Developing Funboost Mixin
ydf0509/funboost
当需要为 funboost 创建 Consumer 或 Publisher 的 Mixin 扩展类时使用。触发场景:添加监控、熔断、限流、链路追踪等横切关注点,编写自定义前置/后置处理钩子。关键词:mixin, consumeroverridecls, publisheroverridecls, ConsumerMixin, 自定义消费者, hook, 拦截器, 熔断器, 监控…
Find out what a running Mendix app actually does — logs, Prometheus metrics, OpenTelemetry traces and the model catalog, joined across sources.
$ npx skills add mendixlabs/mxcli --skill analyze-runtime -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install mendixlabs/mxcli analyze-runtime --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/mendixlabs/mxcli.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.claude/skills/mendix/analyze-runtime .claude/skills/analyze-runtime && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "analyze-runtime" agent skill from https://github.com/mendixlabs/mxcli/tree/main/.claude/skills/mendix/analyze-runtime into .claude/skills/analyze-runtime/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "analyze-runtime", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/mendixlabs/mxcli/tree/main/.claude/skills/mendix/analyze-runtimeType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add mendixlabs/mxcli --skill analyze-runtime -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install mendixlabs/mxcli analyze-runtime --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/mendixlabs/mxcli.git skills-src && mkdir -p .agents/skills && cp -r skills-src/.claude/skills/mendix/analyze-runtime .agents/skills/analyze-runtime && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "analyze-runtime" agent skill from https://github.com/mendixlabs/mxcli/tree/main/.claude/skills/mendix/analyze-runtime into .agents/skills/analyze-runtime/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "analyze-runtime", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add mendixlabs/mxcli --skill analyze-runtime -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install mendixlabs/mxcli analyze-runtime --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/mendixlabs/mxcli.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/.claude/skills/mendix/analyze-runtime .cursor/skills/analyze-runtime && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "analyze-runtime" agent skill from https://github.com/mendixlabs/mxcli/tree/main/.claude/skills/mendix/analyze-runtime into .cursor/skills/analyze-runtime/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "analyze-runtime", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/mendixlabs/mxcli.git --path .claude/skills/mendix/analyze-runtime--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add mendixlabs/mxcli --skill analyze-runtime -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install mendixlabs/mxcli analyze-runtime --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/mendixlabs/mxcli.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/.claude/skills/mendix/analyze-runtime .gemini/skills/analyze-runtime && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "analyze-runtime" agent skill from https://github.com/mendixlabs/mxcli/tree/main/.claude/skills/mendix/analyze-runtime into .gemini/skills/analyze-runtime/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "analyze-runtime", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install mendixlabs/mxcli analyze-runtimeInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add mendixlabs/mxcli --skill analyze-runtime -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/mendixlabs/mxcli.git skills-src && mkdir -p .github/skills && cp -r skills-src/.claude/skills/mendix/analyze-runtime .github/skills/analyze-runtime && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "analyze-runtime" agent skill from https://github.com/mendixlabs/mxcli/tree/main/.claude/skills/mendix/analyze-runtime into .github/skills/analyze-runtime/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "analyze-runtime", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add mendixlabs/mxcli --skill analyze-runtime -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install mendixlabs/mxcli analyze-runtime --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/mendixlabs/mxcli.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/.claude/skills/mendix/analyze-runtime .opencode/skills/analyze-runtime && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "analyze-runtime" agent skill from https://github.com/mendixlabs/mxcli/tree/main/.claude/skills/mendix/analyze-runtime into .opencode/skills/analyze-runtime/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "analyze-runtime", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
analyze-runtimeFind out what a running Mendix app actually does — logs, Prometheus metrics, OpenTelemetry traces and the model catalog, joined across sources.
Analyze Runtime is an agent skill from mendixlabs/mxcli. Find out what a running Mendix app actually does — logs, Prometheus metrics, OpenTelemetry traces and the model catalog, joined across sources. Use when profiling a slow page or microflow, finding what hits the database, chasing an error that shows only a generic dialog, or correlating runtime cost with model shape.
