UModel Root Cause Analysis
alibaba/UnifiedModel
Investigates a service incident to its root cause by querying a UModel object graph alongside metrics, logs, topology and recent deployments.
A skill your agent uses for MLflow GenAI observability work: tracing, trace search/export, OpenTelemetry, prompts, GenAI datasets/evaluation, scorers/judges, review queues, assessments, and provider…
Install Genai Observability
$ npx skills add VectorSpaceLab/AREX-Skill --skill genai-observability -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install VectorSpaceLab/AREX-Skill genai-observability --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/VectorSpaceLab/AREX-Skill.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/repositories/repo-skills/mlflow/sub-skills/genai-observability .claude/skills/genai-observability && 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 "genai-observability" agent skill from https://github.com/VectorSpaceLab/AREX-Skill/tree/main/skills/repositories/repo-skills/mlflow/sub-skills/genai-observability into .claude/skills/genai-observability/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "genai-observability", 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/VectorSpaceLab/AREX-Skill/tree/main/skills/repositories/repo-skills/mlflow/sub-skills/genai-observabilityType 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 VectorSpaceLab/AREX-Skill --skill genai-observability -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install VectorSpaceLab/AREX-Skill genai-observability --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/VectorSpaceLab/AREX-Skill.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/repositories/repo-skills/mlflow/sub-skills/genai-observability .agents/skills/genai-observability && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "genai-observability" agent skill from https://github.com/VectorSpaceLab/AREX-Skill/tree/main/skills/repositories/repo-skills/mlflow/sub-skills/genai-observability into .agents/skills/genai-observability/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "genai-observability", 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 VectorSpaceLab/AREX-Skill --skill genai-observability -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install VectorSpaceLab/AREX-Skill genai-observability --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/VectorSpaceLab/AREX-Skill.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/repositories/repo-skills/mlflow/sub-skills/genai-observability .cursor/skills/genai-observability && 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 "genai-observability" agent skill from https://github.com/VectorSpaceLab/AREX-Skill/tree/main/skills/repositories/repo-skills/mlflow/sub-skills/genai-observability into .cursor/skills/genai-observability/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "genai-observability", 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/VectorSpaceLab/AREX-Skill.git --path skills/repositories/repo-skills/mlflow/sub-skills/genai-observability--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 VectorSpaceLab/AREX-Skill --skill genai-observability -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install VectorSpaceLab/AREX-Skill genai-observability --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/VectorSpaceLab/AREX-Skill.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/repositories/repo-skills/mlflow/sub-skills/genai-observability .gemini/skills/genai-observability && 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 "genai-observability" agent skill from https://github.com/VectorSpaceLab/AREX-Skill/tree/main/skills/repositories/repo-skills/mlflow/sub-skills/genai-observability into .gemini/skills/genai-observability/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "genai-observability", 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 VectorSpaceLab/AREX-Skill genai-observabilityInstalls 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 VectorSpaceLab/AREX-Skill --skill genai-observability -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/VectorSpaceLab/AREX-Skill.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/repositories/repo-skills/mlflow/sub-skills/genai-observability .github/skills/genai-observability && 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 "genai-observability" agent skill from https://github.com/VectorSpaceLab/AREX-Skill/tree/main/skills/repositories/repo-skills/mlflow/sub-skills/genai-observability into .github/skills/genai-observability/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "genai-observability", 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 VectorSpaceLab/AREX-Skill --skill genai-observability -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install VectorSpaceLab/AREX-Skill genai-observability --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/VectorSpaceLab/AREX-Skill.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/repositories/repo-skills/mlflow/sub-skills/genai-observability .opencode/skills/genai-observability && 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 "genai-observability" agent skill from https://github.com/VectorSpaceLab/AREX-Skill/tree/main/skills/repositories/repo-skills/mlflow/sub-skills/genai-observability into .opencode/skills/genai-observability/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "genai-observability", 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.
Facts
- Skill name
genai-observability- GitHub stars
- 330
- Token cost
- ~726 tokens
- SKILL.md length
- 256 words
- Files
- 6 (incl. scripts, references)
- Skills in repo
- 159
- Repo updated
- First seen
- Licence
- Apache-2.0
At a glance
A skill your agent uses for MLflow GenAI observability work: tracing, trace search/export, OpenTelemetry, prompts, GenAI datasets/evaluation, scorers/judges, review queues, assessments, and provider…
- Works in 6 steps: Set a tracking URI/experiment… → Instrument deterministic code with… → Retrieve traces with… → …
- MLflow GenAI observability work: tracing
- SKILL.md covers Route First, Safe Workflow and Bundled Probe
- Runs Python scripts from its folder; calls python
What it does
Genai Observability is an agent skill from VectorSpaceLab/AREX-Skill. Use for MLflow GenAI observability work: tracing, trace search/export, OpenTelemetry, prompts, GenAI datasets/evaluation, scorers/judges, review queues, assessments, and provider autologging. Routes classic experiment/run logging to tracking-and-registry, model flavor packaging to models-and-flavors, and deployment/server/MCP commands to serving-and-projects.
