> ## Documentation Index
> Fetch the complete documentation index at: https://docs.digcrate.app/llms.txt
> Use this file to discover all available pages before exploring further.

# /influence — Influence map

> Trace an artist's musical connections through reviews, collaborations, and co-mentions

The `/influence` command maps an artist's musical connections by checking the influence cache, running discovery if needed, and rendering an `InfluenceChain` component — a narrative timeline with a hero banner, tabbed connection cards, and cited sources.

## Syntax

```
/influence [artist]
```

| Parameter | Description                       |
| --------- | --------------------------------- |
| `artist`  | Artist name to map influences for |

## Example

```
/influence J Dilla
```

## What it produces

The agent outputs an `InfluenceChain` component with:

* A hero banner for the subject artist
* 6–12 connection cards sorted by weight (strongest connections first)
* Tabs for each connection showing context, sonic elements, key works, and sources
* Source citations with direct links from the research

## How connections are discovered

The agent runs in three phases:

<Steps>
  <Step title="Cache check">
    Calls `lookup_influences` to check whether connections have been cached from a previous research session. If 5 or more connections are found, the agent skips to the enrich phase.
  </Step>

  <Step title="Discovery (if cache is thin)">
    Runs a Perplexity web search for `[artist] musical influences collaborations discography` to get a comprehensive overview in one call. If more signal is needed, searches review co-mentions — when two artists are mentioned together in a review, that co-occurrence is treated as an influence signal.
  </Step>

  <Step title="Enrichment">
    Calls `research_influences_batch` with the top 3–5 connection names. This fires parallel Perplexity calls and returns all results in one response. Results are then saved back to the cache.
  </Step>
</Steps>

## Connection fields

Each connection in the `InfluenceChain` includes:

| Field                  | Description                                                    |
| ---------------------- | -------------------------------------------------------------- |
| `name`                 | Connected artist name                                          |
| `weight`               | Connection strength (0–1), used for sort order                 |
| `relationship`         | `influenced by`, `collaborated with`, or `co_mention`          |
| `context`              | Enriched paragraph or summary from research                    |
| `pullQuote`            | Direct quote if found (optional)                               |
| `pullQuoteAttribution` | `Artist Name, Publication, Year` (optional)                    |
| `sonicElements`        | Array of sonic or stylistic qualities shared between artists   |
| `keyWorks`             | Album-to-album reference showing the connection in the catalog |
| `sources`              | Array of `{ name, url, snippet, date }` from research          |

## Direction convention

Digcrate uses the Badillo-Goicoechea 2025 direction convention for edges in the influence graph:

* `from` = **INFLUENCER**, `to` = **INFLUENCED**
* If a review of artist B mentions artist A → edge points A → B
* `influenced by` for directional influence
* `collaborated with` for production or co-creation
* `co_mention` for same-review co-occurrence without a clear directional signal

<Note>
  Artist images are auto-fetched from Spotify by the `InfluenceChain` component. The agent does not need to look up artwork — passing image URLs is not required.
</Note>

## Related commands

<CardGroup cols={2}>
  <Card title="/artist" icon="user-music" href="/commands/artist">
    Full artist profile including the Connections tab
  </Card>

  <Card title="/story" icon="book-open" href="/commands/story">
    Narrative deep dive on an artist, genre, or movement
  </Card>

  <Card title="/track" icon="music" href="/commands/track">
    Track-level research including credits and samples
  </Card>
</CardGroup>

<Tip>
  Pro users benefit from influence caching — previously discovered connections load instantly and skip the discovery phase entirely.
</Tip>