claude-memSkill

---
name: mem-search
description: Search claude-mem's persistent cross-session memory database. Use when user asks "did we already solve this?", "how did we do X last time?", or needs work from previous sessions.
---

# Memory Search

Search past work across all sessions. Simple workflow: search -> filter -> fetch -> (rarely) disclose raw tool I/O.

## When to Use

Use when users ask about PREVIOUS sessions (not current conversation):

- "Did we already fix this?"
- "How did we solve X last time?"
- "What happened last week?"

## Layered Workflow (ALWAYS Follow)

**NEVER fetch full details without filtering first. 10x token savings.**

### Step 1: Search - Get Index with IDs

Use the `search` MCP tool:

```
search(query="authentication", limit=20, project="my-project")
```

**Returns:** Table with IDs, timestamps, types, titles (~50-100 tokens/result)

```
| ID | Time | T | Title | Read |
|----|------|---|-------|------|
| #11131 | 3:48 PM | 🟣 | Added JWT authentication | ~75 |
| #10942 | 2:15 PM | 🔴 | Fixed auth token expiration | ~50 |
```

**Parameters:**

- `query` (string) - Search term
- `limit` (number) - Max results, default 20, max 100
- `project` (string) - Project name filter
- `type` (string, optional) - "observations", "sessions", or "prompts"
- `obs_type` (string, optional) - Comma-separated: bugfix, feature, decision, discovery, change
- `dateStart` (string, optional) - YYYY-MM-DD or epoch ms
- `dateEnd` (string, optional) - YYYY-MM-DD or epoch ms
- `offset` (number, optional) - Skip N results
- `orderBy` (string, optional) - "date_desc" (default), "date_asc", "relevance"

### Step 2: Timeline - Get Context Around Interesting Results

Use the `timeline` MCP tool:

```
timeline(anchor=11131, depth_before=3, depth_after=3, project="my-project")
```

Or find anchor automatically from query:

```
timeline(query="authentication", depth_before=3, depth_after=3, project="my-project")
```

**Returns:** `depth_before + 1 + depth_after` items in chronological order with observations, sessions, and prompts interleaved around the anchor.

**Parameters:**

- `anchor` (number, optional) - Observation ID to center around
- `query` (string, optional) - Find anchor automatically if anchor not provided
- `depth_before` (number, optional) - Items before anchor, default 5, max 20
- `depth_after` (number, optional) - Items after anchor, default 5, max 20
- `project` (string) - Project name filter

### Step 3: Fetch - Get Full Details ONLY for Filtered IDs

Review titles from Step 1 and context from Step 2. Pick relevant IDs. Discard the rest.

Use the `get_observations` MCP tool:

```
get_observations(ids=[11131, 10942])
```

**ALWAYS use `get_observations` for 2+ observations - single request vs N requests.**

**Parameters:**

- `ids` (array of numbers, required) - Observation IDs to fetch
- `orderBy` (string, optional) - "date_desc" (default), "date_asc"
- `limit` (number, optional) - Max observations to return
- `project` (string, optional) - Project name filter

**Returns:** Complete observation objects with title, subtitle, narrative, facts, concepts, files (~500-1000 tokens each)

### Step 4: Disclose Raw Tool I/O - Only When Step 3 Was Not Enough

Observations are *summaries*. When the answer needs the literal bytes a tool
returned — the exact diff, the exact command output, the exact API response —
use the `get_tool_uses` MCP tool:

```
get_tool_uses(ids=["toolu_01ABC..."], project="my-project")
```

**Do not start here.** Raw tool bodies are unsummarized and can run to thousands
of tokens each; that is the whole reason claude-mem compresses them into
observations in the first place. Reach for this layer only after search /
timeline / get_observations pointed you at specific tool calls.

**Parameters:**

- `ids` (array, required) - Numeric `tool_uses` ids OR opaque `tool_use_id` strings
- `limit` (number, optional) - Max rows to return
- `project` (string, optional) - Project name filter
- `contentSessionId` (string, optional) - Restrict to one session

**Returns:** The stored `tool_input` / `tool_response` for those calls, plus the
tool name, session ids, and the observation each was folded into. Payloads over
64 KB were truncated on write and carry a `…[truncated: N bytes]` marker.

## Examples

**Find recent bug fixes:**

```
search(query="bug", type="observations", obs_type="bugfix", limit=20, project="my-project")
```

**Find what happened last week:**

```
search(type="observations", dateStart="2025-11-11", limit=20, project="my-project")
```

**Understand context around a discovery:**

```
timeline(anchor=11131, depth_before=5, depth_after=5, project="my-project")
```

**Batch fetch details:**

```
get_observations(ids=[11131, 10942, 10855], orderBy="date_desc")
```

**Recover the exact output of a command we ran last week:**

```
search(query="migration failed", limit=20, project="my-project")
get_observations(ids=[11131])            # read the summary first
get_tool_uses(ids=["toolu_01ABC..."])    # only if the summary omitted the detail
```

## Why This Workflow?

- **Search index:** ~50-100 tokens per result
- **Full observation:** ~500-1000 tokens each
- **Raw tool body:** up to 64 KB each — the layer you skip 95% of the time
- **Batch fetch:** 1 HTTP request vs N individual requests
- **10x token savings** by filtering before fetching

## Knowledge Agents

Want synthesized answers instead of raw records? Use `/knowledge-agent` to build a queryable corpus from your observation history. The knowledge agent reads all matching observations and answers questions conversationally.