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Agent skill

Tune Monitor

by sickn33 in sickn33/agentic-awesome-skills

Analyze a Monte Carlo monitor and recommend config changes to reduce alert noise.

Apache-2.0Auto-check passedDatabases

Install Tune Monitor

skills CLI
$ npx skills add sickn33/agentic-awesome-skills --skill tune-monitor -a claude-code

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

GitHub CLI
$ gh skill install sickn33/agentic-awesome-skills tune-monitor --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/tune-monitor .claude/skills/tune-monitor && 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
tune-monitor
GitHub stars
47k
Used in
1 other repo
Token cost
~3.3k tokens
SKILL.md length
1,162 words
Files
1
Skills in repo
1,497
Repo updated
First seen
Licence
Apache-2.0

At a glance

Analyze a Monte Carlo monitor and recommend config changes to reduce alert noise.

  • Works in 6 steps: Validate Input → Fetch Monitor Report → 5: Determine Monitor Type and Load… → …
  • Tasks that involve SQL
  • SKILL.md covers When to Use, Prerequisites, Available MCP tools and Phase 0: Validate Input, plus 5 more sections
  • Reaches clidocs.getmontecarlo.com

What it does

Tune Monitor is an agent skill from sickn33/agentic-awesome-skills. Analyze a Monte Carlo monitor and recommend config changes to reduce alert noise. Supports metric, custom SQL, validation, and table monitors. Fetches the report, identifies patterns, and suggests tuning.

Its SKILL.md is about 3.3k 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 Databases, covering SQL and MCP servers. It works with SQL and 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

  • Tasks that involve SQL
  • Tasks that involve MCP servers

Example prompts

  • “/tune-monitor”

Workflow steps

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

  1. Validate Input
  2. Fetch Monitor Report
  3. 5: Determine Monitor Type and Load Reference
  4. Analyze the Report
  5. Generate Recommendations
  6. Present the Report

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 (its code samples are markdown).

    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:

    • clidocs.getmontecarlo.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

Tune Monitor loads about 3.3k tokens when it runs. Until then it costs about 54 tokens; SKILL.md has 1,162 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~54
When it runs · the whole SKILL.md, loaded when a task matches
~3.3k

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). 1,162 words, ~3,280 tokens.

Download SKILL.mdSave it as .claude/skills/tune-monitor/SKILL.md (or your agent's skills folder).
name
tune-monitor
description
Analyze a Monte Carlo monitor and recommend config changes to reduce alert noise. Supports metric, custom SQL, validation, and table monitors. Fetches the report, identifies patterns, and suggests tuning.
risk
critical
source
https://github.com/monte-carlo-data/mc-agent-toolkit/tree/main/skills/tune-monitor
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

Tune Monitor: Noise Reduction Analysis

When to Use

Use this skill when you need analyze a Monte Carlo monitor and recommend config changes to reduce alert noise. Supports metric, custom SQL, validation, and table monitors. Fetches the report, identifies patterns, and suggests tuning.

You are a Monte Carlo monitor tuning agent. Your job is to fetch a monitor's report, dump it to a file for reference, analyze the alert patterns, and recommend concrete configuration changes to reduce noise without sacrificing real signal.

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.

Arguments: $ARGUMENTS

Reference files live next to this skill file. Use the Read tool (not MCP resources) to access them:

  • Metric monitor tuning: references/metric-monitor.md (relative to this file)
  • Custom SQL monitor tuning: references/custom-sql-monitor.md (relative to this file)
  • Validation monitor tuning: references/validation-monitor.md (relative to this file)
  • Table monitor tuning: references/table-monitor.md (relative to this file)

Prerequisites

  • Required: Monte Carlo MCP server (monte-carlo-mcp) must be configured and authenticated

Available MCP tools

ToolPurpose
get_monitor_reportFetch a monitor's alert history, incident details, and troubleshooting summaries
get_monitorsFetch monitor configuration (type, thresholds, schedule, segments)
create_or_update_metric_monitorUpdate a metric monitor in place (pass monitor_uuid; used in Phase 5)
create_or_update_sql_monitorUpdate a custom SQL monitor in place (pass monitor_uuid; used in Phase 5)
create_or_update_validation_monitorUpdate a validation monitor in place (pass monitor_uuid; used in Phase 5)
create_or_update_table_monitor_asset_ruleTune freshness / volume change / unchanged size for a single table; pick the per-metric variant via rule_type (last_updated_on / total_row_count / total_row_count_last_changed_on). One call per (table, metric) pair (used in Phase 5).

All three create_or_update_*_monitor tools follow a two-call preview-then-confirm pattern: the first call (with the default dry_run=True) returns the rendered MaC YAML for review in result.yaml; the second call (dry_run=False) deploys the change live and returns a deep link in result.instructions. Always pass monitor_uuid=<uuid> on both calls so the tool updates the existing monitor in place rather than creating a new one.

