Skill UI

bdistill transforms AI interactions into a structured, queryable knowledge base. It extracts, structures, and quality-scores domain knowledge from both closed-source AI models (in-session) and local open-source models (via Ollama). Users can build comprehensive reference datasets, Q&A pairs, or generate structured training data for traditional machine learning, ensuring validated and exportable domain expertise.

Processes meeting transcripts and recordings to normalize speakers, log metadata, and generate actionable coaching on speaking balance, conflict avoidance, filler usage, and question quality whenever users reference their meetings or tools like Otter or Zoom.

Crafts polished internal communications by matching request types (3P updates, newsletters, FAQs, etc.) to reference templates, gathering context, confirming scope, and producing concise, audience-aware drafts with metrics and links.

This skill applies behavioral economics, specifically prospect theory, to frame messages emphasizing the cost of inaction (loss) rather than just the benefits of action (gain). It guides the creation of persuasive copy, UX copy, and pricing strategies by setting credible reference points and quantifying the missed opportunity. The goal is to ethically leverage the psychological pain of delay, ensuring the approach remains credible and avoids manipulative fear-mongering.

Generates a precise, evidence-based installation plan for a project's component catalog (ECC). Instead of loading a full, noisy bundle, this skill analyzes the repository's actual code structure, dependencies, and CI/CD evidence to classify skills, rules, and commands into 'DAILY' (essential) and 'LIBRARY' (referenceable) buckets. It ensures only necessary components are active, optimizing the development environment.

Presents modal dialogs for asking clarifying questions, preferences, or confirmations when the user cannot see CLI output, ensuring explicit consent before sensitive or ambiguous actions.

Generates professional documentation for software projects, covering READMEs, API references, architecture guides, inline comments, and changelogs, adapting style and scope to the codebase and user request.

Creates high-fidelity visuals through structured JSON prompts and a Python generator script, handling subjects, styles, references, and technical specs for characters, scenes, or products.

Checks Microsoft SDK code by verifying API signatures, fetching docs, and finding Azure samples before writing or reviewing code that touches .NET or Microsoft APIs.

Example skill that walks through the Anthropic SKILL.md structure, showcasing when to load it, how to author steps, and how to reference bundled resources for skill creation.

This skill provides explicit operations for managing durable OpenViking memories and resources. Users can `recall` past decisions or preferences, `read` linked `viking://` URIs to expand context, or `remember` information for explicit retention. It is essential for maintaining accurate context in advanced AI workflows.

Researcher persona keeps references, notes, and teams coordinated by using Drive folders, Docs summaries, Sheets logs, and Gmail-aware sharing plus workflow announcements for structured collaboration.