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.
The English summary is being prepared. Showing the original description.
See how to installReview the instructions the agent will receive before you install.
Search past work across all sessions. Simple workflow: search -> filter -> fetch.
Use when users ask about PREVIOUS sessions (not current conversation):
NEVER fetch full details without filtering first. 10x token savings.
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"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 filterReview 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 filterReturns: Complete observation objects with title, subtitle, narrative, facts, concepts, files (~500-1000 tokens each)
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")
No reviews yet. Tell others what you thought.
No login needed. Share how it worked for you, ask a question, or note where it did not run.
No comments yet. Be the first to write one.
Set up and use 1Password CLI (op). Use when installing the CLI, enabling desktop app integration, signing in, and reading/injecting secrets for commands.
The English summary is being prepared. Showing the original description.
Use this skill when the user requests to review, analyze, critique, or summarize academic papers, research articles, preprints, or scientific publications. Supports comprehensive structured reviews covering methodology assessment, contribution evaluation, literature positioning, and constructive feedback generation. Trigger on queries involving paper URLs, uploaded PDFs, arXiv links, or requests like "review this paper", "analyze this research", "summarize this study", or "write a peer review".
The English summary is being prepared. Showing the original description.
Add descriptions for new models from the HuggingFace router to chat-ui configuration. Use when new models are released on the router and need descriptions added to prod.yaml and dev.yaml. Triggers on requests like "add new model descriptions", "update models from router", "sync models", or when explicitly invoking /add-model-descriptions.
The English summary is being prepared. Showing the original description.
Automates browser interactions for web testing, form filling, screenshots, and data extraction. Use when the user needs to navigate websites, interact with web pages, fill forms, take screenshots, test web applications, or extract information from web pages.
The English summary is being prepared. Showing the original description.
Master advanced AgentDB features including QUIC synchronization, multi-database management, custom distance metrics, hybrid search, and distributed systems integration. Use when building distributed AI systems, multi-agent coordination, or advanced vector search applications.
The English summary is being prepared. Showing the original description.
Create and train AI learning plugins with AgentDB's 9 reinforcement learning algorithms. Includes Decision Transformer, Q-Learning, SARSA, Actor-Critic, and more. Use when building self-learning agents, implementing RL, or optimizing agent behavior through experience.
The English summary is being prepared. Showing the original description.