---
title: "Share Of Voice Snapshot"
description: "Compute brand SOV using latest FullMention result snapshots."
order: 1
featured: false
---

   
                                
                                                                           
        
   
# Share Of Voice Snapshot

## What it does

Compute brand Share of Voice (SOV) metrics based on the latest result snapshots fetched from the FullMention API, and persist analytical outputs locally.

## Execution Contract

```yaml
Every execution of this skill must operate under the following contract:
- **ingestion_plan**: A documented plan for pulling data.
- **max_api_calls**: 3 (default, strictly enforced).
- **cache_key**: A unique key identifying the cached API dataset.
- **dataset_timestamp**: ISO timestamp of the ingested dataset.
- **analysis_mode**: `offline_only`
```

## Data Access Policy

- **API Target**: Consume data from the FullMention API at `GET /v2/runs/{runId}`.
- **Controlled Ingestion**: Perform exactly one controlled ingestion pull from the FullMention API. Paginated batch fetching is preferred.
- **API Decoupling**: Do NOT treat the FullMention API as a persistent database or state-store; it is a read-only snapshot provider.
- **24-Hour TTL**: FullMention v2 deletes run data after 24 hours, meaning offline persistence/database caching is a strict requirement for historical tracking.
- **Local Persistence**: Save all analytical outputs locally in the current workspace directory.
  - Raw structured JSON must be saved to `[skill_name].json` (e.g. `share-of-voice-snapshot.json`).
  - A premium, beautifully styled markdown report must be saved to `[skill_name].md` (e.g. `share-of-voice-snapshot.md`).
- **Caching**: Reuse the same stored dataset across iterative prompts. Do not repeat identical API calls.
- **Refresh Window**: Make additional API calls only if the user explicitly requests a refresh window or a missing page fetch.
- **Rate Limits & Backoff**: Respect API rate limits and backoff policies. Never run open-ended call loops.
- **Allowed Sources**:
  - Local working dataset produced from one ingestion pull of FullMention API data.
  - Optional user-provided local file/DB snapshot (read-only).
  - No repeated API fetching during analysis.

## Required Input Fields & Parameters

The input dataset from the API/file must map to these fields:
- `id` (string, unique result identifier)
- `keyword` (string, searched keyword)
- `brandRankings[].name` (string, brand name)
- `brandRankings[].position` (integer, brand rank position)

## Analytical Method

Follow these step-by-step logic rules during analysis:
1. **Source Selection**: Use `brandRankings[]` data directly.
2. **Weighting Mentions**: Weight each brand mention by the formula `1 / rank` (e.g., Rank 1 has weight 1.0, Rank 2 has weight 0.5, Rank 3 has weight 0.33, etc.).
3. **Normalization & Aggregation**: Normalize brand names (trim whitespace, convert to lowercase for comparison, but preserve readable casing in output) and aggregate their total weighted scores.
4. **SOV Percentage Calculation**: Convert the aggregated score of each brand to a percentage of the total weighted score of all brands in the dataset.

## Expected Output

The skill must generate two outputs in the local workspace:

1. **`share-of-voice-snapshot.json`**:
   Contains the raw structured analytical output, including the execution contract metadata, the final leaderboard, insights, confidence metrics, and the evidence map.

2. **`share-of-voice-snapshot.md`**:
   A premium, beautiful human-readable report. This report must contain:
   - **Leaderboard**: Formatted table with columns: `Brand | Weighted Score | SOV %`
   - **Top 3 Insights**: Strategic take-aways, brand dominance notes, or market anomalies.
   - **Confidence & Limitations**:
     - A confidence score from 0-100.
     - **Confidence Rationale**: Explanation of how the confidence score was derived.
     - **Limitations**: A list of data limitations or gaps.
   - **Evidence Map**: An array of objects `evidence_map[]` with:
     - `finding_id`
     - `metric_name`
     - `source_field_paths[]`
     - `sample_result_ids[]`

## Guardrails & Constraints

- **No Web Lookups**: Do not perform external web lookups or enrichment of brand data.
- **No Hallucination**: Do not invent brands, rankings, recommendations, or hidden fields that are not present in the ingested dataset.
- **Sample Constraints**: If the ingested dataset contains fewer than 10 results, the confidence score must be significantly lower, and a clear explanation of this limitation must be included in the report.

