claude-mem/plugin/skills/mem-search/SKILL.md

name, description

name	description
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.

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

Smart-Explore Language Support

Smart-explore tools (smart_search, smart_outline, smart_unfold) use tree-sitter AST parsing. The following languages are supported out of the box.

24 Bundled Languages

JS, TS, Python, Go, Rust, Ruby, Java, C, C++, Kotlin, Swift, PHP, Elixir, Lua, Scala, Bash, Haskell, Zig, CSS, SCSS, TOML, YAML, SQL, Markdown

Markdown Special Support

Markdown files get structure-aware parsing beyond generic tree-sitter:

• Heading hierarchy -- #/##/### headings are extracted as nested symbols (sections contain subsections)
• Code block detection -- fenced code blocks are surfaced as code symbols with language annotation
• Section-aware unfold -- smart_unfold on a heading returns the full section content (heading through all subsections until the next heading of equal or higher level)

User-Installable Grammars via .claude-mem.json

Add custom tree-sitter grammars for languages not in the bundled set. Place .claude-mem.json in the project root:

{
  "grammars": {
    "gleam": {
      "package": "tree-sitter-gleam",
      "extensions": [".gleam"]
    },
    "protobuf": {
      "package": "tree-sitter-proto",
      "extensions": [".proto"],
      "query": ".claude-mem/queries/proto.scm"
    }
  }
}

Fields:

• package (string, required) -- npm package name for the tree-sitter grammar
• extensions (array of strings, required) -- file extensions to associate with this language
• query (string, optional) -- path to a custom .scm query file for symbol extraction. If omitted, a generic query is used.

Rules:

• User grammars do NOT override bundled languages. If a language is already bundled, the entry is ignored.
• The npm package must be installed in the project (npm install tree-sitter-gleam).
• Config is cached per project root. Changes to .claude-mem.json take effect on next worker restart.

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.