Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add fabioc-aloha/Alex_Skill_Mall --skill semantic-overlap-detectiongit clone --depth 1 https://github.com/fabioc-aloha/Alex_Skill_MallWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/fabioc-aloha/alex_skill_mall/semantic-overlap-detection)<a href="https://agentmods.dev/skills/fabioc-aloha/alex_skill_mall/semantic-overlap-detection"><img src="https://agentmods.dev/badge/skills/fabioc-aloha/alex_skill_mall/semantic-overlap-detection/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/fabioc-aloha/alex_skill_mall/semantic-overlap-detection"><img src="https://agentmods.dev/badge/skills/fabioc-aloha/alex_skill_mall/semantic-overlap-detection.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00099 | $0.06350 |
| Opus 5 | $0.00049 | $0.03175 |
| Sonnet 5 | $0.00020 | $0.01270 |
| Haiku 4.5 | $0.00010 | $0.00635 |
Grade A, and why
semantic-overlap-detection scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 8d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
88% identical to semantic-model-disambiguation — 219 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 428 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Semantic Overlap Detection
Analyze a semantic model (via MCP connection or TMDL export) to find columns whose value domains overlap, classify ambiguity types, generate proposed fixes, and produce a review Excel workbook for domain expert review. This skill covers the full analysis pipeline through Excel generation and async handoff.
Next step after this skill: Once the domain expert has reviewed the Excel, use the semantic-disambiguation skill to apply approved changes to the live model.
When to Use
- A semantic model has multiple columns that could plausibly match the same natural-language query
- A Fabric data agent or Copilot is silently picking the wrong column
- You need to prepare a model for the "Prep data for AI" workflow
- You want to audit a TMDL export for disambiguation gaps before connecting a data agent
Procedure
Phase 0: Read Model Metadata via MCP (preferred) or TMDL files
MCP tool naming: This skill references tools by their VS Code MCP integration names (
mcp_powerbi-modeling-mcp_*). In other environments (Agency CLI, Claude Code), find the equivalent Power BI Modeling MCP server tools — the operations and parameters are the same, only the tool name prefix may differ.
If MCP tools are available, connect to the live model and read metadata directly. This is preferred over TMDL file parsing because it reflects the current model state, includes runtime metadata (hidden flags, descriptions), and avoids stale exports.
-
Connect via
mcp_powerbi-modeling-mcp_connection_operations:- Fabric:
ConnectFabricwith workspace and dataset names - Desktop:
Connectwith port number
- Fabric:
-
List all tables via
mcp_powerbi-modeling-mcp_table_operations(operation:List). For each table, record:name,isHidden,description- Skip tables where
isHidden = true
-
List columns per table via
mcp_powerbi-modeling-mcp_column_operations(operation:List, filter bytableNames). For each column, record:name,tableName,dataType,isHidden,description,sourceProviderType- Skip columns where
isHidden = trueordataTypeis not string/text
What ships with it
5 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 8d ago First seen · 428 lines · 99 tokens per session scan A 5ef274db189f
semantic-overlap-detection is a skill published in the GitHub repository fabioc-aloha/Alex_Skill_Mall (4 stars, last pushed today), licensed MIT. It adds 99 tokens to every session and 6,350 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 0 findings. It is 88% identical to semantic-model-disambiguation, differing in 219 lines, and is treated as a copy.
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