> For the complete documentation index, see [llms.txt](https://syv-labs.gitbook.io/context-pool/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://syv-labs.gitbook.io/context-pool/get-started/first-insights.md).

# Your First Insights

## What Gets Extracted

ContextPool reads your chat transcripts and asks an LLM to extract the moments that are actually worth remembering. Not summaries of conversations — specific, actionable engineering insights.

Each insight has a **type**:

| Type       | What it captures                                         |
| ---------- | -------------------------------------------------------- |
| `bug`      | A specific bug found — what it was, what caused it       |
| `fix`      | The solution applied — the exact change that resolved it |
| `decision` | An architectural or design choice made and why           |
| `pattern`  | A reusable approach or convention established            |
| `gotcha`   | A non-obvious pitfall that took time to discover         |

***

## Example Output

Given a session where you debugged a Rust build failure, `cxp` might extract:

```markdown
- **gotcha** reqwest needs `rustls-tls` feature on Alpine/ARM — default OpenSSL fails to link
- **decision** switched from `tokio::spawn` to `rayon` for CPU-bound extraction — avoids blocking the async runtime
- **fix** added `CARGO_NET_GIT_FETCH_WITH_CLI=true` to fix private crate auth in CI
```

These are stored in a `.summary.md` file and indexed by the MCP server. Your agent can retrieve them with `get_project_context` or find them by keyword with `search_context`.

***

## Getting Higher Quality Insights

**Have longer, more specific conversations.** Short back-and-forth chats produce fewer insights. Sessions where you work through a real problem — debugging, designing, refactoring — tend to produce the most valuable memory.

**Name things explicitly in chat.** If you say "let's use a queue here instead of polling", that's a decision worth remembering. The more explicit the reasoning in your session, the better the extraction.

**Run `cxp init` after sessions, not just before.** Index new sessions regularly so your memory stays current. The MCP `fetch_project_context` tool does this automatically — but running `cxp init` manually from the CLI is faster for bulk historical imports.

***

## What Gets Filtered Out

The extraction is aggressive about dropping noise:

* Tool call results and file contents injected by the IDE
* Generic explanations ("here's how async/await works...")
* Exploratory chat with no concrete outcome
* Thinking blocks and internal reasoning traces
* Lines longer than 500 characters (usually raw data)

Only what's worth telling a future engineer — or a future agent — makes it through.

***

## Sharing With Your Team

Commit your `ContextPool/` directory to git. Your teammates will automatically have your insights the next time they clone or pull:

```bash
git add ContextPool/
git commit -m "chore: add contextpool memory"
```

Or use [Team Sync](/context-pool/team-sync/overview.md) for automatic cloud sharing across your whole engineering team.


---

# Agent Instructions
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## Querying This Documentation
If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://syv-labs.gitbook.io/context-pool/get-started/first-insights.md?ask=<question>
```

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The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.