Its SKILL.md is about 2.8k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.
It sits in DevOps & Cloud, covering Monitoring and alerting and Observability. It works with OpenTelemetry and Prometheus. The repository describes itself as: Mendix cli tool, a headless way to work with Mendix projects. Enables Mendix projects for use with 3rd party agentic coding tools like Claude Code and Copilot. Includes a… The licence is Apache-2.0.
5 steps, taken from the step headings in SKILL.md.
Read from SKILL.md and the folder at commit a924d11. It shows what the files ask for, not the result of running them.
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Shell commands in SKILL.md call:
curlFrom the folder's file list and the shell code blocks in SKILL.md.
No URLs in SKILL.md. Its commands use curl, which can reach the network depending on how they are called.
From URLs in SKILL.md, links to its own repository left out.
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Analyze Runtime loads about 2.8k tokens when it runs. Until then it costs about 83 tokens; SKILL.md has 1,268 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.
The full file from mendixlabs/mxcli at commit a924d11, republished under its Apache-2.0 licence (© mendixlabs). 1,268 words, ~2,841 tokens.
.claude/skills/analyze-runtime/SKILL.md (or your agent's skills folder).When you need to understand what an app actually does at runtime — why a page is slow, which microflow dominates cost, what hits the database, whether an error is network or logic — the signals live in four places. This skill is the procedure for collecting them and, crucially, joining them, because the useful questions cross sources that no single tool answers alone.
| Signal | Where | How you get it |
|---|---|---|
Logs (server stack traces + your LOG output) | <projectDir>/.mxcli/runtime.log | mxcli run --local tees it automatically |
| Metrics (throughput, DB counts, sessions, queues) | /prometheus on the admin port | mxcli run --local --metrics |
| Traces (per-microflow / per-activity spans + timings) | console→runtime.log, or an OTLP collector | mxcli run --local --trace / --trace-otlp |
| Model shape (activities, complexity, refs, XPath) | .mxcli/catalog.db (SQLite) | mxcli … "refresh catalog full" then SELECT … FROM CATALOG.* |
Prerequisite: run the app with the fast local loop — see run-local. Everything
below assumes mxcli run --local (add the flags noted per signal).
run --local writes the runtime log to <projectDir>/.mxcli/runtime.log (override
--runtime-log, - disables). It carries JVM stdout/stderr and the application
log — server stack traces, your microflow/nanoflow LOG output, and the DB
synchronization counts at startup.
mxcli run --local -p app.mpr
tail -f .mxcli/runtime.logGotchas:
LOG lands under the Client_Nanoflow node, not the node name you
declared — a filter built around microflow node names silently drops it. LOG DEBUG
from a nanoflow is dropped server-side (browser console only). See write-nanoflows.create or modify data-loss class of bug).mxcli logEverything logs at INFO by default, so the detail you need usually is not in the
file at all — and raising the whole runtime to TRACE is unusable. Logging is
publish/subscribe: code publishes to a named LogNode, and each node has its
own level.
mxcli log list # every node and its level (57 on a blank 11.12 app)
mxcli log list --filter connectionbus # narrow it — nobody remembers the exact names
mxcli log set ConnectionBus_Queries TRACE
mxcli log set ConnectionBus_Queries=TRACE Connector=DEBUG # one admin call
mxcli log set ConnectionBus_Queries INFO # put it backLevels: NONE CRITICAL ERROR WARNING INFO DEBUG TRACE.
This needs a running app (it goes through the M2EE admin API), and the change lasts as long as the process — it is a debugging knob, not project configuration.
