[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill-axiom-metrics-chart":3,"mdc--ls9smb-key":37,"related-org-axiom-metrics-chart":2066,"related-repo-axiom-metrics-chart":2231},{"slug":4,"name":4,"fn":5,"description":6,"org":7,"tags":12,"stars":27,"repoUrl":28,"updatedAt":29,"license":30,"forks":31,"topics":32,"repo":33,"sourceUrl":35,"mdContent":36},"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. Use when you have a metrics v3 query response and want to see the series as a chart in the terminal or transcript.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},"axiom","Axiom","https:\u002F\u002Fpexgzepcugksgbtrxkhf.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Forg-logos\u002Faxiom.png","axiomhq",[13,17,20,21,24],{"name":14,"slug":15,"type":16},"Observability","observability","tag",{"name":18,"slug":19,"type":16},"Data Visualization","data-visualization",{"name":9,"slug":8,"type":16},{"name":22,"slug":23,"type":16},"Charts","charts",{"name":25,"slug":26,"type":16},"Metrics","metrics",11,"https:\u002F\u002Fgithub.com\u002Faxiomhq\u002Fskills","2026-07-18T05:47:14.576127",null,1,[],{"repoUrl":28,"stars":27,"forks":31,"topics":34,"description":30},[],"https:\u002F\u002Fgithub.com\u002Faxiomhq\u002Fskills\u002Ftree\u002FHEAD\u002Fskills\u002Fmetrics-chart","---\nname: metrics-chart\ndescription: 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. Use when you have a metrics v3 query response and want to see the series as a chart in the terminal or transcript.\n---\n\n# metrics-chart\n\nTurns a metrics query response into a **multi-series line chart**.\n\nInput is exactly the body the Axiom metrics query service returns for\n`application\u002Fvnd.metrics.v3+json` — defined in the metrics query service\n(`service\u002Fquery`, `JsonV3QueryResponse` in `src\u002Fserver.rs`). It is a\nregularly-sampled, multi-series time series; this skill draws it.\n\n## TL;DR\n\n```bash\n# From a file, stdout picks the best renderer for your terminal:\npython3 scripts\u002Fmetrics_chart.py response.json\n\n# From a pipe (e.g. straight off the query API):\ncurl ... -H 'Accept: application\u002Fvnd.metrics.v3+json' | python3 scripts\u002Fmetrics_chart.py\n\n# Force a zero-dependency ASCII chart (always works, anywhere):\npython3 scripts\u002Fmetrics_chart.py --format ascii response.json\n```\n\n> **Aggregate first.** Charts are only readable with a *handful* of series.\n> The cleanest result comes from a query that already groups\u002Faggregates to a few\n> lines (e.g. `group by status` rather than per-`pod`). This skill's series\n> reduction (below) is a safety net for high-cardinality results, **not** a\n> substitute for aggregating in the query.\n\n## Input format (vnd.metrics.v3+json)\n\n```json\n{\n  \"metadata\": {\n    \"group_keys\": [\"code\", \"method\"],\n    \"warnings\": [],\n    \"unit\": \"ms\",\n    \"custom_unit\": \"millisec\"\n  },\n  \"series\": [\n    {\n      \"metric\": \"http_requests\",\n      \"tags\": { \"code\": 200, \"method\": \"GET\", \"path\": \"\u002F\" },\n      \"start\": 1750753164,\n      \"resolution\": 60,\n      \"data\": [420.0, 42.0, 30.0, 9.0, 15.0, 18.0]\n    }\n  ]\n}\n```\n\n| Field                 | Meaning                                                                 |\n| --------------------- | ----------------------------------------------------------------------- |\n| `metadata.group_keys` | Tags the query grouped on → used to build legend labels.                |\n| `metadata.warnings`   | Shown as `⚠` captions under the chart.                                  |\n| `metadata.unit`       | Canonical y-axis unit (`ms`, `bytes`, …).                               |\n| `metadata.custom_unit`| Human y-axis label; preferred over `unit` when present.                 |\n| `series[].start`      | Unix **seconds** (UTC) of `data[0]`.                                    |\n| `series[].resolution` | **Seconds** per point; `x[i] = start + resolution*i` (uniform spacing). |\n| `series[].data`       | Y values; **`null` = gap** (the line breaks, it is not drawn as 0).     |\n\nThe parser also accepts v2\u002Fv2a (same shape with an extra per-series `summary`,\nignored) and the v0\u002Fv1 bare-array form, so you can feed older responses too.\n\n## Behaviour\n\n- **Labels** come from `group_keys` values joined with ` | ` → `200 | GET`\n  (not `code=200,method=GET`). Without `group_keys`, falls back to `k=v` tags.\n- **Local time** axis by default; `--tz UTC` or `--tz Europe\u002FBerlin` to override.\n- **Gaps**: `null` points break the line.\n- **Series reduction** (unless `--all`), in order:\n  1. **Collapse overlapping** series whose lines essentially coincide (within\n     `--eps`, default 2% of the y-range); the most prominent is kept.\n  2. **Top-N** by peak value (`--top`, default 8).\n  3. The legend prints **how many were dropped**, e.g.\n     `showing 8 of 30 series (5 overlapping, 17 low\u002Fhidden)`, plus the\n     aggregate-your-query tip.\n\n## Output formats\n\n`--format auto` (default) picks the best renderer for the context:\n\n- an **inline image** when `gnuplot` is installed and the terminal supports\n  images (Kitty\u002FGhostty, iTerm2);\n- a **PNG file plus a \"display this file\" instruction** when output is\n  piped\u002Fcaptured (e.g. an AI agent harness) and `gnuplot` is installed — the\n  agent then displays the file;\n- **ASCII** in a plain terminal, or whenever `gnuplot` is unavailable.\n\nForce a specific backend with `--format`:\n\n| `--format` | Needs     | Output                                                        |\n| ---------- | --------- | ------------------------------------------------------------- |\n| `auto`     | —         | inline image, else a PNG file + display instruction when piped, else ASCII |\n| `ascii`    | nothing   | Unicode braille line chart; renders in any terminal\u002Ftranscript\u002FCI log. |\n| `png`      | gnuplot   | PNG; displayed inline (Kitty\u002FiTerm2) or written to `--output`\u002Ftemp. |\n| `svg`      | gnuplot   | SVG written to `--output` or a temp file (path printed).      |\n| `sixel`    | gnuplot   | sixel stream to stdout (terminals with sixel support).        |\n\nIf an image format is requested but `gnuplot` is missing, it prints a note and\nfalls back to ASCII — it never hard-fails.\n\n## Using from an AI agent \u002F harness\n\nTerminal harnesses (e.g. pi) collapse tool output and don't repaint inline\nANSI, so ASCII charts come out monochrome and folded. **Prefer images:** render\na file and let the agent display it. With `--format auto` (the default) this is\nautomatic whenever output is piped and `gnuplot` is installed — the skill\nwrites a PNG and prints:\n\n```\nmetrics-chart: wrote image to \u002Ftmp\u002Fmetrics-chart-XXXX.png\n→ Display this file to the user with your image tool (e.g. read it); do not paste its contents.\n```\n\nThe agent should then **display that file** (its image\u002F`read` tool renders it\ninline, in colour, un-folded). To control the path, pass `--output chart.png`.\nDo not paste the PNG bytes or the ASCII into chat.