# Long Horizon Information Recall And Personalization

> Skill: long-horizon-information-recall-and-personalization

- Skill: `dingxingdi/long-horizon-information-recall-and-personalization-2` (Agent Skill, multi-file: 5 files)
- Install (CLI): `npx skillmds@latest add dingxingdi/long-horizon-information-recall-and-personalization-2`
- Raw SKILL.md: https://api.skillmd.com/api/skills/dingxingdi/long-horizon-information-recall-and-personalization-2/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/long-horizon-information-recall-and-personalization-2

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# Skill: long-horizon-information-recall-and-personalization
## 1. Capability Definition & Real Case
* **Professional Definition**: The ability to recall user- or assistant-provided details from distant prior interactions and use them accurately to answer factual questions or personalize later responses.
* **Dimension Hierarchy**: Conversational Memory->Persistent Personal Memory->long-horizon-information-recall-and-personalization
### Real Case

**[Case 1]**
* **Initial Environment**: The assistant has accumulated dozens of prior user-assistant sessions spanning work, travel, food, and photography. Relevant memory items are buried in older conversations rather than repeated in the current session.
* **Real Question**: Can you remind me of the romantic restaurant in Rome you recommended for dinner?
* **Real Trajectory**: 1. Search prior sessions for recommendation history tied to Rome and romantic dinner context. 2. Distinguish assistant-provided information from user-provided preferences. 3. Retrieve the specific restaurant name rather than paraphrasing the recommendation broadly. 4. Answer directly and, if appropriate, lightly personalize the response using the earlier preference context.
* **Real Answer**: Roscioli.
* **Why this demonstrates the capability**: This tests whether the assistant can recover a distant assistant-side memory item and use it in a user-facing way. The challenge is both recall accuracy and the ability to turn retrieved history into a personalized, context-aware reply without hallucinating adjacent details.

## 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`