Phase 0: Validate Input

Extract the monitor UUID from $ARGUMENTS. It must be a valid UUID (format: xxxxxxxx-xxxx-xxxx-xxxx-xxxxxxxxxxxx).

If no UUID is provided or it doesn't look like a UUID, stop and tell the user:

Please provide a monitor UUID. Example: /tune-monitor 94c2dd3a-ef49-40f8-b1c1-741ba057cabf

Phase 1: Fetch Monitor Report

Call get_monitor_report with:

  • monitor_uuid: the UUID from $ARGUMENTS
  • max_incidents: 50

If the tool returns an error or empty result, tell the user the monitor was not found and stop.

Also fetch the monitor's full config via get_monitors with:

  • monitor_ids: [{monitor_uuid}]
  • include_fields: [config]

Run both calls in parallel.

Phase 1.5: Determine Monitor Type and Load Reference

From the get_monitors config response, determine the monitor type:

Config indicatorTypeReference file
Monitor type is a metric monitor variant (e.g., metric, field health)Metricreferences/metric-monitor.md
Monitor type is a custom SQL rule / custom monitorCustom SQLreferences/custom-sql-monitor.md
Monitor type is a validation rule / validation monitorValidationreferences/validation-monitor.md
Monitor type is a table monitor (freshness, volume, schema across tables)Tablereferences/table-monitor.md

Read the appropriate reference file using the Read tool with the path relative to this skill file. The reference contains type-specific config fields to extract, recommendation guidance, and apply-changes instructions.

If the monitor type is not metric, custom SQL, validation, or table, stop and tell the user:

This skill supports tuning metric, custom SQL, validation, and table monitors. This monitor is a {type} monitor, which is not supported.

Phase 2: Analyze the Report

Analyze the monitor report and config together. Focus on:

2a. Alert volume & frequency
  • How many incidents in the last 30 days? Last 7 days?
  • What is the firing cadence — multiple times per day? Daily? Sporadic?
  • Are incidents clustered in time (bursts) or spread evenly?
2b. Anomaly patterns
  • Which segments (field values) are firing most? Are they the same segments repeatedly?
  • Are anomalies consistently marginal (just above threshold) or severe?
  • Are any anomalies from sparse/bursty event types that naturally spike?
  • Are anomalies caused by known operational events (deployments, batch jobs, bulk user actions)?
  • For validation monitors: how many invalid rows per incident? Is the count stable or growing?
  • For table monitors: which (table, metric) pairs are firing most? Are they the same repeatedly?
Show full SKILL.md (438 more words)Show less
2c. Current configuration

Extract the current configuration. The specific fields to look for are documented in the per-type reference loaded in Phase 1.5. At minimum, extract:

  • Monitor type and what it measures
  • Schedule interval
  • Audiences / notification channels
  • Whether the monitor uses ML thresholds or explicit thresholds
2d. Troubleshooting analysis (if available)

Look at any troubleshooting TL;DRs in the report. Note:

  • Are most anomalies assessed as "likely normal data variation"?
  • Are there recurring root causes?
  • Is there a blind spot (e.g., no upstream metadata)?

Phase 3: Generate Recommendations

Based on the analysis, produce a prioritized list of recommendations. For each recommendation:

  • State the problem it solves
  • Give the specific config change (use exact field names from the MC config schema)
  • Explain the trade-off (what signal might be lost)
General recommendations (all monitor types)
Sensitivity tuning (ML thresholds only)

This applies to any monitor that uses ML thresholds — both metric monitors and custom SQL monitors. Skip this section for validation monitors (they don't use ML thresholds), for table monitors (they have their own per-metric sensitivity — see the table monitor reference), and for monitors with explicit thresholds (for custom SQL monitors, see threshold adjustment in the per-type reference instead).

  • If anomalies are consistently marginal (observed value just barely above threshold) AND assessed as normal variation → recommend lowering sensitivity one step:
    • If current sensitivity is HIGH → recommend "sensitivity": "medium"
    • If current sensitivity is MEDIUM or AUTO → recommend "sensitivity": "low"
  • If current sensitivity is already LOW and still noisy → note this isn't a sensitivity issue
Schedule / interval
  • If the monitor fires multiple times per day but anomalies always resolve within hours → recommend increasing schedule interval (e.g., from 720 min to 1440 min) to reduce duplicate alerts
  • If anomalies are caused by data arriving late → recommend increasing collection_lag
Snooze / training period
  • If the monitor was recently created (<30 days) and is still learning patterns → recommend waiting for the model to stabilize before tuning
Audience / notification routing
  • If the monitor has no audiences configured and is generating noise → recommend adding audiences only for high-severity anomalies, or removing notifications entirely for known-noisy monitors
Type-specific recommendations

For type-specific recommendations (WHERE conditions, segment exclusion, aggregation changes, threshold adjustment, SQL modifications, alert condition modifications, per-table-metric sensitivity tuning), follow the guidance in the per-type reference loaded in Phase 1.5.

