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Use when asked to reduce Axiom costs, find unused columns or field values, identify data waste, or track ingest spend.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},"axiom","Axiom","https:\u002F\u002Fpexgzepcugksgbtrxkhf.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Forg-logos\u002Faxiom.png","axiomhq",[13,17,20,21],{"name":14,"slug":15,"type":16},"Observability","observability","tag",{"name":18,"slug":19,"type":16},"Dashboards","dashboards",{"name":9,"slug":8,"type":16},{"name":22,"slug":23,"type":16},"Cost Optimization","cost-optimization",11,"https:\u002F\u002Fgithub.com\u002Faxiomhq\u002Fskills","2026-04-06T18:04:22.202025",null,1,[],{"repoUrl":25,"stars":24,"forks":28,"topics":31,"description":27},[],"https:\u002F\u002Fgithub.com\u002Faxiomhq\u002Fskills\u002Ftree\u002FHEAD\u002Fskills\u002Fcontrolling-costs","---\nname: controlling-costs\ndescription: Analyzes Axiom query patterns to find unused data, then builds dashboards and monitors for cost optimization. Use when asked to reduce Axiom costs, find unused columns or field values, identify data waste, or track ingest spend.\n---\n\n# Axiom Cost Control\n\nDashboards, monitors, and waste identification for Axiom usage optimization.\n\n## Before You Start\n\n1. Load required skills:\n   ```\n   skill: axiom-sre\n   skill: building-dashboards\n   ```\n   \n   Building-dashboards provides: `dashboard-list`, `dashboard-get`, `dashboard-create`, `dashboard-update`, `dashboard-delete`\n\n2. Find the audit dataset. Try `axiom-audit` first:\n   ```apl\n   ['axiom-audit']\n   | where _time > ago(1h)\n   | summarize count() by action\n   | where action in ('usageCalculated', 'runAPLQueryCost')\n   ```\n   - If not found → ask user. Common names: `axiom-audit-logs-view`, `audit-logs`\n   - If found but no `usageCalculated` events → wrong dataset, ask user\n\n3. Verify `axiom-history` access (required for Phase 4):\n   ```apl\n   ['axiom-history'] | where _time > ago(1h) | take 1\n   ```\n   If not found, Phase 4 optimization will not work.\n\n4. Confirm with user:\n   - Deployment name?\n   - Audit dataset name?\n   - Contract limit in TB\u002Fday? (required for Phase 3 monitors)\n\n5. Replace `\u003Cdeployment>` and `\u003Caudit-dataset>` in all commands below.\n\n**Tips:**\n- Run any script with `-h` for full usage\n- Do NOT pipe script output to `head` or `tail` — causes SIGPIPE errors\n- Requires `jq` for JSON parsing\n- Use axiom-sre's `axiom-query` for ad-hoc APL, not direct CLI\n\n## Which Phases to Run\n\n| User request | Run these phases |\n|--------------|------------------|\n| \"reduce costs\" \u002F \"find waste\" | 0 → 1 → 4 |\n| \"set up cost control\" | 0 → 1 → 2 → 3 |\n| \"deploy dashboard\" | 0 → 2 |\n| \"create monitors\" | 0 → 3 |\n| \"check for drift\" | 0 only |\n\n---\n\n## Phase 0: Check Existing Setup\n\n```bash\n# Existing dashboard?\ndashboard-list \u003Cdeployment> | grep -i cost\n\n# Existing monitors?\naxiom-api \u003Cdeployment> GET \"\u002Fv2\u002Fmonitors\" | jq -r '.[] | select(.name | startswith(\"Cost Control:\")) | \"\\(.id)\\t\\(.name)\"'\n```\n\nIf found, fetch with `dashboard-get` and compare to `templates\u002Fdashboard.json` for drift.\n\n---\n\n## Phase 1: Discovery\n\n```bash\nscripts\u002Fbaseline-stats -d \u003Cdeployment> -a \u003Caudit-dataset>\n```\n\nCaptures daily ingest stats and produces the **Analysis Queue** (needed for Phase 4).\n\n---\n\n## Phase 2: Dashboard\n\n```bash\nscripts\u002Fdeploy-dashboard -d \u003Cdeployment> -a \u003Caudit-dataset>\n```\n\nCreates dashboard with: ingest trends, burn rate, projections, waste candidates, top users. See `reference\u002Fdashboard-panels.md` for details.