Upload files or raw text into a Senso knowledge base. Handles file upload via presigned URLs, raw text/markdown creation, folder targeting, and processing status polling. Use when the user wants to add documents, files, or text content to their Senso KB.
Browse, organize, and manage the Senso knowledge base folder tree. Create folders, move and rename nodes, view content details, download files, and check sync status. Use when the user wants to structure, reorganize, or explore their KB.
Manage the Senso content verification and publishing workflow. List items awaiting review, approve or reject content versions, publish to external destinations, save drafts, manage content ownership, and unpublish content. Use when the user wants to review, approve, reject, or publish generated content.
Search a Senso knowledge base for verified answers, context chunks, or content IDs. Use when the user asks a question that should be grounded in organizational knowledge, says "check Senso", or needs factual answers backed by verified documents.
Generate brand-aligned content using the Senso content engine. Orchestrates brand kit verification, content type selection, prompt management, and generation runs. Use when the user wants to generate content like blog posts, FAQs, comparison pages, or any templated output from their knowledge base.
Configure a Senso organization's brand kit (voice, tone, persona, writing rules) and content type templates. Use when the user wants to set up or update their brand identity, create content output formats, or prepare for content generation.
Build a complete, self-healing knowledge base for any company using Senso
Voice AI agent skills and MCP tools for Bland AI. Make phone calls, manage personas, knowledge bases, pathways, and more. Use when the user wants to build, test, or manage AI-powered phone agents.
Builds an autonomous iterative research loop that narrows a wide uncertainty/exposure range toward a user-defined target by issuing Google searches, extracting evidence from results, re-estimating the range from accumulated evidence, and repeating until converged. Use when the user wants to implement autonomous research that progressively reduces uncertainty toward a quantified goal — risk analysis, market sizing, due diligence, literature review, or any domain where a wide estimate must be narrowed with real evidence.
Search your Senso.ai knowledge base hands-free through Edith smart glasses. Triggers on knowledge/document queries.
Set up Edith smart glasses as an OpenClaw channel. Run this when the user wants to connect their smart glasses to OpenClaw, mentions "Edith glasses", or provides a link code for glasses setup.
Build a complete, self-healing knowledge base for any company using Senso
Autonomous incident response and self-healing codebase agent. Use when building SRE automation, incident pipelines, error detection, auto-remediation, or production monitoring systems. Covers the full lifecycle from error ingestion to diagnosis, fix generation, approval gating, phone escalation, and deployment.
Use when securing Fastify API endpoints with JWT Bearer token validation, scope/permission checks, or stateless auth - integrates @auth0/auth0-fastify-api for REST APIs receiving access tokens from frontends or mobile apps.
Use when adding authentication to Angular applications with route guards and HTTP interceptors - integrates @auth0/auth0-angular SDK for SPAs
Work on the liquor_store_agentic_monitor hackathon demo — agentic multimodal retail POS monitoring (mock CV/ASR + POS fusion), Streamlit UI, policy tuning, segments JSON, webhooks, deploy.
Add voice AI to an app — inbound/outbound phone calls, voice agents, or speech-to-text/text-to-speech pipelines. Supports Bland AI, ElevenLabs, Deepgram, and Web Speech API. Use when the user says "add voice", "make it speak", "phone call feature", or "voice agent".
Autonomous product feedback monitoring agent. Ingests signals from GitHub, reviews, Slack. Learns what matters to you. Calls you when it's critical. Gets smarter with every interaction.
Guides agents through autonomous ManiSkill and VSLAM evaluation, tuning, verification, memory storage, and summary writing using the Autolab MCP server and repo tooling.
Evaluate, score, and systematically improve prompts in the codebase. Identifies weak prompts, generates test cases, scores outputs, and proposes optimized versions. Use when the user says "improve this prompt", "why is the AI doing X", "eval my prompts", or "optimize the agent".