# Data Sufficiency Evaluation And Unanswerability Detection

> Skill: data sufficiency evaluation and unanswerability detection

- Skill: `dingxingdi/data-sufficiency-evaluation-and-unanswerability-detection` (Agent Skill, multi-file: 5 files)
- Install (CLI): `npx skillmds@latest add dingxingdi/data-sufficiency-evaluation-and-unanswerability-detection`
- Raw SKILL.md: https://api.skillmd.com/api/skills/dingxingdi/data-sufficiency-evaluation-and-unanswerability-detection/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- Author: dingxingdi (https://skillmd.com/u/dingxingdi)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/dingxingdi/data-sufficiency-evaluation-and-unanswerability-detection

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# Skill: data sufficiency evaluation and unanswerability detection

## 1. Capability Definition & Real Case
* **Professional Definition**: This capability involves the systematic evaluation of data environments to determine if they contain the necessary variables, cohorts, and granularity to support a specific analytical request. It emphasizes non-verifiability detection, where the agent must critically identify structural data absences and confidently declare the task unanswerable, thereby avoiding hallucinations or proxy-based guessing.
* **Dimension Hierarchy**: Data Grounding->Data Retrieval & Semantic Grounding->data sufficiency evaluation and unanswerability detection

### Real Case
**[Case 1]**
* **Initial Environment**: A data sandbox contains clinical archives for cancer patients but lacks mutational burden records or labels mapping the exact locations of distant metastases.
* **Real Question**: Do patients with brain metastases have a significantly higher mutational burden compared to those with non-brain metastases?
* **Real Trajectory**: Inspect the clinical tables for columns related to 'brain_metastasis' or 'mutational_burden'; identify that while the patient cohort exists, there is no column defining the location of metastases or the mutational burden metric; instead of attempting a guess, state that the dataset is missing the critical grouping and outcome variables; conclude the request is unanswerable.
* **Real Answer**: Unanswerable. The dataset lacks mutational burden records and brain metastasis location labels.
* **Why this demonstrates the capability**: The agent avoids the common error of misidentifying nearest-neighbor columns or hallucinating a result, correctly identifying that the specific comparative evidence required for the analytical request is physically absent from the environment.
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**[Case 2]**
* **Initial Environment**: An e-commerce dataset containing product names, categories, and review scores, but entirely missing any sales volume, quantity, or revenue metrics.
* **Real Question**: Which product category generated the highest total revenue in Q3 2023?
* **Real Trajectory**: Start searching for revenue, sales, or price/quantity columns; read the headers and schemas of all available tables; observe that only review and product metadata information is present; halt the analysis and declare that revenue cannot be calculated without sales volume or unit price data.
* **Real Answer**: Unanswerable due to missing revenue and sales data.
* **Why this demonstrates the capability**: The question explicitly asks for a metric that requires data structurally unavailable in the table. The agent must successfully evaluate the schema and recognize the insufficiency before confidently refusing to compute a number, directly testing capability of unanswerability detection.

## Pipeline Execution Instructions
To synthesize data for this capability, you must strictly follow a 3-phase pipeline. **Do not hallucinate steps.** Read the corresponding reference file for each phase sequentially:

1. **Phase 1: Environment Exploration**
   Read the exploration guidelines to discover raw knowledge seeds:
   `references/EXPLORATION.md`

2. **Phase 2: Trajectory Selection**
   Once Phase 1 is complete, read the selection criteria to evaluate the trajectory:
   `references/SELECTION.md`

3. **Phase 3: Data Synthesis**
   Once a trajectory passes Phase 2, read the synthesis instructions to generate the final data:
   `references/SYNTHESIS.md`