\n\n## Options\n\n| Flag                | Default | Meaning                                            |\n| ------------------- | ------- | -------------------------------------------------- |\n| `--format`          | `auto`  | `auto`\u002F`ascii`\u002F`png`\u002F`svg`\u002F`sixel`.                |\n| `--tz NAME`         | local   | IANA tz for the x-axis; `UTC` for UTC.             |\n| `--top N`           | `8`     | Max series after collapse; must be >= 1.           |\n| `--eps F`           | `0.02`  | Overlap threshold as a fraction of the y-range.    |\n| `--all`             | off     | Draw every series (disable collapse + top-N).      |\n| `--width` \u002F `--height` | auto | Chart size (cells for ASCII, pixels for images).   |\n| `--title TEXT`      | —       | Chart title.                                       |\n| `--output PATH`     | —       | Write png\u002Fsvg to PATH instead of a temp file.      |\n| `--color` \u002F `--no-color` | auto | Force ANSI colour on\u002Foff (default: on when a TTY). |\n\n## Dependencies\n\n- **Default path: none.** Pure Python 3.9+ standard library. The ASCII renderer\n  always works.\n- **Optional: `gnuplot`** (one system binary) for PNG\u002FSVG\u002Fsixel. Install only if\n  you want high-fidelity images.\n\n## Why not graphviz?\n\n`graphviz` (`dot`\u002F`neato`) lays out node-edge graphs; it has no numeric\u002Ftime\naxis and cannot plot `data[i]` over time. This payload is a numeric time series\nwith no graph structure, so graphviz does not apply. `gnuplot` is the correct\nimage backend. (matplotlib is avoided on purpose: it is a heavy multi-package\npip install.)\n\n## Tests\n\n```bash\ncd scripts && python3 -m unittest test_metrics_chart -v\n```\n\nThe parser tests are pinned to the golden bytes from the metrics query\nservice's `service\u002Fquery\u002Fsrc\u002Fserver\u002Ftests.rs`.\n",{"data":38,"body":39},{"name":4,"description":6},{"type":40,"children":41},"root",[42,49,63,101,108,263,308,314,886,1102,1115,1121,1303,1309,1320,1379,1391,1543,1555,1561,1587,1597,1625,1631,1911,1917,1945,1951,1992,1998,2048,2060],{"type":43,"tag":44,"props":45,"children":46},"element","h1",{"id":4},[47],{"type":48,"value":4},"text",{"type":43,"tag":50,"props":51,"children":52},"p",{},[53,55,61],{"type":48,"value":54},"Turns a metrics query response into a ",{"type":43,"tag":56,"props":57,"children":58},"strong",{},[59],{"type":48,"value":60},"multi-series line chart",{"type":48,"value":62},".",{"type":43,"tag":50,"props":64,"children":65},{},[66,68,75,77,83,85,91,93,99],{"type":48,"value":67},"Input is exactly the body the Axiom metrics query service returns for\n",{"type":43,"tag":69,"props":70,"children":72},"code",{"className":71},[],[73],{"type":48,"value":74},"application\u002Fvnd.metrics.v3+json",{"type":48,"value":76}," — defined in the metrics query service\n(",{"type":43,"tag":69,"props":78,"children":80},{"className":79},[],[81],{"type":48,"value":82},"service\u002Fquery",{"type":48,"value":84},", ",{"type":43,"tag":69,"props":86,"children":88},{"className":87},[],[89],{"type":48,"value":90},"JsonV3QueryResponse",{"type":48,"value":92}," in ",{"type":43,"tag":69,"props":94,"children":96},{"className":95},[],[97],{"type":48,"value":98},"src\u002Fserver.rs",{"type":48,"value":100},"). 