\n\n---\n\n## Phase 3: Monitors\n\n**Contract is required.** You must have the contract limit from preflight step 4.\n\n### Step 1: List available notifiers\n\n```bash\nscripts\u002Flist-notifiers -d \u003Cdeployment>\n```\n\nPresent the list to the user and ask which notifier they want for cost alerts.\nIf they don't want notifications, proceed without `-n`.\n\n### Step 2: Create monitors\n\n```bash\nscripts\u002Fcreate-monitors -d \u003Cdeployment> -a \u003Caudit-dataset> -c \u003Ccontract_tb> [-n \u003Cnotifier_id>]\n```\n\nCreates 3 monitors:\n\n1. **Total Ingest Guard** — alerts when daily ingest >1.2x contract OR 7-day avg grows >15% vs baseline\n2. **Per-Dataset Spike** — robust z-score detection, alerts per dataset with attribution\n3. **Query Cost Spike** — hardened z-score with 30d baseline, 5d exclusion gap, persistence-based gating (median_z > 3, p25_z > 2.5)\n\nThe spike monitors use `notifyByGroup: true` so each dataset triggers a separate alert.\n\nSee `reference\u002Fmonitor-strategy.md` for threshold derivation.\n\n---\n\n## Phase 4: Optimization\n\n### Get the Analysis Queue\n\nRun `scripts\u002Fbaseline-stats` if not already done. It outputs a prioritized list:\n\n| Priority | Meaning |\n|----------|---------|\n| P0⛔ | Top 3 by ingest OR >10% of total — MANDATORY |\n| P1 | Never queried — strong drop candidate |\n| P2 | Rarely queried (Work\u002FGB \u003C 100) — likely waste |\n\n**Work\u002FGB** = query cost (GB·ms) \u002F ingest (GB). Lower = less value from data.\n\n### Analyze datasets in order\n\nWork top-to-bottom. For each dataset:\n\n**Step 1: Column analysis**\n```bash\nscripts\u002Fanalyze-query-coverage -d \u003Cdeployment> -D \u003Cdataset> -a \u003Caudit-dataset>\n```\n\nIf 0 queries → recommend DROP, move to next.\n\n**Step 2: Field value analysis**\n\nPick a field from suggested list (usually `app`, `service`, or `kubernetes.labels.app`):\n```bash\nscripts\u002Fanalyze-query-coverage -d \u003Cdeployment> -D \u003Cdataset> -a \u003Caudit-dataset> -f \u003Cfield>\n```\n\nNote values with high volume but never queried (⚠️ markers).\n\n**Step 3: Handle empty values**\n\nIf `(empty)` has >5% volume, you MUST drill down with alternative field (e.g., `kubernetes.namespace_name`).\n\n**Step 4: Record recommendation**\n\nFor each dataset, note: name, ingest volume, Work\u002FGB, top unqueried values, action (DROP\u002FSAMPLE\u002FKEEP), estimated savings.\n\n### Done when\n\nAll P0⛔ and P1 datasets analyzed. Then compile report using `reference\u002Fanalysis-report-template.md`.\n\n---\n\n---\n\n## Cleanup\n\n```bash\n# Delete monitors\naxiom-api \u003Cdeployment> GET \"\u002Fv2\u002Fmonitors\" | jq -r '.[] | select(.name | startswith(\"Cost Control:\")) | \"\\(.id)\\t\\(.name)\"'\naxiom-api \u003Cdeployment> DELETE \"\u002Fv2\u002Fmonitors\u002F\u003Cid>\"\n\n# Delete dashboard\ndashboard-list \u003Cdeployment> | grep -i cost\ndashboard-delete \u003Cdeployment> \u003Cid>\n```\n\n**Note:** Running `create-monitors` twice creates duplicates. Delete existing monitors first if re-deploying.