presentations","presentations","2026-07-25T05:30:45.282458",80]