# Exploration Aware Evidence Collection

> Skill: exploration-aware-evidence-collection

- Skill: `dingxingdi/exploration-aware-evidence-collection` (Agent Skill, multi-file: 5 files)
- Install (CLI): `npx skillmds@latest add dingxingdi/exploration-aware-evidence-collection`
- Raw SKILL.md: https://api.skillmd.com/api/skills/dingxingdi/exploration-aware-evidence-collection/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/exploration-aware-evidence-collection

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# Skill: exploration-aware-evidence-collection

## 1. Capability Definition & Real Case
* **Professional Definition**: This capability measures whether an embodied agent can plan and execute an exploration trajectory that collects sufficient task-relevant evidence before answering an open-ended question. It emphasizes grounding, evidence sufficiency, and the alignment between what the agent observed and what it claims in the final answer.
* **Dimension Hierarchy**: Goal-Directed Exploration->Question-Conditioned Exploration->exploration-aware-evidence-collection

### Real Case
**[Case 1]**
* **Initial Environment**: The agent starts in a home corridor outside the kitchen with only a partial view of cabinets and the sink area. The faucet itself is not visible from the start position.
* **Real Question**: Did I leave the faucet running in the kitchen?
* **Real Trajectory**: The agent walks from the corridor into the kitchen, turns toward the sink, inspects the faucet directly, and stops once the water state is visible.
* **Real Answer**: No, the faucet is turned off.
* **Why this demonstrates the capability**: The answer cannot be grounded from the starting observation. The agent must move to a location where the target state becomes directly observable. This tests exploration quality and evidence gathering rather than prior knowledge.
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**[Case 2]**
* **Initial Environment**: The embodied agent begins near the entryway of an apartment. The living room and TV corner are not fully visible, and the floor lamp is hidden behind a partial wall until the agent enters deeper into the room.
* **Real Question**: Where is the floor lamp in the living room?
* **Real Trajectory**: The agent explores the living room, checks the TV side of the room, identifies the lamp, and records a grounded spatial answer tied to a visible landmark.
* **Real Answer**: It is in the corner of the living room, next to the TV.
* **Why this demonstrates the capability**: The correct answer depends on reaching a vantage point with clear evidence. A generic guess like 'near the couch' would be plausible but ungrounded. The capability is therefore about collecting the right visual proof before responding.
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**[Case 3]**
* **Initial Environment**: The agent starts in a laundry-adjacent hallway and has not yet observed the utility area. Several rooms are reachable from the initial location, including a bathroom and a bedroom.
* **Real Question**: How many washing machines do I have?
* **Real Trajectory**: The agent first identifies the laundry region, enters it, inspects the appliance area, and counts distinct washing machine units before answering.
* **Real Answer**: Only one.
* **Why this demonstrates the capability**: The task is not answerable from commonsense priors because the home could plausibly contain zero, one, or multiple machines. The agent must collect counting evidence from a relevant region. This makes the exploration path itself part of the competence being tested.

## 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`