A sophisticated context-retrieval skill that empowers AI agents to search, synthesize, and apply insights from past conversations to solve current problems.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install remembering-conversations
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install remembering-conversations using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Remembering Conversations?
The Remembering Conversations skill provides a robust episodic memory layer for AI coding agents, ensuring that valuable historical context is never lost. By leveraging Openclaw Skills, agents can transition from simple pattern matching to informed decision-making based on previous architectural choices, debugging sessions, and user preferences. Instead of forcing the agent to process massive amounts of raw chat history, this skill utilizes a specialized search agent to find and summarize the most relevant information.
This approach is designed to save significant context window space—often between 50 to 100 times more efficient than traditional methods. By integrating this into your workflow through Openclaw Skills, you ensure that the agent remains highly performant while maintaining a deep understanding of the project's evolution and the rationale behind specific code implementations.
Remembering Conversations Use Cases
- Retrieving the reasoning behind past architectural decisions when the user asks "why did we do this?"
- Finding specific code examples or implementation patterns established in earlier sessions.
- Troubleshooting complex issues by searching for similar "gotchas" or errors encountered previously.
- Following established but unfamiliar workflows that were documented in past interactions.
- Accelerating the development process by avoiding the re-implementation of logic that was already discussed.
How Remembering Conversations Works
- The primary agent identifies a trigger, such as a user referencing past work or reaching a technical impasse.
- The agent dispatches a specialized search-conversations subagent using the Task tool.
- The subagent performs a targeted search across the episodic memory database to find relevant historical entries.
- The subagent reads the top 2 to 5 search results to gather detailed context.
- A synthesis process converts the raw history into an actionable report ranging from 200 to 1000 words.
- The refined insights and original sources are returned to the primary agent to inform the current task.
Remembering Conversations Setup
To utilize this skill within the Openclaw Skills ecosystem, ensure the episodic-memory MCP plugin is active in your environment. No manual installation of the search agent is required, as it is dispatched dynamically.
# The skill is triggered via the Task tool using the following configuration:
# subagent_type: "search-conversations"
When a search is needed, the agent should announce the dispatch and provide a specific prompt focusing on decisions, patterns, or code examples.
Remembering Conversations Data Schema & Taxonomy
The Remembering Conversations skill organizes data through a structured retrieval and synthesis pipeline:
| Component | Function |
|---|---|
| search tool | Queries the episodic memory for keywords and topics. |
| show tool | Loads specific conversation content for detailed analysis. |
| subagent_type | Identifies the specialized agent as search-conversations. |
| Synthesis Output | A Markdown-formatted report containing findings and sources. |
Remembering Conversations Advanced Features
- Context Window Optimization: Reduces token usage by 50-100x compared to raw history loading.
- Targeted Synthesis: Filters out noise to provide only the most relevant decisions and code snippets.
- Multi-Agent Orchestration: Uses a dedicated subagent to handle intensive search tasks without cluttering the main agent's focus.
- Source Tracking: Provides clear references to past conversations for manual verification if needed.
- Workflow Continuity: Maintains project momentum by bridging the gap between disconnected sessions via Openclaw Skills.
SKILL.md
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