mem-search
- Repo stars 80,444
- Author updated Live
- Author repo claude-mem
- Domain
- Data
- Compatible agents
-
- Claude Code
- Cursor
- Cline
- Codex
- Windsurf
- Gemini CLI
- +20
- Trust score
- 88 / 100 · community maintained
- Author / version / license
- @thedotmack · no license declared
- Token usage
- Lean
- Setup complexity
- Plug-and-play
- External API key
- Not required
- Operating systems
- Unspecified (assume cross-platform)
- Runtime requirements
- No special requirements
- Permissions
-
- Read-only
- Write / modify
- Network behavior
- External requests
- Install commands
- 26 variants
Profile is derived at build time from SKILL.md and install vectors. Subject to drift from author intent.
Heads up: 未限定 allowed-tools,默认拥有全部工具权限。
---
name: mem-search
description: Search claude-mem's persistent cross-session memory database. Use when user asks "did we already…
category: data
runtime: no special runtime
---
# mem-search output preview
## PART A: Task fit
- Use case: 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..
- Inputs: target material, constraints, expected output, and acceptance criteria.
- Evidence boundary: follow “When to Use / 3-Layer Workflow (ALWAYS Follow) / Step 1: Search - Get Index with IDs” and do not present inference as author intent.
## PART B: Execution result
- **01** The card summarizes the use case; runtime output centers on “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.”.
- **02** When the source has headings, the agent prioritizes “When to Use / 3-Layer Workflow (ALWAYS Follow) / Step 1: Search - Get Index with IDs” so the result follows the author’s structure.
- **03** Typical output includes task judgment, concrete steps, required commands or file edits, validation, and follow-up options.
- **04** Risk context follows the fingerprint: read files, write/modify files; may access external network resources; usually needs no extra API key.
## Running Rules
- read files, write/modify files; may access external network resources; usually needs no extra API key.
- Validate with a small sample before expanding scope.
- Return the result, validation criteria, and next iteration options. The source mentions slash commands such as `/knowledge-agent`; use them first when your agent supports command triggers.
Name target files or source material, expected output, forbidden changes, and whether network or shell access is allowed. Permission fingerprint: read files, write/modify files.
Start with a small task and check whether the result follows “When to Use / 3-Layer Workflow (ALWAYS Follow) / Step 1: Search - Get Index with IDs”. Inspect diffs, logs, previews, or tests before expanding scope.
Confirm the final output includes a concrete result, evidence, and next action. If it stays generic, tighten inputs, boundaries, and acceptance criteria.
---
name: mem-search
description: Search claude-mem's persistent cross-session memory database. Use when user asks "did we already…
category: data
source: thedotmack/claude-mem
---
# mem-search
## When to use
- Search claude-mem's persistent cross-session memory database. Use when user asks "did we already solve this?", "how di…
- Use it when the task has clear inputs, repeatable steps, and validation criteria.
## What to provide
- Target material, scope, expected result, and forbidden changes.
- Whether network, commands, file writes, or external services are allowed.
## Execution rules
- Organize steps around “When to Use / 3-Layer Workflow (ALWAYS Follow) / Step 1: Search - Get Index with IDs” and keep inference separate from source facts.
- read files, write/modify files; may access external network resources; usually needs no extra API key.
- Validate with a small sample before expanding the task.
## Output requirements
- Return the deliverable, key evidence, validation method, and next action.
- Mark missing information as unknown; do not invent commands, platforms, or dependencies. The author source anchors workflow facts; repository files anchor sources and commands; Fluxly only adds fit, limitations, and quality judgment.
skill "mem-search" {
input -> user goal + target files + boundaries + acceptance criteria
context -> When to Use / 3-Layer Workflow (ALWAYS Follow) / Step 1: Search - Get Index with IDs
rules -> SKILL.md triggers / order / output contract
runtime -> no special runtime | read files, write/modify files | may access external network resources
guardrails -> usually needs no extra API key + small-sample validation + diff/log review
output -> copyable result + checklist + next iteration
} 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 termlimit(number) - Max results, default 20, max 100project(string) - Project name filtertype(string, optional) - "observations", "sessions", or "prompts"obs_type(string, optional) - Comma-separated: bugfix, feature, decision, discovery, changedateStart(string, optional) - YYYY-MM-DD or epoch msdateEnd(string, optional) - YYYY-MM-DD or epoch msoffset(number, optional) - Skip N resultsorderBy(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 aroundquery(string, optional) - Find anchor automatically if anchor not provideddepth_before(number, optional) - Items before anchor, default 5, max 20depth_after(number, optional) - Items after anchor, default 5, max 20project(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 fetchorderBy(string, optional) - "date_desc" (default), "date_asc"limit(number, optional) - Max observations to returnproject(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.
Decide Fit First
Design Intent
How To Use It
Boundaries And Review