Multi-platform content distribution across X, LinkedIn, Threads, and Bluesky. Adapts content per platform using content-engine patterns. Never posts identical content cross-platform. Use when the user wants to distribute content across social platforms.

Pin session decisions, questions, objections, scope constraints, and corrections to a persistent board that survives context compaction. Use PROACTIVELY when: (1) user approves/rejects a recommendation, (2) user asks a clarifying question about a proposal, (3) user states a scope constraint, (4) user corrects a misunderstanding. Also use when user says pin, track this, mark as approved, board, show pins. Auto-invoke without user asking whenever a decision, question, or constraint is detected.

Sense-making before action. Classify problem using Cynefin triangulation (3 tests + decomposition) to route to the right skill chain. Use when: frame, what approach, how should I start, which skill, where to begin, unsure what to do. NOT for known tasks — just do them.

Automatic quality control, linting, and static analysis procedures. Use after every code modification to ensure syntax correctness and project standards. Triggers onKeywords: lint, format, check, validate, types, static analysis.

Route and calibrate research drawn from socially noisy web communities so agents do not mistake crowd mood for truth. Use when research notes, summaries, or quoted material come from Moltbook, Hacker News, Reddit, or Product Hunt and the job is to decide how much weight that material deserves, separate concrete weak signals from vibe/noise, or choose the right source-specific calibration skill before carrying findings forward.

Calibrate research done on Hacker News so agents do not mistake experienced technical cynicism, anti-hype sentiment, or comment-thread confidence for balanced evidence. Use when summarizing Hacker News reactions, extracting concerns from HN threads, citing HN as part of research, or deciding how much weight to give repeated negative or skeptical Hacker News comments.

Interpret and calibrate already-collected research material derived from Moltbook so agents do not mistake noise, spam, novelty, or social heat for reliable evidence. Use when weighing notes, summaries, or quoted material from Moltbook as part of research, deciding whether a Moltbook claim is worth following up, checking source reliability or evidence quality, or separating concrete weak signals from social-performance noise. Prefer when Moltbook is being used as a weak-signal discovery source rather than as authoritative proof. This skill classifies gathered Moltbook material as concrete report, vibe signal, or noise; flags evidence strength and uncertainty; and suggests whether the claim is follow-up-worthy. This is an informational calibration skill, not a browsing or execution workflow.

Assess your AI fluency using Anthropic's 4D framework (Dakan, Feller & Anthropic, 2025). Scans Claude Code sessions, runs LLM-based behavior classification on all messages, asks a self-assessment questionnaire for 6 unobservable behaviors, and generates a visual HTML report with scores and actionable feedback. Use when "assess fluency", "AI fluency", "fluency report", "fluency assessment", "4D framework", or "how AI fluent am I".

Runs multiple independent tasks concurrently by dispatching one focused agent per problem domain, completing batch workloads faster than sequential processing. Use when facing 2+ parallel tasks such as multiple failing test files, broken subsystems, or independent bugs that can be investigated and fixed simultaneously without shared state or sequential dependencies. Also useful when users ask to "run things in parallel", "work on multiple issues at the same time", handle "batch processing", or "multi-task" across independent problem domains.

Query and display available GitHub Copilot AI models with their capabilities, context limits, and features. