Datalineage Summary
google/skills
Summarizes Google Cloud Data Lineage graphs to help users debug data quality issues and understand data provenance for BQ/GCS.
Route data-related requests to the right Monte Carlo skill or workflow.
Install Monte Carlo Context Detection
The automated check flagged lines worth reading first. See the safety section below.
$ npx skills add sickn33/agentic-awesome-skills --skill monte-carlo-context-detection -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install sickn33/agentic-awesome-skills monte-carlo-context-detection --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/sickn33/agentic-awesome-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/monte-carlo-context-detection .claude/skills/monte-carlo-context-detection && 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 "monte-carlo-context-detection" agent skill from https://github.com/sickn33/agentic-awesome-skills/tree/main/skills/monte-carlo-context-detection into .claude/skills/monte-carlo-context-detection/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "monte-carlo-context-detection", 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/sickn33/agentic-awesome-skills/tree/main/skills/monte-carlo-context-detectionType 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 sickn33/agentic-awesome-skills --skill monte-carlo-context-detection -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install sickn33/agentic-awesome-skills monte-carlo-context-detection --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/sickn33/agentic-awesome-skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/monte-carlo-context-detection .agents/skills/monte-carlo-context-detection && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "monte-carlo-context-detection" agent skill from https://github.com/sickn33/agentic-awesome-skills/tree/main/skills/monte-carlo-context-detection into .agents/skills/monte-carlo-context-detection/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "monte-carlo-context-detection", 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 sickn33/agentic-awesome-skills --skill monte-carlo-context-detection -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install sickn33/agentic-awesome-skills monte-carlo-context-detection --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/sickn33/agentic-awesome-skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/monte-carlo-context-detection .cursor/skills/monte-carlo-context-detection && 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 "monte-carlo-context-detection" agent skill from https://github.com/sickn33/agentic-awesome-skills/tree/main/skills/monte-carlo-context-detection into .cursor/skills/monte-carlo-context-detection/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "monte-carlo-context-detection", 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/sickn33/agentic-awesome-skills.git --path skills/monte-carlo-context-detection--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 sickn33/agentic-awesome-skills --skill monte-carlo-context-detection -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install sickn33/agentic-awesome-skills monte-carlo-context-detection --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/sickn33/agentic-awesome-skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/monte-carlo-context-detection .gemini/skills/monte-carlo-context-detection && 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 "monte-carlo-context-detection" agent skill from https://github.com/sickn33/agentic-awesome-skills/tree/main/skills/monte-carlo-context-detection into .gemini/skills/monte-carlo-context-detection/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "monte-carlo-context-detection", 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 sickn33/agentic-awesome-skills monte-carlo-context-detectionInstalls 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 sickn33/agentic-awesome-skills --skill monte-carlo-context-detection -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/sickn33/agentic-awesome-skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/monte-carlo-context-detection .github/skills/monte-carlo-context-detection && 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 "monte-carlo-context-detection" agent skill from https://github.com/sickn33/agentic-awesome-skills/tree/main/skills/monte-carlo-context-detection into .github/skills/monte-carlo-context-detection/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "monte-carlo-context-detection", 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 sickn33/agentic-awesome-skills --skill monte-carlo-context-detection -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install sickn33/agentic-awesome-skills monte-carlo-context-detection --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/sickn33/agentic-awesome-skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/monte-carlo-context-detection .opencode/skills/monte-carlo-context-detection && 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 "monte-carlo-context-detection" agent skill from https://github.com/sickn33/agentic-awesome-skills/tree/main/skills/monte-carlo-context-detection into .opencode/skills/monte-carlo-context-detection/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "monte-carlo-context-detection", 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
monte-carlo-context-detection- GitHub stars
- 47k
- Used in
- 1 other repo
- Token cost
- ~2.6k tokens
- SKILL.md length
- 1,185 words
- Files
- 2 (incl. references)
- Skills in repo
- 1,493
- Repo updated
- First seen
- Licence
- MIT
At a glance
Route data-related requests to the right Monte Carlo skill or workflow.
- Works in 5 steps: Fast-path clear intent (stop early if… → Categorize intent → Gather scope (only if needed) → …
- Any ambiguous data observability request
- SKILL.md covers When to Use, When to activate this skill, When NOT to activate this skill and Workflow: Reactive Routing, plus 2 more sections
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
What it does
Monte Carlo Context Detection is an agent skill from sickn33/agentic-awesome-skills. Route data-related requests to the right Monte Carlo skill or workflow. USE WHEN alerts, incidents, data broken, stale, coverage gaps, data quality, or any ambiguous data observability request.
Its SKILL.md is about 2.6k tokens, which your agent loads only when the skill is triggered. The skill folder holds 2 other files, including reference files (for example `references/signal-definitions.md`).
It sits in Data & Analytics, covering Data cleaning, Test coverage and Data governance. 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 MIT.
When your agent uses it
- Any ambiguous data observability request
- Tasks that involve Data cleaning
- Tasks that involve Test coverage
Example prompts
- “/monte-carlo-context-detection”
Requirements
- Python 3
Workflow steps
5 steps, taken from the step headings in SKILL.md.
What it can do on your machine
Read from SKILL.md and the folder at commit 680176d. 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
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
Monte Carlo Context Detection loads about 2.6k tokens when it runs, and up to ~3.4k if it reads all its reference files. Until then it costs about 56 tokens; SKILL.md has 1,185 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: warnings
The automated check found patterns that need a careful read before installing.
- WarningTells the agent its actions are pre-authorized / not to stop for confirmationSKILL.md:143
.md and begin executing it immediately. Do not ask for confirmation.
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 680176d, republished under its MIT licence (© sickn33). 1,185 words, ~2,581 tokens.
Download SKILL.mdSave it as
.claude/skills/monte-carlo-context-detection/SKILL.md (or your agent's skills folder). This skill also uses 1 other file; get the full folder from GitHub.- name
- monte-carlo-context-detection
- description
- Route data-related requests to the right Monte Carlo skill or workflow. USE WHEN alerts, incidents, data broken, stale, coverage gaps, data quality, or any ambiguous data observability request.
- when_to_use
- Invoke for ambiguous or incomplete data-observability requests that don't clearly name a specific skill. Example triggers: "something is wrong with my data"…
- bucket
- Agent-routing
- version
- 1.0.0
- source_repo
- monte-carlo-data/mc-agent-toolkit
- source_type
- community
- source
- community
- date_added
- 2026-09-21
- risk
- unknown
When to Use
- Use when this upstream workflow matches the user's stated goal.
- Use when the task requires the procedures documented in this skill.
Monte Carlo Context Detection
This skill determines which Monte Carlo skill or workflow best fits the user's current context. It activates reactively for ambiguous or multi-step data-related messages, gathers signals, and routes to the right skill or workflow.
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-configuredmonte-carlo-mcpserver, do not route to it — it may point at a different endpoint or credentials.
Reference file for signal definitions: references/signal-definitions.md (relative to this file). Read it before routing.
When to activate this skill
This skill is activated by the CLAUDE.md routing table when:
- The user's message relates to data quality, alerts, incidents, coverage, or Monte Carlo — but doesn't clearly match a single skill in the routing table
- The user's intent is ambiguous or could span multiple skills
- The user asks a broad question like "help me with my data" or "what's going on?"
When NOT to activate this skill
- A skill or workflow is already active in the conversation — the active skill owns the conversation, do not intercept
- The user's message clearly matches a single skill in the CLAUDE.md routing table — route directly, no need for context detection
- The user is editing a dbt model — defer to the
preventskill which auto-activates via hooks - The user's message is not data-related at all
Workflow: Reactive Routing
This skill is purely reactive — it activates for ambiguous or multi-step data-related messages and routes them.
Follow these steps in order.
Step 0: Fast-path clear intent (stop early if matched)
Before doing anything else, check whether the user's message unambiguously matches a single existing skill. If so, skip the rest of this workflow and immediately load that skill — do NOT read references/signal-definitions.md, do NOT make API probes.
| Clear user intent | Skill to load immediately |
|---|---|
| "Check health of [named table]" / "status of [named table]" | ../asset-health/SKILL.md |
| "Create a [monitor type] on [named table]" | ../monitoring-advisor/SKILL.md |
| "Investigate alert on [named table]" / "why is [named table] stale/broken?" | ../incident-response/SKILL.md |
| "What should I monitor?" / "where are my coverage gaps?" | ../proactive-monitoring/SKILL.md |
| "Instrument my agent" / "set up Monte Carlo tracing on [named framework] agent" / "setting up an agent" | ../instrument-agent/SKILL.md |
Context-detection is for ambiguous requests only. If the request is clear, routing through this skill wastes turns and tokens.
If no clear match, proceed to Step 1.
Step 1: Categorize intent
Read references/signal-definitions.md for the full signal catalog. Determine which category the user's message falls into:
| Category | Signals | Example messages |
|---|---|---|
| Specific asset | User mentions a table name, or has a .sql model file open in their IDE | "what's wrong with stg_payments?", "check this table" |
| Active incident | Keywords: alert, broken, stale, failing, incident, triage, wrong data | "I have alerts firing", "data looks wrong", "something broke" |
| Coverage/monitoring | Keywords: monitor, coverage, gaps, unmonitored, what should I watch | "what should I monitor?", "where are my gaps?" |
| Agent instrumentation | Keywords: instrument, set up tracing, set up Monte Carlo tracing, setting up an agent. Often mentions an AI framework (LangChain, LangGraph, OpenAI, Anthropic, CrewAI, Bedrock, SageMaker, Vertex AI) | "instrument my agent", "set up MC tracing on my LangGraph agent", "setting up an agent" |
| General/exploratory | No clear category, broad question | "help me with data quality", "what can Monte Carlo do?" |
Step 2: Gather scope (only if needed)
- Specific asset known (from file context or user mention) → proceed to Step 3
- Active incident, no scope → ask: "Want me to check recent alerts? Any specific time range or severity?"
- Coverage/monitoring, no scope → ask: "Which warehouse should I look at, or should I check across all?"
- General/exploratory → present the categories: "I can help with: (1) investigating active alerts or data issues, (2) analyzing monitoring coverage and creating monitors, or (3) checking the health of specific tables. What are you looking for?"
Show full SKILL.md (519 more words)Show less
Step 3: Scoped API probe (when scope is available)
Only make API calls when you have enough context to scope them:
- Specific asset → call
get_alertswith the table's MCON or name filter, andget_monitorsfor that table - Active incident with scope → call
get_alertswith the user's time range / severity filters - Coverage/monitoring → skip API probe, route directly to proactive monitoring workflow (it handles its own API calls)
- If MCP tool calls fail (auth not configured) → skip API, fall back to conversation intent alone
Always scope MCP calls tightly. Unscoped get_alerts, search, or get_monitors on large accounts can return hundreds of results, overflow the tool-result token limit, spill to disk, and force expensive chunk reads — burning user tokens and risking workflow failure. Minimum scoping:
get_alerts→ time filter (created_after, default last 7 days) + at least one ofwarehouse,table_names,severitysearch→ needed to resolve a table name to its MCON (get_tablerequires MCON). ALWAYS passlimit(e.g. 5), the table name asquery, and filter bywarehouse_uuidordatabase/schema.warehouse_typesalone ("snowflake") matches thousands of tables. Disambiguation rules when multiple matches return:- If the user named a warehouse (e.g. "analytics-snowflake") → auto-pick the match whose
warehouse_display_namematches and proceed. Do NOT stop to ask. - If the user named a database/schema → auto-pick the match in that database/schema.
- If one match is flagged
is_key_asset: trueand others aren't → auto-pick the key asset. - Only ask the user to disambiguate when none of the above resolve it.
- If the user named a warehouse (e.g. "analytics-snowflake") → auto-pick the match whose
get_monitors→ always filter bymcons(table MCON) orwarehouse_uuid
If you don't have enough scope, ask the user before calling.
Step 4: Route
Based on the combined signals from Steps 1-3:
| Combined signals | Confidence | Action |
|---|---|---|
| Active alerts found + incident intent | High | Auto-activate incident response workflow: read and follow ../incident-response/SKILL.md |
| Coverage intent + data project detected | High | Auto-activate proactive monitoring workflow: read and follow ../proactive-monitoring/SKILL.md |
| User asks to create a specific monitor (type + table known) | High | Auto-activate monitoring-advisor: read and follow ../monitoring-advisor/SKILL.md |
| Table mentioned + "health" / "status" / "check" intent | High | Auto-activate asset-health: read and follow ../asset-health/SKILL.md |
| Agent instrumentation intent (instrument / set up tracing / setting up an agent) + Python codebase context | High | Auto-activate instrument-agent: read and follow ../instrument-agent/SKILL.md |
| Ambiguous or conflicting signals | Low | Suggest options and wait for user to choose |
High confidence = auto-activate. Load the target skill's SKILL.md and begin executing it immediately. Do not ask for confirmation.
Low confidence = suggest. Present 2-3 options with brief descriptions and let the user choose. Example:
"Based on what you've described, I can:
- Investigate alerts — triage and fix active data issues (incident response workflow)
- Improve monitoring — find coverage gaps and create monitors (proactive monitoring workflow)
Which would be most helpful?"