Nodes worth knowing:
| Question | Node |
|---|---|
| What SQL is being run | ConnectionBus_Queries (and _Retrieve, _Update) |
| Database sync at startup | ConnectionBus_Synchronize |
| Consumed OData / REST calls | ODataConsume, REST Consume |
| Published OData requests (the incoming URI) | OData Publish — note the space; only exists if the project publishes a service |
| Microflow execution | MicroflowEngine, ActionManager |
| Scheduled events / queues | SystemTask, TaskQueue |
| Java/JS action wiring | Connector |
--force creates the node, permanently. Without it an unknown node is refused,
which is what you want — a typo should be an error. With it the name is registered
for the life of the process, so a typo becomes a real (empty) node that shows up in
log list from then on. Use it only to pre-register a node that has not published
yet.
Nodes appear only once something registers them, so the list is a property of
this app, not of Mendix. A blank 11.12 app reports 57; adding one published
OData service makes it 58. This is why log list is the first step rather than a
remembered name — and why --force exists for a node that has not registered yet.
OData Publish — note the space — is the node, and it exists only when the
project publishes a service. At TRACE it logs the full incoming URI, which is the
question $filter/$top/key-lookup bugs turn on:
mxcli log set "OData Publish" TRACE
# GET /odata/f1/Rows?$top=5&$filter=rowKey eq 'abc'TRACE - OData Publish: Incoming request from 127.0.0.1: GET .../Rows?$top=5&$filter=rowKey eq 'abc'
DEBUG - OData Publish: Responding to client with status code 400.ODataConsume is the client side — a different node for a different direction.
That same probe showed Mendix rejecting $filter on a property not declared
Filterable, with a 400 "Property 'rowKey' is non-filterable", before the read
microflow ran. So the platform does enforce the filterability you declare in
expose (…); what it does not do is apply $top/$skip/$orderby for a
read-microflow resource (see odata-data-sharing).
--metrics registers a Prometheus registry, served at
http://127.0.0.1:<admin-port>/prometheus (loopback).
mxcli run --local -p app.mpr --metrics
curl -s http://127.0.0.1:8090/prometheus | grep -E 'connectionbus_|handler_requests|sessions_|taskqueue_'Useful families: connectionbus_{selects,inserts,updates,deletes,transactions}_total
(database pressure), handler_requests_total (throughput), sessions_*,
taskqueue_* (background work). Merge any extra registry (otlp/influx/statsd) with
--runtime-setting 'Metrics.Registries=[…]'.
--trace attaches the bundled OpenTelemetry agent. Default span filters ship with
it (OpenTelemetry._RuntimeSpanFilters) because unfiltered per-activity tracing is
~10× slower and produces ~110k spans for one busy transaction — that's a
flow-shape debugging mode, not a timing mode.
# console exporter → runtime.log (span names/attrs only)
mxcli run --local -p app.mpr --trace
# flame charts: export to a collector (console can't reconstruct call trees/durations)
mxcli run --local -p app.mpr --trace-otlp http://127.0.0.1:4318--trace-otlp <endpoint> (implies --trace), which sets the OTLP
exporter for you; user-set OTEL_* env still wins.--trace-service NAME sets OTEL_SERVICE_NAME (default the .mpr name); use
distinct names per app for multi-app correlation. Trace context (W3C traceparent)
crosses app boundaries automatically over rest call.--runtime-setting 'OpenTelemetry._RuntimeSpanFilters=[]'.The catalog is a SQLite database at .mxcli/catalog.db describing the model. Run
refresh catalog full — plain refresh catalog (fast mode) leaves the analytic
tables (CATALOG.ACTIVITIES, CATALOG.REFS, CATALOG.XPATH_EXPRESSIONS,
CATALOG.WIDGETS) empty (a fast-mode query warns "requires refresh catalog full").
mxcli -p app.mpr -c "refresh catalog full"
mxcli -p app.mpr -c "SELECT MicroflowQualifiedName, COUNT(*) activities
FROM CATALOG.ACTIVITIES GROUP BY 1 ORDER BY 2 DESC LIMIT 10"CATALOG.ACTIVITIES.Id is the model GUID the debugger breaks on (see
debug-microflows), with the action name and its sequence — a named, ordered
activity list per microflow. See catalog-search / graph-analysis for the
richer queries.
