Agent skill

Monte Carlo Asset Health

by sickn33 in sickn33/agentic-awesome-skills

Curated upstream guidance for Monte Carlo Asset Health; use when the workflow matches the user goal.

Apache-2.0Auto-check passed

Install Monte Carlo Asset Health

skills CLI
$ npx skills add sickn33/agentic-awesome-skills --skill monte-carlo-asset-health -a claude-code

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

GitHub CLI
$ gh skill install sickn33/agentic-awesome-skills monte-carlo-asset-health --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/sickn33/agentic-awesome-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/monte-carlo-asset-health .claude/skills/monte-carlo-asset-health && 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
monte-carlo-asset-health
GitHub stars
47k
Used in
1 other repo
Token cost
~2.4k tokens
SKILL.md length
788 words
Files
1
Skills in repo
1,497
Repo updated
First seen
Licence
Apache-2.0

At a glance

Curated upstream guidance for Monte Carlo Asset Health; use when the workflow matches the user goal.

  • Works in 2 steps: references/workflows.md (relative to… → references/parameters.md (relative to…
  • The workflow matches the user goal
  • SKILL.md covers When to Use, REQUIRED: Read reference files…, When to activate this skill and When NOT to activate this skill, plus 3 more sections
  • Reaches github.com

What it does

Monte Carlo Asset Health is an agent skill from sickn33/agentic-awesome-skills. Curated upstream guidance for Monte Carlo Asset Health; use when the workflow matches the user goal.

Its SKILL.md is about 2.4k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It works with Model Context Protocol. The repository describes itself as: AAS Core is the local, agent-first control plane for complete catalog discovery, agent-owned selection, stack validation, and planning, backed by 2,400+ agentic skills. Includes… The licence is Apache-2.0.

When your agent uses it

  • The workflow matches the user goal

Example prompts

  • “/monte-carlo-asset-health”

Workflow steps

2 steps, taken from the first numbered list in SKILL.md.

  1. references/workflows.md (relative to this file) — exact tool calls, phases, and execution order
  2. references/parameters.md (relative to this file) — parameter conventions and field details

What it can do on your machine

Read from SKILL.md and the folder at commit b84d35a. 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

    No scripts in the folder and no shell commands in SKILL.md.

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

  • Network

    Hosts in commands or code, which the agent is likely to contact:

    • github.com

    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

Monte Carlo Asset Health loads about 2.4k tokens when it runs. Until then it costs about 31 tokens; SKILL.md has 788 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~31
When it runs · the whole SKILL.md, loaded when a task matches
~2.4k

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 sickn33/agentic-awesome-skills at commit b84d35a, republished under its Apache-2.0 licence (© sickn33). 788 words, ~2,395 tokens.

Download SKILL.mdSave it as .claude/skills/monte-carlo-asset-health/SKILL.md (or your agent's skills folder).
name
monte-carlo-asset-health
description
Curated upstream guidance for Monte Carlo Asset Health; use when the workflow matches the user goal.
risk
critical
source
https://github.com/monte-carlo-data/mc-agent-toolkit/tree/main/skills/asset-health
source_repo
monte-carlo-data/mc-agent-toolkit
source_type
community
date_added
2026-07-01
license
Apache-2.0
license_source
https://github.com/monte-carlo-data/mc-agent-toolkit/blob/main/LICENSE

When to Use

  • Use when the user goal matches this upstream workflow.

Monte Carlo Asset Health Skill

This skill checks the health of a data asset using Monte Carlo's observability platform. It produces a structured health report covering freshness, alerts, monitoring coverage, importance, and upstream dependency health.

Monte Carlo tool routing (required): Always call Monte Carlo MCP tools through this plugin's bundled server, whose fully-qualified tool names are mcp__plugin_mc-agent-toolkit_monte-carlo-mcp__<tool> (e.g. mcp__plugin_mc-agent-toolkit_monte-carlo-mcp__get_alerts). Bare tool names used in this skill (get_alerts, search, get_table, …) refer to that bundled server. If the session also has a separately-configured monte-carlo-mcp server, do not route to it — it may point at a different endpoint or credentials.

REQUIRED: Read reference files before executing

You MUST read both reference files using the Read tool before making any MCP tool calls. These files are the source of truth for tool calls, parameters, and response interpretation. This file only defines when to activate and how to format the output.

  1. references/workflows.md (relative to this file) — exact tool calls, phases, and execution order
  2. references/parameters.md (relative to this file) — parameter conventions and field details

Do NOT make any MCP tool calls until you have read both files.

When to activate this skill

Activate when the user:

  • Asks about health: "how is table X doing?", "check health of X", "is X healthy?"
  • Asks about status: "what's the status of X?", "status of orders table"
  • Asks to check on a table: "check on X table", "check on X"
  • Asks about reliability, freshness, or quality of a specific asset
  • References a table in context of incident triage or change planning

When NOT to activate this skill

  • Profiling or exploring table data (row counts, column stats, distributions) → use explore-table
  • Creating or suggesting monitors → use monitoring-advisor
  • Active incident triage (investigating root cause of a firing alert) → use prevent skill Workflow 3

Health report format

CRITICAL: Only report data returned by the tools defined in references/workflows.md. Do NOT call additional tools, do NOT infer or fabricate metrics. Each row below specifies exactly which tool provides its value.