Prevent guardrail
If the user is actively editing a dbt model file (making code changes, not just viewing or asking about it) and the prevent skill's hooks are active, do NOT route to any other skill. Instead respond:
"The prevent skill will automatically handle impact assessment for dbt model changes via its pre-edit hooks. No additional routing needed."
Examples
User: Apply this skill to my current task.
Assistant: Follow the workflow in this skill, cite limitations, and ask before risky steps.Limitations
- Imported upstream skill; verify credentials, permissions, and safety boundaries before execution.
- Does not replace environment-specific validation, testing, or maintainer review.
© sickn33, MIT. 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 1 other file (references) in skills/monte-carlo-context-detection of sickn33/agentic-awesome-skills.
- SKILL.md
- references/signal-definitions.md
Open the folder on GitHubat commit 680176d
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 Context Detection 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 |
|---|---|---|---|---|---|---|
| Monte Carlo Context Detection this skillsickn33/agentic-awesome-skills | 47k | 1 repos | ~2.6k | Automated safety check: Warn | MIT | |
| Datalineage Summarygoogle/skills | 21k | — | ~1.7k | Automated safety check: Pass | Apache-2.0 | |
| Openbb Data Fetchermonarchjuno/vibe-investing | 299 | — | ~2.9k | Automated safety check: Notes | MIT | |
| Investigate Datawalkthru-earth/geocoding-playground | 153 | — | ~935 | Automated safety check: Pass | CC-BY-4.0 | |
| Odoo Data Quality Gateerpipe-org/mcp-odoo | 421 | — | ~765 | Automated safety check: Pass | MIT | |
| Diagnosekbanc85/claudia | 296 | — | ~1.8k | Automated safety check: Pass | Custom licence |
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Questions about Monte Carlo Context Detection
What does Monte Carlo Context Detection do?
Route data-related requests to the right Monte Carlo skill or workflow. Monte Carlo Context Detection is an agent skill from sickn33/agentic-awesome-skills. Route data-related requests to the right Monte Carlo skill or workflow.
When should I use Monte Carlo Context Detection?
Monte Carlo Context Detection fits situations like: any ambiguous data observability request; tasks that involve Data cleaning; tasks that involve Test coverage.
How do I install Monte Carlo Context Detection in Claude Code?
Run `npx skills add sickn33/agentic-awesome-skills --skill monte-carlo-context-detection -a claude-code`. Or copy the skill folder (skills/monte-carlo-context-detection in sickn33/agentic-awesome-skills) into .claude/skills/monte-carlo-context-detection in your project. Claude Code loads it when a task matches its description.
How do I install Monte Carlo Context Detection in Codex?
Run `npx skills add sickn33/agentic-awesome-skills --skill monte-carlo-context-detection -a codex`. Or copy the skill folder (skills/monte-carlo-context-detection in sickn33/agentic-awesome-skills) into .agents/skills/monte-carlo-context-detection in your project. Codex loads it when a task matches its description.
Can I use Monte Carlo Context Detection 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-context-detection -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-context-detection, .gemini/skills/monte-carlo-context-detection, .github/skills/monte-carlo-context-detection and .opencode/skills/monte-carlo-context-detection in your project.
What does Monte Carlo Context Detection need to run?
SKILL.md names no scripts, command-line tools or credentials: Monte Carlo Context Detection is instructions for the agent only. Our summary lists: Python 3.
Does Monte Carlo Context Detection 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 Monte Carlo Context Detection safe to install?
Our automated static check of SKILL.md flagged 1 warning(s): tells the agent its actions are pre-authorized / not to stop for confirmation. Read the flagged lines before installing; the check is not a guarantee either way.
What licence does Monte Carlo Context Detection use?
Monte Carlo Context Detection is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Monte Carlo Context Detection use?
About 2.6k tokens (SKILL.md is roughly 10k 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 859 tokens, read only when the agent opens those files.
What are the alternatives to Monte Carlo Context Detection?
Skills that share tags, products or a category with Monte Carlo Context Detection: Datalineage Summary (google/skills, 21k stars), Openbb Data Fetcher (monarchjuno/vibe-investing, 299 stars), Investigate Data (walkthru-earth/geocoding-playground, 153 stars) and Odoo Data Quality Gate (erpipe-org/mcp-odoo, 421 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Monte Carlo Context Detection?
sickn33 (a GitHub user) maintains it in sickn33/agentic-awesome-skills, which has 47,379 GitHub stars. The repository holds 1,493 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.