Each signal alone answers little; the useful questions cross them. Because the catalog is a plain database and the app's dev data is Postgres, one engine can join model shape + live data + telemetry with no ETL. mxcli does not embed DuckDB — this is a dev-container recipe (dev data, dev telemetry, everything read-only):
-- in duckdb, from the project dir, after `refresh catalog full` + a --trace-otlp run
ATTACH '.mxcli/catalog.db' AS cat (TYPE sqlite, READ_ONLY);
ATTACH 'dbname=app host=127.0.0.1 user=mendix' AS app (TYPE postgres, READ_ONLY);
CREATE VIEW spans AS SELECT * FROM read_json_auto('spans.jsonl');Two joins that are impossible in any single source:
cat.activities_data (count) and complexity. Complexity does not predict cost: a
2-activity flow that delegates can cost more than a 14-activity one. A lint rule
can't see that; this join can.cat (which entity)
and app (row counts). This is how you find that the task-queue poller
(system$queuedtask, owned by no microflow) is the app's largest DB consumer —
invisible from any per-microflow view.Caveats: keep every attachment read-only (never a production DB; even locally make it explicit), and filter/sample traces first — unfiltered span volume is large.
Runtime log lines carry no trace id, so joining logs to traces is a fuzzy
timestamp join (worst exactly under concurrency). The OTel agent populates the trace
id in the MDC, but Mendix's log pattern doesn't print it — closing the file-log side
needs an upstream %X{trace_id} change, not mxcli. For collector-side correlation,
export traces (and logs) via OTLP with --trace-otlp so the backend joins them.
| Question | Reach for |
|---|---|
| "Why did this error?" (server-side) | Logs (runtime.log) |
| "How much DB / throughput / queue work?" | Metrics (--metrics) |
| "Where does the time go in this flow?" | Traces (--trace-otlp for a flame chart) |
| "What's the flow's shape / activity list?" | Catalog (refresh catalog full) or the debugger |
| "Which cost belongs to which entity/model construct?" | Warehouse (catalog × spans × app DB) |
run-local — the local loop these flags hang off (--metrics / --trace / --trace-otlp / --runtime-setting reference).debug-microflows — interactive breakpoints/stepping when a trace isn't enough.catalog-search, graph-analysis — catalog query patterns and dependency analysis.verify-with-oql — query the running app's data directly.© mendixlabs, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Just SKILL.md in .claude/skills/mendix/analyze-runtime of mendixlabs/mxcli.
Open the folder on GitHubat commit a924d11
Analyze Runtime next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Analyze Runtime this skillmendixlabs/mxcli | 128 | — | ~2.8k | Automated safety check: Pass | Apache-2.0 | |
| Developing Funboost Mixinydf0509/funboost | 892 | — | ~2.1k | Automated safety check: Pass | None | |
| Archestra Dev Observabilityarchestra-ai/archestra | 4.3k | — | ~1.2k | Automated safety check: Pass | Custom licence | |
| Frontmcp Observabilityagentfront/frontmcp | 146 | — | ~4.6k | Automated safety check: Pass | Apache-2.0 | |
| Monitoring Observabilityahmedasmar/devops-claude-skills | 203 | — | ~3.9k | Automated safety check: Pass | None | |
| Observability Architecturemajiayu000/litellm-rs | 116 | — | ~1.3k | Automated safety check: Pass | MIT |
ydf0509/funboost
当需要为 funboost 创建 Consumer 或 Publisher 的 Mixin 扩展类时使用。触发场景:添加监控、熔断、限流、链路追踪等横切关注点,编写自定义前置/后置处理钩子。关键词:mixin, consumeroverridecls, publisheroverridecls, ConsumerMixin, 自定义消费者, hook, 拦截器, 熔断器, 监控…
archestra-ai/archestra
A skill your agent uses when changing Archestra tracing, metrics, OpenTelemetry, Tempo, Grafana, Prometheus, LLM/MCP spans, observability labels, or local observability setup.
agentfront/frontmcp
A skill your agent uses when adding tracing, structured logging, metrics, or monitoring to a FrontMCP server.
ahmedasmar/devops-claude-skills
Monitoring and observability strategy, implementation, and troubleshooting.
majiayu000/litellm-rs
LiteLLM-RS Observability Architecture. An agent skill from majiayu000/litellm-rs.
