> For the complete documentation index, see [llms.txt](https://docs.rootcause.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.rootcause.ai/api-and-integrations/api-access/mcp-integration/mcp-skills.md).

# Guided Skills

Skills are packaged expert workflows: step-by-step procedures for the platform's more involved flows, with the pitfalls written down. Composing a causal dataset, validating a twin before trusting it, writing SQL in the platform's dialect: each is a skill the assistant loads and follows rather than improvising.

You do not need to manage them. The assistant is instructed to check the skills catalogue before acting on a request, and loads the right one on its own (`load_skill`). But you can also invoke one directly.

***

## Invoking a skill yourself

In clients that support MCP prompts, every skill is a slash command. In Claude Code, type `/mcp__rootcause__` and the full list autocompletes; for example:

```
/mcp__rootcause__build-and-validate-twin
```

This loads the workflow into the conversation and the assistant follows it from there. In clients without prompt support, just ask ("use the build-and-validate-twin skill") and the assistant loads it as a tool call.

***

## The catalogue

### Bringing data in

| Skill                               | What it walks through                                                                                                                                   |
| ----------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `discover-and-register-source`      | Finding an external or public data source and bringing it in: discover, register, preview, import                                                       |
| `import-from-database-connector`    | Importing tables, collections, or files from an already-connected database or storage connector                                                         |
| `extract-documents`                 | Turning documents in the workspace (PDF, DOCX, PPTX, text) into structured sources: lifted tables, per-document extraction, entity mentions             |
| `research-to-table`                 | Turning web pages or a research prompt into a workspace table (on deployments with web research enabled)                                                |
| `external-data-for-causal-analysis` | Importing external data so it survives causal analysis: column types, anchor identifiers, ontology fixes after import                                   |
| `install-foundation-ontology`       | Vetting and proposing a curated Foundation Ontology package and whether it will join to your data; the installation itself completes in the platform UI |

### Shaping data

| Skill                     | What it walks through                                                                                                                                          |
| ------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `compose-causal-view`     | Turning a causal question ("what drives X?") into a valid, analyzable dataset                                                                                  |
| `concatenate-datasets`    | Stacking same-shaped sources end to end (monthly exports, split files) into one table                                                                          |
| `find-join-keys`          | Establishing whether two sources can actually join, by probing real values instead of trusting column names                                                    |
| `refine-ontology`         | Repairing the ontology: wrong join relationships, duplicate concepts, mis-classified identifiers                                                               |
| `query-with-anchor-sql`   | The default for data questions: [Anchor SQL](/api-and-integrations/anchor-sql.md) over concepts — grammar, grains, metrics, and the one-shot error repair loop |
| `write-datafusion-sql`    | Raw SQL against the underlying sources, for what Anchor SQL cannot express: quoting, joins, dates, medians, and the dialect's limits                           |
| `answer-business-metrics` | Hygiene for descriptive numbers: one metric definition, semantics resolved before aggregating, coefficients computed or labelled as assumed                    |

### Causal modelling

| Skill                      | What it walks through                                                                                            |
| -------------------------- | ---------------------------------------------------------------------------------------------------------------- |
| `assess-data-suitability`  | Judging whether the data is sufficient and clean enough to trust a causal result                                 |
| `build-and-validate-twin`  | Building, training, and validating a Digital Twin, including choosing the right twin type                        |
| `configure-twin-discovery` | Discovery and training settings, and injecting domain knowledge: forced edges, treatment and outcome roles, lags |
| `validate-twin`            | Judging a trained twin before trusting it: per-node fit, graph structure, causal chains into the outcome         |

### Simulating

| Skill                  | What it walks through                                                                                                                                  |
| ---------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------ |
| `run-simulation`       | Choosing and configuring the right simulation for the question: intervention, counterfactual, optimisation, forecast, explanation, root cause, anomaly |
| `explore-environments` | Exploring a multi-environment twin: which regions or segments behave alike, how one slice differs, where the model is weakest                          |

### Reporting and building

| Skill               | What it walks through                                                                           |
| ------------------- | ----------------------------------------------------------------------------------------------- |
| `write-report`      | Generating a report from workspace evidence, then revising it                                   |
| `build-chart`       | Configuring each chart type correctly: sankey, histogram, scatter, stacked and multi-series     |
| `build-application` | Building an interactive Application: module wiring, data binding, and the create, run, fix loop |

The catalogue grows with the platform; the live list in your client (`/mcp__rootcause__` autocomplete, or asking the assistant to list its skills) is always current.