Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Generates AI summaries of conversations after silence, extracting entities, action items, and relationships for searchable meeting notes and context retrieval.
Generates AI summaries of conversations after silence, extracting entities, action items, and relationships for searchable meeting notes and context retrieval.
Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.
I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Tell me what you changed and call out any manual steps you could not complete.
I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Summarize what changed and any follow-up checks I should run.
Automatic conversation summaries with entity extraction and relationship mapping.
When a conversation ends (60 seconds of silence), Percept generates an AI-powered summary with extracted entities (people, companies, topics), action items, and relationship connections. Summaries are stored locally and searchable.
User asks "what did we talk about?" or "summarize that meeting" User wants meeting notes or action items from a conversation Agent needs context from a recent conversation
percept-listen skill installed and running OpenClaw agent accessible via CLI (used for LLM summarization)
Conversation ends (60s silence timeout) Percept builds a speaker-tagged transcript Sends transcript to OpenClaw for AI summarization Extracts entities (people, orgs, topics) and relationships Stores summary + entities in SQLite Entities linked via relationship graph (works_on, client_of, mentioned_with)
5-tier cascade for identifying entities: Exact match (confidence 1.0) Fuzzy match (0.8) โ handles typos, nicknames Contextual/graph (0.7) โ uses relationship connections Recency (0.6) โ recently mentioned entities ranked higher Semantic search (0.5) โ vector similarity via LanceDB
Summaries are searchable via the Percept dashboard (port 8960) or SQLite directly: SELECT * FROM conversations WHERE summary LIKE '%action items%' ORDER BY end_time DESC; Full-text search via FTS5: SELECT * FROM utterances_fts WHERE utterances_fts MATCH 'project deadline';
Utterances: 30 days Summaries: 90 days Relationships: 180 days Speaker profiles: never expire
GitHub: https://github.com/GetPercept/percept
Code helpers, APIs, CLIs, browser automation, testing, and developer operations.
Largest current source with strong distribution and engagement signals.