\n\n---\n\n## Reference\n\n### Audit Dataset Fields\n\n| Field | Description |\n|-------|-------------|\n| `action` | `usageCalculated` or `runAPLQueryCost` |\n| `properties.hourly_ingest_bytes` | Hourly ingest in bytes |\n| `properties.hourly_billable_query_gbms` | Hourly query cost |\n| `properties.dataset` | Dataset name |\n| `resource.id` | Org ID |\n| `actor.email` | User email |\n\n### Common Fields for Value Analysis\n\n| Dataset type | Primary field | Alternatives |\n|--------------|---------------|--------------|\n| Kubernetes logs | `kubernetes.labels.app` | `kubernetes.namespace_name`, `kubernetes.container_name` |\n| Application logs | `app` or `service` | `level`, `logger`, `component` |\n| Infrastructure | `host` | `region`, `instance` |\n| Traces | `service.name` | `span.kind`, `http.route` |\n\n### Units & Conversions\n\n- Scripts use **TB\u002Fday**\n- Dashboard filter uses **GB\u002Fmonth**\n\n| Contract | TB\u002Fday | GB\u002Fmonth |\n|----------|--------|----------|\n| 5 PB\u002Fmonth | 167 | 5,000,000 |\n| 10 PB\u002Fmonth | 333 | 10,000,000 |\n| 15 PB\u002Fmonth | 500 | 15,000,000 |\n\n### Optimization Actions\n\n| Signal | Action |\n|--------|--------|\n| Work\u002FGB = 0 | Drop or stop ingesting |\n| High-volume unqueried values | Sample or reduce log level |\n| Empty values from system namespaces | Filter at ingest or accept |\n| WoW spike | Check recent deploys |\n",{"data":35,"body":36},{"name":4,"description":6},{"type":37,"children":38},"root",[39,48,54,61,295,304,367,373,466,470,476,636,656,659,665,724,736,739,745,800,813,816,822,832,839,874,887,893,1000,1005,1038,1051,1064,1067,1073,1079,1091,1152,1162,1168,1173,1181,1258,1263,1271,1299,1395,1400,1408,1429,1437,1442,1448,1460,1463,1466,1472,1688,1706,1709,1715,1721,1855,1861,2030,2036,2059,2138,2144,2218],{"type":40,"tag":41,"props":42,"children":44},"element","h1",{"id":43},"axiom-cost-control",[45],{"type":46,"value":47},"text","Axiom Cost Control",{"type":40,"tag":49,"props":50,"children":51},"p",{},[52],{"type":46,"value":53},"Dashboards, monitors, and waste identification for Axiom usage optimization.",{"type":40,"tag":55,"props":56,"children":58},"h2",{"id":57},"before-you-start",[59],{"type":46,"value":60},"Before You Start",{"type":40,"tag":62,"props":63,"children":64},"ol",{},[65,125,219,251,274],{"type":40,"tag":66,"props":67,"children":68},"li",{},[69,71,84,88,90,96,98,104,105,111,112,118,119],{"type":46,"value":70},"Load required skills:",{"type":40,"tag":72,"props":73,"children":77},"pre",{"className":74,"code":76,"language":46},[75],"language-text","skill: axiom-sre\nskill: building-dashboards\n",[78],{"type":40,"tag":79,"props":80,"children":82},"code",{"__ignoreMap":81},"",[83],{"type":46,"value":76},{"type":40,"tag":85,"props":86,"children":87},"br",{},[],{"type":46,"value":89},"Building-dashboards provides: ",{"type":40,"tag":79,"props":91,"children":93},{"className":92},[],[94],{"type":46,"value":95},"dashboard-list",{"type":46,"value":97},", ",{"type":40,"tag":79,"props":99,"children":101},{"className":100},[],[102],{"type":46,"value":103},"dashboard-get",{"type":46,"value":97},{"type":40,"tag":79,"props":106,"children":108},{"className":107},[],[109],{"type":46,"value":110},"dashboard-create",{"type":46,"value":97},{"type":40,"tag":79,"props":113,"children":115},{"className":114},[],[116],{"type":46,"value":117},"dashboard-update",{"type":46,"value":97},{"type":40,"tag":79,"props":120,"children":122},{"className":121},[],[123],{"type":46,"value":124},"dashboard-delete",{"type":40,"tag":66,"props":126,"children":127},{},[128,130,136,138,184],{"type":46,"value":129},"Find the audit dataset. 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Use when investigating a trace ID, finding traces by criteria (errors, latency, service), or debugging distributed system issues.