Rank Volatility Monitor

What it does

Track and analyze brand rank instability and volatility over multiple snapshot periods, calculating standard deviations, disappearance rates, and mean absolute rank changes.

Execution Contract

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. rank-volatility-monitor.json).
  • A premium, beautifully styled markdown report must be saved to [skill_name].md (e.g. rank-volatility-monitor.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:

• updatedAt (string, ISO-8601 timestamp of snapshot update)
• 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. Rank Series Construction: Build a chronological rank series for each unique brand + keyword combination across all snapshot periods sorted by updatedAt.
2. Instability Metrics Calculation:
   • Standard Deviation of Rank (stdDevRank): Calculate the standard deviation of rankings across the series.
   • Disappearance Rate (disappearanceRate): Calculate the frequency with which a brand disappears from the top rankings after having been present: $$\text{disappearanceRate} = \frac{\text{periods absent}}{\text{total periods after first appearance}}$$
   • Mean Absolute Rank Change (meanAbsoluteRankChange): Calculate the average absolute difference between consecutive rank positions: $$\text{meanAbsoluteRankChange} = \frac{\sum | \text{rank}t - \text{rank}{t-1} |}{N-1}$$
3. Volatility Scoring: Combine these metrics to calculate a single normalized volatility score from 0 to 100 (where 100 represents maximum instability/volatility).

Expected Output

The skill must generate two outputs in the local workspace:

1. rank-volatility-monitor.json: Contains the raw structured analytical output, including the execution contract metadata, per-brand volatility calculations, anomaly alerts, confidence metrics, and the evidence map.

2. rank-volatility-monitor.md: A premium, beautiful human-readable report. This report must contain:

   • Volatility Leaderboard: Summary table showing brand names, keywords, calculated standard deviation, disappearance rate, mean absolute rank change, and the final Volatility Score (0-100).
   • Anomaly Alerts: Clear warnings or callouts highlighting brand-keyword pairs exhibiting unusually high or sudden volatility shifts.
   • 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

• Minimum Time Points Constraint: Stable calculation requires a minimum of 3 unique chronological time points (snapshots). If the dataset contains fewer than 3 time points, emit a prominent warning regarding insufficient historical depth and lower the confidence score accordingly.
• No Silent Interpolation: Do not silently interpolate missing periods or rank positions. Any gaps or assumed values must be explicitly declared and highlighted as a limitation.
• No Web Lookups: Do not perform external web lookups or enrichment of brand data.
• No Hallucination: Do not invent brands, rankings, keywords, or timestamps that are not present in the ingested dataset.

Copy-ready Skill Prompt

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

Skill: Rank Volatility Monitor
Goal: Track rank instability across multiple snapshot pulls.

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. `rank-volatility-monitor.json`).
  - A premium, beautifully styled markdown report must be saved to `[skill_name].md` (e.g. `rank-volatility-monitor.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.

The input dataset from the API/file must map to these fields:
- `updatedAt` (string, ISO-8601 timestamp of snapshot update)
- `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. **Rank Series Construction**: Build a chronological rank series for each unique `brand` + `keyword` combination across all snapshot periods sorted by `updatedAt`.
2. **Instability Metrics Calculation**:
   - **Standard Deviation of Rank (`stdDevRank`)**: Calculate the standard deviation of rankings across the series.
   - **Disappearance Rate (`disappearanceRate`)**: Calculate the frequency with which a brand disappears from the top rankings after having been present:
     $$\text{disappearanceRate} = \frac{\text{periods absent}}{\text{total periods after first appearance}}$$
   - **Mean Absolute Rank Change (`meanAbsoluteRankChange`)**: Calculate the average absolute difference between consecutive rank positions:
     $$\text{meanAbsoluteRankChange} = \frac{\sum | \text{rank}_t - \text{rank}_{t-1} |}{N-1}$$
3. **Volatility Scoring**: Combine these metrics to calculate a single normalized `volatility score` from 0 to 100 (where 100 represents maximum instability/volatility).

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

1. **`rank-volatility-monitor.json`**:
   Contains the raw structured analytical output, including the execution contract metadata, per-brand volatility calculations, anomaly alerts, confidence metrics, and the evidence map.

2. **`rank-volatility-monitor.md`**:
   A premium, beautiful human-readable report. This report must contain:
   - **Volatility Leaderboard**: Summary table showing brand names, keywords, calculated standard deviation, disappearance rate, mean absolute rank change, and the final Volatility Score (0-100).
   - **Anomaly Alerts**: Clear warnings or callouts highlighting brand-keyword pairs exhibiting unusually high or sudden volatility shifts.
   - **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:
- **Minimum Time Points Constraint**: Stable calculation requires a minimum of 3 unique chronological time points (snapshots). If the dataset contains fewer than 3 time points, emit a prominent warning regarding insufficient historical depth and lower the confidence score accordingly.
- **No Silent Interpolation**: Do not silently interpolate missing periods or rank positions. Any gaps or assumed values must be explicitly declared and highlighted as a limitation.
- **No Web Lookups**: Do not perform external web lookups or enrichment of brand data.
- **No Hallucination**: Do not invent brands, rankings, keywords, or timestamps that are not present in the ingested dataset.