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(matplotlib is avoided on purpose: it is a heavy multi-package\npip install.)",{"type":43,"tag":102,"props":1993,"children":1995},{"id":1994},"tests",[1996],{"type":48,"value":1997},"Tests",{"type":43,"tag":109,"props":1999,"children":2001},{"className":111,"code":2000,"language":113,"meta":114,"style":114},"cd scripts && python3 -m unittest test_metrics_chart -v\n",[2002],{"type":43,"tag":69,"props":2003,"children":2004},{"__ignoreMap":114},[2005],{"type":43,"tag":120,"props":2006,"children":2007},{"class":122,"line":31},[2008,2014,2019,2024,2028,2033,2038,2043],{"type":43,"tag":120,"props":2009,"children":2011},{"style":2010},"--shiki-light:#6182B8;--shiki-default:#82AAFF;--shiki-dark:#82AAFF",[2012],{"type":48,"value":2013},"cd",{"type":43,"tag":120,"props":2015,"children":2016},{"style":142},[2017],{"type":48,"value":2018}," scripts",{"type":43,"tag":120,"props":2020,"children":2021},{"style":191},[2022],{"type":48,"value":2023}," &&",{"type":43,"tag":120,"props":2025,"children":2026},{"style":136},[2027],{"type":48,"value":214},{"type":43,"tag":120,"props":2029,"children":2030},{"style":142},[2031],{"type":48,"value":2032}," -m",{"type":43,"tag":120,"props":2034,"children":2035},{"style":142},[2036],{"type":48,"value":2037}," unittest",{"type":43,"tag":120,"props":2039,"children":2040},{"style":142},[2041],{"type":48,"value":2042}," test_metrics_chart",{"type":43,"tag":120,"props":2044,"children":2045},{"style":142},[2046],{"type":48,"value":2047}," -v\n",{"type":43,"tag":50,"props":2049,"children":2050},{},[2051,2053,2059],{"type":48,"value":2052},"The parser tests are pinned to the golden bytes from the metrics query\nservice's ",{"type":43,"tag":69,"props":2054,"children":2056},{"className":2055},[],[2057],{"type":48,"value":2058},"service\u002Fquery\u002Fsrc\u002Fserver\u002Ftests.rs",{"type":48,"value":62},{"type":43,"tag":2061,"props":2062,"children":2063},"style",{},[2064],{"type":48,"value":2065},"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":2067,"total":721},[2068,2082,2097,2107,2125,2144,2160,2173,2186,2194,2204,2217],{"slug":2069,"name":2069,"fn":2070,"description":2071,"org":2072,"tags":2073,"stars":2079,"repoUrl":2080,"updatedAt":2081},"axiom-apl","write and debug APL queries for Axiom","APL query language reference for Axiom. Provides operators, functions, patterns, and CLI usage. Auto-invoked by specialized Axiom skills when writing or debugging APL queries.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[2074,2077,2078],{"name":2075,"slug":2076,"type":16},"APL","apl",{"name":9,"slug":8,"type":16},{"name":14,"slug":15,"type":16},58,"https:\u002F\u002Fgithub.com\u002Faxiomhq\u002Fcli","2026-04-06T18:04:15.826882",{"slug":2083,"name":2083,"fn":2084,"description":2085,"org":2086,"tags":2087,"stars":2079,"repoUrl":2080,"updatedAt":2096},"detect-anomalies","detect anomalies in observability data","Detect anomalies in Axiom datasets using statistical analysis. Use when looking for unusual patterns, volume spikes, outliers, or new error types in observability data.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[2088,2089,2092,2095],{"name":9,"slug":8,"type":16},{"name":2090,"slug":2091,"type":16},"Data Analysis","data-analysis",{"name":2093,"slug":2094,"type":16},"Monitoring","monitoring",{"name":14,"slug":15,"type":16},"2026-04-06T18:04:19.681304",{"slug":2098,"name":2098,"fn":2099,"description":2100,"org":2101,"tags":2102,"stars":2079,"repoUrl":2080,"updatedAt":2106},"explore-dataset","explore Axiom dataset schema and patterns","Explore an Axiom dataset to understand its schema, fields, volume, and patterns. Use when discovering a new dataset, investigating data structure, or understanding what data is available.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[2103,2104,2105],{"name":9,"slug":8,"type":16},{"name":2090,"slug":2091,"type":16},{"name":14,"slug":15,"type":16},"2026-04-06T18:04:18.425533",{"slug":2108,"name":2108,"fn":2109,"description":2110,"org":2111,"tags":2112,"stars":2079,"repoUrl":2080,"updatedAt":2124},"find-traces","analyze OpenTelemetry distributed traces in Axiom","Analyze OpenTelemetry distributed traces from Axiom. 