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[2270,2271,2274,2277,2278],{"name":9,"slug":8,"type":16},{"name":2272,"slug":2273,"type":16},"Debugging","debugging",{"name":2275,"slug":2276,"type":16},"Distributed Tracing","distributed-tracing",{"name":14,"slug":15,"type":16},{"name":2279,"slug":2280,"type":16},"OpenTelemetry","opentelemetry","2026-04-06T18:04:17.130694",{"slug":2283,"name":2283,"fn":2284,"description":2285,"org":2286,"tags":2287,"stars":24,"repoUrl":25,"updatedAt":2300},"axiom-alerting","manage Axiom monitors and notifiers","Create and manage Axiom monitors and notifiers via the v2 public API. 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Use for incident response, root cause analysis, production debugging, or log investigation.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[2307,2308,2309,2312,2313],{"name":9,"slug":8,"type":16},{"name":2272,"slug":2273,"type":16},{"name":2310,"slug":2311,"type":16},"Incident Response","incident-response",{"name":14,"slug":15,"type":16},{"name":2314,"slug":2315,"type":16},"SRE","sre","2026-04-06T18:04:27.289824",{"slug":2318,"name":2318,"fn":2319,"description":2320,"org":2321,"tags":2322,"stars":24,"repoUrl":25,"updatedAt":2327},"building-dashboards","build Axiom dashboards via API","Designs and builds Axiom dashboards via API. Covers chart types, APL and metrics\u002FMPL query patterns, SmartFilters, layout, and configuration options. Use when creating dashboards, migrating from Splunk, or configuring chart options.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[2323,2324,2325,2326],{"name":2233,"slug":142,"type":16},{"name":9,"slug":8,"type":16},{"name":18,"slug":19,"type":16},{"name":14,"slug":15,"type":16},"2026-04-06T18:04:23.452912",{"slug":4,"name":4,"fn":5,"description":6,"org":2329,"tags":2330,"stars":24,"repoUrl":25,"updatedAt":26},{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[2331,2332,2333,2334],{"name":9,"slug":8,"type":16},{"name":22,"slug":23,"type":16},{"name":18,"slug":19,"type":16},{"name":14,"slug":15,"type":16},{"slug":2336,"name":2336,"fn":2337,"description":2338,"org":2339,"tags":2340,"stars":24,"repoUrl":25,"updatedAt":2352},"metrics-chart","render Axiom metrics as charts","Render Axiom metrics query results (application\u002Fvnd.metrics.v3+json) as line charts. Zero-dependency Unicode\u002FASCII by default; upgrades to inline PNG\u002FSVG\u002Fsixel via gnuplot when present. 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Use when asked to query metrics, explore metric datasets, check metric values, or investigate OTel metrics data.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[2359,2360,2361],{"name":9,"slug":8,"type":16},{"name":2349,"slug":2350,"type":16},{"name":14,"slug":15,"type":16},"2026-07-18T05:12:32.381376",{"slug":2364,"name":2364,"fn":2365,"description":2366,"org":2367,"tags":2368,"stars":24,"repoUrl":25,"updatedAt":2375},"spl-to-apl","translate Splunk SPL to Axiom APL","Translates Splunk SPL queries to Axiom APL. Provides command mappings, function equivalents, and syntax transformations. Use when migrating from Splunk, converting SPL queries, or learning APL equivalents of SPL patterns.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[2369,2370,2371,2374],{"name":2233,"slug":142,"type":16},{"name":9,"slug":8,"type":16},{"name":2372,"slug":2373,"type":16},"Migration","migration",{"name":14,"slug":15,"type":16},"2026-04-06T18:04:20.939952",{"slug":2377,"name":2377,"fn":2378,"description":2379,"org":2380,"tags":2381,"stars":24,"repoUrl":25,"updatedAt":2389},"writing-evals","scaffold evals for the Axiom AI SDK","Scaffolds evaluation suites for the Axiom AI SDK. Generates eval files, scorers, flag schemas, and config from natural-language descriptions. 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