Agent skill

Analyze Runtime

by mendixlabs in mendixlabs/mxcli

Find out what a running Mendix app actually does — logs, Prometheus metrics, OpenTelemetry traces and the model catalog, joined across sources.

Apache-2.0Auto-check passedDevOps & Cloud

Install Analyze Runtime

skills CLI
$ npx skills add mendixlabs/mxcli --skill analyze-runtime -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install mendixlabs/mxcli analyze-runtime --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ 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-src

Use ~/.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/

Facts

Skill name
analyze-runtime
GitHub stars
128
Token cost
~2.8k tokens
SKILL.md length
1,268 words
Files
1
Skills in repo
75
Repo updated
First seen
Licence
Apache-2.0

At a glance

Find out what a running Mendix app actually does — logs, Prometheus metrics, OpenTelemetry traces and the model catalog, joined across sources.

  • Works in 5 steps: Logs — the first stop for errors → Metrics — throughput and database pressure → Traces — where the time goes → …
  • Profiling a slow page
  • SKILL.md covers Overview, When to Use This Skill, 1. Logs — the first stop for… and 2. Metrics — throughput and…, plus 6 more sections
  • Calls curl

What it does

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.

When your agent uses it

  • Profiling a slow page
  • Finding what hits the database
  • Chasing an error that shows only a generic dialog
  • Correlating runtime cost with model shape

Example prompts

  • “/analyze-runtime”

Workflow steps

5 steps, taken from the step headings in SKILL.md.

  1. Logs — the first stop for errors
  2. Metrics — throughput and database pressure
  3. Traces — where the time goes
  4. Model shape — the catalog
  5. The app warehouse — join the signals (external DuckDB)

What it can do on your machine

Read from SKILL.md and the folder at commit a924d11. It shows what the files ask for, not the result of running them.

  • Tool permissions

    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.

  • Runs code

    Shell commands in SKILL.md call:

    • curl

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    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.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

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.

Always · name and description, kept in context so the agent knows when to use it
~83
When it runs · the whole SKILL.md, loaded when a task matches
~2.8k

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.

Safety

Auto-check passed

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.

SKILL.md

The full file from mendixlabs/mxcli at commit a924d11, republished under its Apache-2.0 licence (© mendixlabs). 1,268 words, ~2,841 tokens.

Download SKILL.mdSave it as .claude/skills/analyze-runtime/SKILL.md (or your agent's skills folder).
name
analyze-runtime
description
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.

Analyze an App's Runtime Behavior — Logs, Metrics, Traces, Catalog

Overview

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.

SignalWhereHow you get it
Logs (server stack traces + your LOG output)<projectDir>/.mxcli/runtime.logmxcli run --local tees it automatically
Metrics (throughput, DB counts, sessions, queues)/prometheus on the admin portmxcli run --local --metrics
Traces (per-microflow / per-activity spans + timings)console→runtime.log, or an OTLP collectormxcli run --local --trace / --trace-otlp
Model shape (activities, complexity, refs, XPath).mxcli/catalog.db (SQLite)mxcli … "refresh catalog full" then SELECT … FROM CATALOG.*

When to Use This Skill

  • A page/microflow is slow and you need to find where the time goes.
  • You want to know which entities/queries the app actually hits, and how often.
  • A server-side error shows only a generic dialog in the browser.
  • You're profiling and need a flame chart, or want cost correlated with model shape.

Prerequisite: run the app with the fast local loop — see run-local. Everything below assumes mxcli run --local (add the flags noted per signal).

1. Logs — the first stop for errors

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.

bash
mxcli run --local -p app.mpr
tail -f .mxcli/runtime.log

Gotchas:

  • Nanoflow 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.
  • A spike in "Executing N database synchronization command(s)" on an unchanged model is a red flag (see the create or modify data-loss class of bug).
Turning up one subsystem: mxcli log

Everything 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.

bash
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 back

Levels: 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:

QuestionNode
What SQL is being runConnectionBus_Queries (and _Retrieve, _Update)
Database sync at startupConnectionBus_Synchronize
Consumed OData / REST callsODataConsume, REST Consume
Published OData requests (the incoming URI)OData Publish — note the space; only exists if the project publishes a service
Microflow executionMicroflowEngine, ActionManager
Scheduled events / queuesSystemTask, TaskQueue
Java/JS action wiringConnector

--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.

Seeing what a published OData resource is asked

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:

bash
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).

2. Metrics — throughput and database pressure

--metrics registers a Prometheus registry, served at http://127.0.0.1:<admin-port>/prometheus (loopback).

bash
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=[…]'.

Show full SKILL.md (543 more words)Show less

3. Traces — where the time goes

--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.

bash
# 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
  • The console exporter omits start/end timestamps and parent span IDs — you get span names + attributes but no call tree and no durations. For real timing/flame charts use --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.
  • To examine flow shape on a small flow, temporarily disable the filters with --runtime-setting 'OpenTelemetry._RuntimeSpanFilters=[]'.

4. Model shape — the catalog

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").

bash
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.

5. The app warehouse — join the signals (external DuckDB)

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):

sql
-- 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:

  • Runtime cost × model shape — span durations per microflow joined to 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.
  • Query time × entity × live rows — span DB timings joined to 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.

Known gap: logs ↔ traces

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.

Decision guide

QuestionReach 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)

See Also

  • 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

Files

Just SKILL.md in .claude/skills/mendix/analyze-runtime of mendixlabs/mxcli.

Open the folder on GitHubat commit a924d11

Compare with similar skills

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.

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Categories

Questions about Analyze Runtime

What does Analyze Runtime do?

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.

When should I use Analyze Runtime?

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.

How do I install Analyze Runtime in Claude Code?

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.

How do I install Analyze Runtime in Codex?

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.

Can I use Analyze Runtime in Cursor, Gemini CLI or GitHub Copilot?

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.

What does Analyze Runtime need to run?

Going by SKILL.md and its folder, Analyze Runtime needs the command-line tools its instructions call (curl).

Does Analyze Runtime access the network?

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.

Is Analyze Runtime safe to install?

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.

What licence does Analyze Runtime use?

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.

How many tokens does Analyze Runtime use?

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.

What are the alternatives to Analyze Runtime?

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.

Who maintains Analyze Runtime?

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.