Claude-mem mem-search

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.

install

source · Clone the upstream repo

git clone https://github.com/thedotmack/claude-mem

Claude Code · Install into ~/.claude/skills/

T=$(mktemp -d) && git clone --depth=1 https://github.com/thedotmack/claude-mem "$T" && mkdir -p ~/.claude/skills && cp -r "$T/plugin/skills/mem-search" ~/.claude/skills/thedotmack-claude-mem-mem-search && rm -rf "$T"

manifest: plugin/skills/mem-search/SKILL.md

Memory Search

Search past work across all sessions. Simple workflow: search -> filter -> fetch.

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?"

3-Layer 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)

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")

Why This Workflow?

Search index: ~50-100 tokens per result
Full observation: ~500-1000 tokens each
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.