Phase 4: Present the Report

Output a structured analysis. This is the primary output — include it in full.

markdown
## Monitor Tune Report: {monitor_uuid}

**Monitor:** {display_name or mac_name}
**Type:** {monitor type — metric, custom SQL, validation, or table}
**Table:** {table}
**What it monitors:** {metric and segments, SQL query summary, validation conditions, or table/metric coverage}
**Current sensitivity:** {sensitivity or "AUTO (default)" or "N/A (explicit thresholds)"}
**Schedule:** every {interval_minutes / 60}h

### Alert Summary (last 30 days)
- Total alerts: {count}
- Firing frequency: {e.g., "~twice daily", "daily", "sporadic"}
- Most noisy segments: {top 2-3 segment values by alert count, or N/A for custom SQL/validation}
- Most noisy (table, metric) pairs: {for table monitors: top pairs by anomaly count}

### Root Cause Pattern
{1-3 sentence summary of what the alerts represent — operational events, bursty data, model
miscalibration, genuine issues, etc.}

### Recommendations

#### 1. {Highest-impact change} [RECOMMENDED]
**Problem:** ...
**Change:**
```yaml
{specific config field}: {new value}

Trade-off: ...

2. {Second change} [OPTIONAL]

...

3. {Third change} [OPTIONAL]

...

What NOT to change

{Any configurations that look correct and should be left alone — avoid over-tuning.}

If these changes are made

{Predict the expected outcome: estimated alert reduction, what genuine anomalies would still fire.}


**Next step:** "Want me to apply any of these changes to the monitor config, or explore the alert
history further?"

---

## Phase 5: Apply Changes (if user requests)

To apply changes, follow the apply-changes instructions in the per-type reference loaded in
Phase 1.5. Each reference specifies the correct tool and constraints for that monitor type.

General rules for all types:
1. **Always preview first** — show the user what will change before applying.
2. **Get explicit confirmation** before applying any change.
3. **Validate the preview YAML against the schema** — before presenting the preview YAML to the user, fetch the published MaC JSON Schema from `https://clidocs.getmontecarlo.com/mac/schema.json` (WebFetch) and check the preview YAML against it. If any field in the YAML does not appear in the schema for the given monitor type, flag it and correct it. Note: the schema validates field names, types, and enum values only — cross-field semantic constraints are enforced by the backend at apply time, not by the schema.
4. **MaC-managed monitors** — if `get_monitors` returns a `mac_name` or the user mentions the monitor is managed via a MaC YAML file, note this before applying: changes made via the API will be overwritten the next time `montecarlo monitors apply` runs. Offer to hand off to `/manage-mac` (edit workflow) instead so the YAML file stays the source of truth.

---

## Guidelines

- **Be specific.** Generic advice like "reduce sensitivity" is less useful than exact config changes.
- **Prefer surgical changes.** A targeted WHERE condition beats a blunt sensitivity reduction.
- **Preserve signal.** Always explain what genuine anomalies would still be caught after tuning.
- **Cite evidence.** Reference specific incident dates, segment values, and counts from the report.
- **Degrade gracefully.** If troubleshooting runs are missing, note the limited context and
  reason from alert patterns alone.
- **Add `$schema` when saving YAML to a file.** If the user asks to save the MaC YAML to a file, add `# yaml-language-server: $schema=https://clidocs.getmontecarlo.com/mac/schema.json` as the first line of that file.

## 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/tune-monitor 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

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Nvidia Ontology QueryNVIDIA/skills3.6k—~1.7kAutomated safety check: PassApache-2.0
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Pytorch Clickhousepytorch/test-infra113—~2.8kAutomated safety check: PassCustom licence

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Works with

Categories

Questions about Tune Monitor

What does Tune Monitor do?

Analyze a Monte Carlo monitor and recommend config changes to reduce alert noise. Tune Monitor is an agent skill from sickn33/agentic-awesome-skills. Analyze a Monte Carlo monitor and recommend config changes to reduce alert noise.

When should I use Tune Monitor?

Tune Monitor fits situations like: tasks that involve SQL; tasks that involve MCP servers.

How do I install Tune Monitor in Claude Code?

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

How do I install Tune Monitor in Codex?

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

Can I use Tune Monitor 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 tune-monitor -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/tune-monitor, .gemini/skills/tune-monitor, .github/skills/tune-monitor and .opencode/skills/tune-monitor in your project.

What does Tune Monitor need to run?

SKILL.md names no scripts, command-line tools or credentials: Tune Monitor is instructions for the agent only.

Does Tune Monitor access the network?

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

Is Tune Monitor 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 Tune Monitor use?

Tune Monitor 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 Tune Monitor use?

About 3.3k tokens (SKILL.md is roughly 13k 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 Tune Monitor?

Skills that share tags, products or a category with Tune Monitor: Sap Hana CLI (secondsky/sap-skills, 462 stars), Nvidia Ontology Query (NVIDIA/skills, 3.6k stars), Dune MCP Skill (holon-run/uxc, 116 stars) and Semantic Analyst (sidequery/sidemantic, 129 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Tune Monitor?

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.