All sections (Active Alerts, Monitors, Upstream Issues, Recommendations) must always appear with their heading. Never omit a section — if there is no data, show the empty-state text defined below.

Never use emoji shortcodes (like :warning: or :arrow_up:). Use Unicode emoji characters directly (like ⚠️) or plain text. Shortcodes render as raw text in the terminal.

Always display URLs as bare URLs, never as markdown links (e.g., [text](https://github.com/monte-carlo-data/mc-agent-toolkit/tree/main/skills/asset-health/url)).

{MC_WEBAPP_URL} appears throughout this template. Every occurrence must be replaced with the actual value returned by calling get_mc_webapp_url(). Never hardcode or guess this URL — it varies by environment.

Present results in this structure:

## Health Check: <table_name>

**Tags:** `tag1:value1`, `tag2:value2` (or "None" if no tags)
**Link:** {MC_WEBAPP_URL}/assets/{mcon}
**Warehouse:** snowflake-prod (Snowflake)
**Status: 🟢 Healthy / 🟡 Degraded / 🔴 Unhealthy** | **Importance:** 0.85 (key asset ⭐️)
**Avg Reads/Day:** ~538 | **Avg Writes/Day:** ~12

| Metric        | Value                          | Signal |
|---------------|--------------------------------|--------|
| Last Activity | Apr 6, 2025                    | 🟢 Recent    |
| Alerts        | 2 active                       | 🔴 Has alerts |
| Monitoring    | 3 active monitors              | 🟢 Monitored  |
| Upstream      | 1/3 sources unhealthy          | 🔴 Issues     |

### Active Alerts

| Date  | Type           | Priority | Status           | Link                                                    |
|-------|----------------|----------|------------------|---------------------------------------------------------|
| Apr 8 | Metric anomaly | P3       | Not acknowledged | {MC_WEBAPP_URL}/alerts/{alert_uuid} |
| Apr 7 | Freshness      | P2       | Acknowledged     | {MC_WEBAPP_URL}/alerts/{alert_uuid} |

If there are more than 5 active alerts, display only 5. Do NOT put the overflow
message inside the table as a row. Instead, put it as plain text on the line
immediately after the table:

There are N more alerts not shown for brevity

If there are zero active alerts, show:
No active alerts in the last 7 days.

### Monitors

| Type        | Name                                    | Incidents (7d) | Status              |
|-------------|-----------------------------------------|----------------|---------------------|
| TABLE       | Orders freshness and schema             | 3              | Running hourly      |
| METRIC      | Revenue row count                       | 0              | Never executed      |
| BULK_METRIC | Warehouse volume check                  | 21             | ⚠️ 1 table has errors |

If there are zero monitors, show:
No monitors configured for this table.

### Upstream Issues
- raw_orders — FRESHNESS alert: not updated in 8h
- raw_payments — healthy
- dim_customers — healthy

> Want me to check further upstream for **raw_orders**?

If there are no upstream dependencies, show:
No upstream dependencies found.

### Diagnosis

1-2 sentences summarizing what is causing the table to be unhealthy, or
confirming it is healthy. This should naturally lead into the recommendations.

Example (unhealthy):
Upstream table raw_orders has not been updated in 8 hours, which is likely
causing staleness in this table. There are also 2 unacknowledged alerts.

Example (healthy):
Table is healthy — no active alerts, monitored, and all upstream sources
are in good shape.

### Recommendations
- Investigate upstream raw_orders freshness — likely root cause of this table's staleness
- Acknowledge or investigate the 2 active alerts

If there are no recommendations, show:
No recommendations — table looks healthy.
Show full SKILL.md (350 more words)Show less
Metric definitions — exact data sources

Each metric row MUST use only the specified data source. Do not add, infer, or embellish values beyond what the tool returns.

MetricData sourceWhat to showSignal
Last Activityget_table → last_activityDate of last activity (e.g., "Apr 6, 2025")🟢 Recent (within 7 days) / 🟡 Stale (older than 7 days)
Alertsget_alerts → count"N active" or "No active alerts"🔴 Has alerts / 🟢 No alerts
Monitoringget_monitors → count where is_paused is false"N active monitors" or "0 active monitors (M paused)". Include relevant details from monitor fields (incident counts, error counts, types).🟢 Monitored (≥1 active) / 🔴 Unmonitored (0 active)
Upstreamget_asset_lineage (upstream) + Phase 3 checks"N/M sources unhealthy" or "All N sources healthy"🔴 Issues (any unhealthy) / 🟢 Healthy (all healthy)

Importance is shown next to the Status line (not in the metrics table). Source: get_table → importance_score + is_important. Show "X.XX (key asset ⭐️)" if key asset or importance > 0.8, otherwise just "X.XX".