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},[2113,2114,2117,2120,2121],{"name":9,"slug":8,"type":16},{"name":2115,"slug":2116,"type":16},"Debugging","debugging",{"name":2118,"slug":2119,"type":16},"Distributed Tracing","distributed-tracing",{"name":14,"slug":15,"type":16},{"name":2122,"slug":2123,"type":16},"OpenTelemetry","opentelemetry","2026-04-06T18:04:17.130694",{"slug":2126,"name":2126,"fn":2127,"description":2128,"org":2129,"tags":2130,"stars":27,"repoUrl":28,"updatedAt":2143},"axiom-alerting","manage Axiom monitors and notifiers","Create and manage Axiom monitors and notifiers via the v2 public API. Use when building alerting, routing notifications, validating monitor behavior, and maintaining alert configurations end-to-end.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[2131,2134,2135,2138,2139,2140],{"name":2132,"slug":2133,"type":16},"API Development","api-development",{"name":9,"slug":8,"type":16},{"name":2136,"slug":2137,"type":16},"Messaging","messaging",{"name":2093,"slug":2094,"type":16},{"name":14,"slug":15,"type":16},{"name":2141,"slug":2142,"type":16},"Operations","operations","2026-05-11T06:13:11.543806",{"slug":2145,"name":2145,"fn":2146,"description":2147,"org":2148,"tags":2149,"stars":27,"repoUrl":28,"updatedAt":2159},"axiom-sre","investigate incidents with Axiom","Expert SRE investigator for incidents and debugging. Uses hypothesis-driven methodology and systematic triage. Can query Axiom observability when available. Use for incident response, root cause analysis, production debugging, or log investigation.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[2150,2151,2152,2155,2156],{"name":9,"slug":8,"type":16},{"name":2115,"slug":2116,"type":16},{"name":2153,"slug":2154,"type":16},"Incident Response","incident-response",{"name":14,"slug":15,"type":16},{"name":2157,"slug":2158,"type":16},"SRE","sre","2026-04-06T18:04:27.289824",{"slug":2161,"name":2161,"fn":2162,"description":2163,"org":2164,"tags":2165,"stars":27,"repoUrl":28,"updatedAt":2172},"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},[2166,2167,2168,2171],{"name":2075,"slug":2076,"type":16},{"name":9,"slug":8,"type":16},{"name":2169,"slug":2170,"type":16},"Dashboards","dashboards",{"name":14,"slug":15,"type":16},"2026-04-06T18:04:23.452912",{"slug":2174,"name":2174,"fn":2175,"description":2176,"org":2177,"tags":2178,"stars":27,"repoUrl":28,"updatedAt":2185},"controlling-costs","reduce Axiom query costs","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.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[2179,2180,2183,2184],{"name":9,"slug":8,"type":16},{"name":2181,"slug":2182,"type":16},"Cost Optimization","cost-optimization",{"name":2169,"slug":2170,"type":16},{"name":14,"slug":15,"type":16},"2026-04-06T18:04:22.202025",{"slug":4,"name":4,"fn":5,"description":6,"org":2187,"tags":2188,"stars":27,"repoUrl":28,"updatedAt":29},{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[2189,2190,2191,2192,2193],{"name":9,"slug":8,"type":16},{"name":22,"slug":23,"type":16},{"name":18,"slug":19,"type":16},{"name":25,"slug":26,"type":16},{"name":14,"slug":15,"type":16},{"slug":2195,"name":2195,"fn":2196,"description":2197,"org":2198,"tags":2199,"stars":27,"repoUrl":28,"updatedAt":2203},"query-metrics","query Axiom MetricsDB","Runs metrics queries against Axiom MetricsDB via scripts. Discovers available metrics, tags, and tag values. 