Its SKILL.md is about 730 tokens, which your agent loads only when the skill is triggered. The skill folder holds 7 other files, including scripts and reference files (for example `references/genai-evaluation.md`, `references/prompts-and-datasets.md` and `references/tracing.md`).
It sits in DevOps & Cloud, covering Observability. It works with MLflow, Model Context Protocol and OpenTelemetry. The repository describes itself as: A Skill Library for Automated Machine Learning. The licence is Apache-2.0.
When your agent uses it
- MLflow GenAI observability work: tracing
- Trace search/export
- GenAI datasets/evaluation
- Provider autologging
Example prompts
- “/genai-observability”
Requirements
- Python 3
Workflow steps
6 steps, taken from the first numbered list in SKILL.md.
- Set a tracking URI/experiment deliberately before generating traces or prompts.
- Instrument deterministic code with manual tracing first; add provider autologging only after extras, credentials, and network access are…
- Retrieve traces with mlflow.get_last_active_trace_id(), mlflow.get_trace(..., flush=True), or mlflow.search_traces(...) before wiring…
- Use datasets/prompts as versioned inputs to evaluation; pin prompt aliases or versions explicitly.
- Add feedback/expectations or custom scorers to make evaluation outcomes inspectable and reproducible.
- For async/provider traces, flush async logging or wait for export before assertions.
What it can do on your machine
Read from SKILL.md and the folder at commit ac3fe1a. 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
Ships 1 file in scripts/ (Python), which the agent can run.
Shell commands in SKILL.md call:
python
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md.
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
Genai Observability loads about 726 tokens when it runs, and up to ~4.2k if it reads all its reference files. Until then it costs about 95 tokens; SKILL.md has 256 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.
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); the scripts in this folder are not scanned.
SKILL.md
The full file from VectorSpaceLab/AREX-Skill at commit ac3fe1a, republished under its Apache-2.0 licence (© VectorSpaceLab). 256 words, ~726 tokens.
Download SKILL.mdSave it as
.claude/skills/genai-observability/SKILL.md (or your agent's skills folder). This skill also uses 5 other files; get the full folder from GitHub.- name
- genai-observability
- description
- Use for MLflow GenAI observability work: tracing, trace search/export, OpenTelemetry, prompts, GenAI datasets/evaluation, scorers/judges, review queues, assessments, and provider autologging. Routes classic experiment/run logging to tracking-and-registry, model flavor packaging to models-and-flavors, and deployment/server/MCP commands to serving-and-projects.
- disable-model-invocation
- true
- metadata.disco-role
- operating
- license
- Apache 2.0
GenAI Observability
Use this sub-skill when the task involves MLflow traces, GenAI evaluation, prompts, datasets, feedback, expectations, labeling/review, or provider tracing integrations.
Route First
- For local app instrumentation, use
@mlflow.trace,mlflow.start_span,mlflow.get_trace,mlflow.search_traces, andmlflow.MlflowClienttrace methods; seereferences/tracing.md. - For evaluation, use
mlflow.genai.evaluate,@mlflow.genai.scorers.scorer, built-in scorers,mlflow.genai.make_judge, and trace/dataset-backed evaluation; seereferences/genai-evaluation.md. - For prompt and dataset lifecycle, use
mlflow.genai.register_prompt,load_prompt, aliases/tags/model config, andcreate_dataset/search_datasets; seereferences/prompts-and-datasets.md. - For OpenAI, Anthropic, Bedrock, Gemini, LangChain, LlamaIndex, and DSPy tracing, prefer provider autologging only when package extras and credentials are installed; keep offline tests on manual tracing.
- For review queues, labeling sessions, feedback, expectations, and assessments, distinguish local tracking-store support from Databricks-only review app features.
- For deployment, auth, AI Gateway, MCP, agent server, and serving commands, route to
serving-and-projects; for classic run metrics/artifacts/model registry, route totracking-and-registry.
Safe Workflow
- Set a tracking URI/experiment deliberately before generating traces or prompts.
- Instrument deterministic code with manual tracing first; add provider autologging only after extras, credentials, and network access are confirmed.
- Retrieve traces with
mlflow.get_last_active_trace_id(),mlflow.get_trace(..., flush=True), ormlflow.search_traces(...)before wiring evaluation. - Use datasets/prompts as versioned inputs to evaluation; pin prompt aliases or versions explicitly.