Avg Reads/Day and Avg Writes/Day are shown below the Status line. Source: get_table → table_stats.avg_reads_per_active_day and table_stats.avg_writes_per_active_day.

Do NOT include downstream data. This skill only queries upstream lineage.

Status determination
  • 🔴 Unhealthy: Any active alerts on the asset (from get_alerts with statuses ["NOT_ACKNOWLEDGED", "ACKNOWLEDGED", "WORK_IN_PROGRESS"] — see parameters.md)
  • 🟡 Degraded: No active alerts, but 0 active monitors on a high-importance asset (importance > 0.8 or key asset)
  • 🟢 Healthy: No active alerts and has at least 1 active monitor
Tags

Display tags from the search tool's properties field. Show as inline badges: key:value. If no tags exist, show "None". Always include the Tags line.

Warehouse

Display the warehouse name and type from the search result. Always include this line.

Recommendations

Only include recommendations derivable from collected data:

  • Upstream health issues that may be root causes
  • Active alerts that need acknowledgment or investigation
  • Do NOT recommend specific monitor types — that is outside this skill's scope

Examples

text
User: Apply this skill to my current task.
Assistant: Follow the workflow in this skill, cite limitations, and ask before risky steps.

Limitations

  • Use this skill only when the task clearly matches its upstream source and local project context.
  • Verify commands, generated code, dependencies, credentials, and external service behavior before applying changes.
  • Do not treat examples as a substitute for environment-specific tests, security review, or user approval for destructive or costly actions.

© sickn33, 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 skills/monte-carlo-asset-health of sickn33/agentic-awesome-skills.

Open the folder on GitHubat commit b84d35a

Used in 1 other repository

We found 5 copies of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in sickn33/agentic-awesome-skills, which our catalogue first saw on October 7, 2026.

Compare with similar skills

Monte Carlo Asset Health 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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MCP Server BuildershareAI-lab/learn-claude-code78k4 repos~1.2kAutomated safety check: PassMIT
MCP Integration for Pluginsanthropics/claude-plugins-official38k11 repos~3.1kAutomated safety check: PassApache-2.0
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Stitch to Remotion Walkthrough Videosgoogle-labs-code/stitch-skills8.5k6 repos~3.2kAutomated safety check: NotesApache-2.0

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Questions about Monte Carlo Asset Health

What does Monte Carlo Asset Health do?

Curated upstream guidance for Monte Carlo Asset Health; use when the workflow matches the user goal. Monte Carlo Asset Health is an agent skill from sickn33/agentic-awesome-skills. Curated upstream guidance for Monte Carlo Asset Health; use when the workflow matches the user goal.

When should I use Monte Carlo Asset Health?

Monte Carlo Asset Health fits situations like: the workflow matches the user goal.

How do I install Monte Carlo Asset Health in Claude Code?

Run `npx skills add sickn33/agentic-awesome-skills --skill monte-carlo-asset-health -a claude-code`. Or copy the skill folder (skills/monte-carlo-asset-health in sickn33/agentic-awesome-skills) into .claude/skills/monte-carlo-asset-health in your project. Claude Code loads it when a task matches its description.

How do I install Monte Carlo Asset Health in Codex?

Run `npx skills add sickn33/agentic-awesome-skills --skill monte-carlo-asset-health -a codex`. Or copy the skill folder (skills/monte-carlo-asset-health in sickn33/agentic-awesome-skills) into .agents/skills/monte-carlo-asset-health in your project. Codex loads it when a task matches its description.

Can I use Monte Carlo Asset Health 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 sickn33/agentic-awesome-skills --skill monte-carlo-asset-health -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/monte-carlo-asset-health, .gemini/skills/monte-carlo-asset-health, .github/skills/monte-carlo-asset-health and .opencode/skills/monte-carlo-asset-health in your project.

What does Monte Carlo Asset Health need to run?

SKILL.md names no scripts, command-line tools or credentials: Monte Carlo Asset Health is instructions for the agent only.

Does Monte Carlo Asset Health access the network?

SKILL.md names 1 domain. In commands or code: github.com; the agent is likely to contact it when it follows the instructions. This is read from the text; nothing was executed.

Is Monte Carlo Asset Health 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 Monte Carlo Asset Health use?

Monte Carlo Asset Health 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 Monte Carlo Asset Health use?

About 2.4k tokens (SKILL.md is roughly 9.6k 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 Monte Carlo Asset Health?

Skills that share tags, products or a category with Monte Carlo Asset Health: MCP Server Builder (anthropics/skills, 180k stars), MCP Server Builder (shareAI-lab/learn-claude-code, 78k stars), MCP Integration for Plugins (anthropics/claude-plugins-official, 38k stars) and Figma use_figma Plugin API Rules (warpdotdev/warp, 65k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Monte Carlo Asset Health?

sickn33 (a GitHub user) maintains it in sickn33/agentic-awesome-skills, which has 47,405 GitHub stars. The repository holds 1,497 skills in this directory. The repository was last updated on October 9, 2026.

Source: sickn33/agentic-awesome-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.