## Copy-ready Skill Prompt

Use this as a full copy/paste prompt in your AI tool:

```text
Skill: Share Of Voice Snapshot
Goal: Compute brand SOV using latest FullMention result snapshots.

Data Access Policy:
- **API Target**: Consume data from the FullMention API at `GET /v2/runs/{runId}`.
- **Controlled Ingestion**: Perform exactly one controlled ingestion pull from the FullMention API. Paginated batch fetching is preferred.
- **API Decoupling**: Do NOT treat the FullMention API as a persistent database or state-store; it is a read-only snapshot provider.
- **24-Hour TTL**: FullMention v2 deletes run data after 24 hours, meaning offline persistence/database caching is a strict requirement for historical tracking.
- **Local Persistence**: Save all analytical outputs locally in the current workspace directory.
  - Raw structured JSON must be saved to `[skill_name].json` (e.g. `share-of-voice-snapshot.json`).
  - A premium, beautifully styled markdown report must be saved to `[skill_name].md` (e.g. `share-of-voice-snapshot.md`).
- **Caching**: Reuse the same stored dataset across iterative prompts. Do not repeat identical API calls.
- **Refresh Window**: Make additional API calls only if the user explicitly requests a refresh window or a missing page fetch.
- **Rate Limits & Backoff**: Respect API rate limits and backoff policies. Never run open-ended call loops.
- **Allowed Sources**:
  - Local working dataset produced from one ingestion pull of FullMention API data.
  - Optional user-provided local file/DB snapshot (read-only).
  - No repeated API fetching during analysis.

Input Fields & Params:
The input dataset from the API/file must map to these fields:
- `id` (string, unique result identifier)
- `keyword` (string, searched keyword)
- `brandRankings[].name` (string, brand name)
- `brandRankings[].position` (integer, brand rank position)

Method:
Follow these step-by-step logic rules during analysis:
1. **Source Selection**: Use `brandRankings[]` data directly.
2. **Weighting Mentions**: Weight each brand mention by the formula `1 / rank` (e.g., Rank 1 has weight 1.0, Rank 2 has weight 0.5, Rank 3 has weight 0.33, etc.).
3. **Normalization & Aggregation**: Normalize brand names (trim whitespace, convert to lowercase for comparison, but preserve readable casing in output) and aggregate their total weighted scores.
4. **SOV Percentage Calculation**: Convert the aggregated score of each brand to a percentage of the total weighted score of all brands in the dataset.

Expected Output:
The skill must generate two outputs in the local workspace:

1. **`share-of-voice-snapshot.json`**:
   Contains the raw structured analytical output, including the execution contract metadata, the final leaderboard, insights, confidence metrics, and the evidence map.

2. **`share-of-voice-snapshot.md`**:
   A premium, beautiful human-readable report. This report must contain:
   - **Leaderboard**: Formatted table with columns: `Brand | Weighted Score | SOV %`
   - **Top 3 Insights**: Strategic take-aways, brand dominance notes, or market anomalies.
   - **Confidence & Limitations**:
     - A confidence score from 0-100.
     - **Confidence Rationale**: Explanation of how the confidence score was derived.
     - **Limitations**: A list of data limitations or gaps.
   - **Evidence Map**: An array of objects `evidence_map[]` with:
     - `finding_id`
     - `metric_name`
     - `source_field_paths[]`
     - `sample_result_ids[]`

Guardrails:
- **No Web Lookups**: Do not perform external web lookups or enrichment of brand data.
- **No Hallucination**: Do not invent brands, rankings, recommendations, or hidden fields that are not present in the ingested dataset.
- **Sample Constraints**: If the ingested dataset contains fewer than 10 results, the confidence score must be significantly lower, and a clear explanation of this limitation must be included in the report.
```