Jeffallan/claude-skills
Sets up application monitoring: structured logs, Prometheus metrics, OpenTelemetry tracing, Grafana dashboards, alert rules and load tests with k6 or Artillery.
mendixlabs/mxcli
Push OData query options into the SQL of a Mendix resource served by a read microflow, so $filter, $orderby, $top, $skip, $count and the key lookup reach the database instead of being silently…
mendixlabs/mxcli
Chart a Mendix app with Vega-Lite through a pluggable widget that takes the specification and the data as separate properties, so the model emits rows and never assembles a chart payload.
mendixlabs/mxcli
Author Mendix AI agent documents in MDL — Model, Knowledge Base, Consumed MCP Service and Agent, with variables, tools and multi-line prompts.
mendixlabs/mxcli
Run set-based INSERT, UPDATE and DELETE against Mendix entities through OQL statements, which the runtime supports and Studio Pro cannot author.
mendixlabs/mxcli
Stand up an HTTP endpoint you control instead of a live third-party API, and point the Mendix app at it — Prism from an OpenAPI contract, a constant swap, or a forward proxy.
mendixlabs/mxcli
Call external REST APIs from Mendix — the three approaches (inline REST CALL, consumed REST client document, generated from OpenAPI) and how to choose.
Works with
Categories
Find out what a running Mendix app actually does — logs, Prometheus metrics, OpenTelemetry traces and the model catalog, joined across sources. Analyze Runtime is an agent skill from mendixlabs/mxcli. Find out what a running Mendix app actually does — logs, Prometheus metrics, OpenTelemetry traces and the model catalog, joined across sources.
Analyze Runtime fits situations like: profiling a slow page; finding what hits the database; chasing an error that shows only a generic dialog; correlating runtime cost with model shape.
Run `npx skills add mendixlabs/mxcli --skill analyze-runtime -a claude-code`. Or copy the skill folder (.claude/skills/mendix/analyze-runtime in mendixlabs/mxcli) into .claude/skills/analyze-runtime in your project. Claude Code loads it when a task matches its description.
Run `npx skills add mendixlabs/mxcli --skill analyze-runtime -a codex`. Or copy the skill folder (.claude/skills/mendix/analyze-runtime in mendixlabs/mxcli) into .agents/skills/analyze-runtime in your project. Codex loads it when a task matches its description.
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add mendixlabs/mxcli --skill analyze-runtime -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/analyze-runtime, .gemini/skills/analyze-runtime, .github/skills/analyze-runtime and .opencode/skills/analyze-runtime in your project.
Going by SKILL.md and its folder, Analyze Runtime needs the command-line tools its instructions call (curl).
SKILL.md contains no URLs. Its commands use curl, which can reach the network depending on how they are called. This is read from the text; nothing was executed.
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.
Analyze Runtime is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
About 2.8k tokens (SKILL.md is roughly 11k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
Skills that share tags, products or a category with Analyze Runtime: Developing Funboost Mixin (ydf0509/funboost, 892 stars), Archestra Dev Observability (archestra-ai/archestra, 4.3k stars), Frontmcp Observability (agentfront/frontmcp, 146 stars) and Monitoring Observability (ahmedasmar/devops-claude-skills, 203 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
mendixlabs (a GitHub organization) maintains it in mendixlabs/mxcli, which has 128 GitHub stars. The repository holds 75 skills in this directory. The repository was last updated on October 7, 2026.
Source: mendixlabs/mxcli on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.