- Add feedback/expectations or custom scorers to make evaluation outcomes inspectable and reproducible.
- For async/provider traces, flush async logging or wait for export before assertions.
Bundled Probe
Run the local smoke probe when validating basic tracing without credentials:
python skills/mlflow/sub-skills/genai-observability/scripts/tracing_smoke.pyThe script uses a temporary local tracking store, creates nested spans, searches the resulting trace, and emits JSON with the trace id and span count.
© VectorSpaceLab, 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
SKILL.md and 5 other files (scripts, references) in skills/repositories/repo-skills/mlflow/sub-skills/genai-observability of VectorSpaceLab/AREX-Skill.
- SKILL.md
- references/genai-evaluation.md
- references/prompts-and-datasets.md
- references/tracing.md
- references/troubleshooting.md
- scripts/tracing_smoke.py
Open the folder on GitHubat commit ac3fe1a
Compare with similar skills
Genai Observability 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 |
|---|---|---|---|---|---|---|
| Genai Observability this skillVectorSpaceLab/AREX-Skill | 330 | — | ~726 | Automated safety check: Pass | Apache-2.0 | |
| UModel Root Cause Analysisalibaba/UnifiedModel | 415 | — | ~1.9k | Automated safety check: Pass | Custom licence | |
| Agentmeasureroy-tong/AgentMeasure | 218 | — | ~753 | Automated safety check: Pass | MIT | |
| Exploring Apm TracesPostHog/posthog | 40k | — | ~3.5k | Automated safety check: Pass | Custom licence | |
| Archestra Dev Observabilityarchestra-ai/archestra | 4.4k | — | ~1.2k | Automated safety check: Pass | Custom licence | |
| Frontmcp Observabilityagentfront/frontmcp | 146 | — | ~4.6k | Automated safety check: Pass | Apache-2.0 |
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API Telemetry
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Related
Categories
Questions about Genai Observability
What does Genai Observability do?
A skill your agent uses for MLflow GenAI observability work: tracing, trace search/export, OpenTelemetry, prompts, GenAI datasets/evaluation, scorers/judges, review queues, assessments, and provider…. Genai Observability is an agent skill from VectorSpaceLab/AREX-Skill. Use for MLflow GenAI observability work: tracing, trace search/export, OpenTelemetry, prompts, GenAI datasets/evaluation, scorers/judges, review queues, assessments, and provider autologging.
When should I use Genai Observability?
Genai Observability fits situations like: MLflow GenAI observability work: tracing; trace search/export; genAI datasets/evaluation; provider autologging.
How do I install Genai Observability in Claude Code?
Run `npx skills add VectorSpaceLab/AREX-Skill --skill genai-observability -a claude-code`. Or copy the skill folder (skills/repositories/repo-skills/mlflow/sub-skills/genai-observability in VectorSpaceLab/AREX-Skill) into .claude/skills/genai-observability in your project. Claude Code loads it when a task matches its description.
How do I install Genai Observability in Codex?
Run `npx skills add VectorSpaceLab/AREX-Skill --skill genai-observability -a codex`. Or copy the skill folder (skills/repositories/repo-skills/mlflow/sub-skills/genai-observability in VectorSpaceLab/AREX-Skill) into .agents/skills/genai-observability in your project. Codex loads it when a task matches its description.
Can I use Genai Observability 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 VectorSpaceLab/AREX-Skill --skill genai-observability -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/genai-observability, .gemini/skills/genai-observability, .github/skills/genai-observability and .opencode/skills/genai-observability in your project.
What does Genai Observability need to run?
Going by SKILL.md and its folder, Genai Observability needs Python for the scripts in its folder and the command-line tools its instructions call (python). Our summary lists: Python 3.
Does Genai Observability access the network?
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.
Is Genai Observability 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. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.
What licence does Genai Observability use?
Genai Observability is published under the Apache-2.0 licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Genai Observability use?
About 726 tokens (SKILL.md is roughly 2.9k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 3.5k tokens, read only when the agent opens those files.
What are the alternatives to Genai Observability?
Skills that share tags, products or a category with Genai Observability: UModel Root Cause Analysis (alibaba/UnifiedModel, 415 stars), Agentmeasure (roy-tong/AgentMeasure, 218 stars), Exploring Apm Traces (PostHog/posthog, 40k stars) and Archestra Dev Observability (archestra-ai/archestra, 4.4k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Genai Observability?
VectorSpaceLab (a GitHub organization) maintains it in VectorSpaceLab/AREX-Skill, which has 330 GitHub stars. The repository holds 159 skills in this directory. The repository was last updated on September 3, 2026.
Source: VectorSpaceLab/AREX-Skill on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.