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},[2200,2201,2202],{"name":9,"slug":8,"type":16},{"name":25,"slug":26,"type":16},{"name":14,"slug":15,"type":16},"2026-07-18T05:12:32.381376",{"slug":2205,"name":2205,"fn":2206,"description":2207,"org":2208,"tags":2209,"stars":27,"repoUrl":28,"updatedAt":2216},"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},[2210,2211,2212,2215],{"name":2075,"slug":2076,"type":16},{"name":9,"slug":8,"type":16},{"name":2213,"slug":2214,"type":16},"Migration","migration",{"name":14,"slug":15,"type":16},"2026-04-06T18:04:20.939952",{"slug":2218,"name":2218,"fn":2219,"description":2220,"org":2221,"tags":2222,"stars":27,"repoUrl":28,"updatedAt":2230},"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. Use when creating evals, writing scorers, setting up flag schemas, or configuring axiom.config.ts.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[2223,2226,2227],{"name":2224,"slug":2225,"type":16},"AI Infrastructure","ai-infrastructure",{"name":9,"slug":8,"type":16},{"name":2228,"slug":2229,"type":16},"Evals","evals","2026-04-06T18:04:26.007097",{"items":2232,"total":239},[2233,2242,2250,2257,2264,2272,2278],{"slug":2126,"name":2126,"fn":2127,"description":2128,"org":2234,"tags":2235,"stars":27,"repoUrl":28,"updatedAt":2143},{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[2236,2237,2238,2239,2240,2241],{"name":2132,"slug":2133,"type":16},{"name":9,"slug":8,"type":16},{"name":2136,"slug":2137,"type":16},{"name":2093,"slug":2094,"type":16},{"name":14,"slug":15,"type":16},{"name":2141,"slug":2142,"type":16},{"slug":2145,"name":2145,"fn":2146,"description":2147,"org":2243,"tags":2244,"stars":27,"repoUrl":28,"updatedAt":2159},{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[2245,2246,2247,2248,2249],{"name":9,"slug":8,"type":16},{"name":2115,"slug":2116,"type":16},{"name":2153,"slug":2154,"type":16},{"name":14,"slug":15,"type":16},{"name":2157,"slug":2158,"type":16},{"slug":2161,"name":2161,"fn":2162,"description":2163,"org":2251,"tags":2252,"stars":27,"repoUrl":28,"updatedAt":2172},{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[2253,2254,2255,2256],{"name":2075,"slug":2076,"type":16},{"name":9,"slug":8,"type":16},{"name":2169,"slug":2170,"type":16},{"name":14,"slug":15,"type":16},{"slug":2174,"name":2174,"fn":2175,"description":2176,"org":2258,"tags":2259,"stars":27,"repoUrl":28,"updatedAt":2185},{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[2260,2261,2262,2263],{"name":9,"slug":8,"type":16},{"name":2181,"slug":2182,"type":16},{"name":2169,"slug":2170,"type":16},{"name":14,"slug":15,"type":16},{"slug":4,"name":4,"fn":5,"description":6,"org":2265,"tags":2266,"stars":27,"repoUrl":28,"updatedAt":29},{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[2267,2268,2269,2270,2271],{"name":9,"slug":8,"type":16},{"name":22,"slug":23,"type":16},{"name":18,"slug":19,"type":16},{"name":25,"slug":26,"type":16},{"name":14,"slug":15,"type":16},{"slug":2195,"name":2195,"fn":2196,"description":2197,"org":2273,"tags":2274,"stars":27,"repoUrl":28,"updatedAt":2203},{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[2275,2276,2277],{"name":9,"slug":8,"type":16},{"name":25,"slug":26,"type":16},{"name":14,"slug":15,"type":16},{"slug":2205,"name":2205,"fn":2206,"description":2207,"org":2279,"tags":2280,"stars":27,"repoUrl":28,"updatedAt":2216},{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[2281,2282,2283,2284],{"name":2075,"slug":2076,"type":16},{"name":9,"slug":8,"type":16},{"name":2213,"slug":2214,"type":16},{"name":14,"slug":15,"type":16}]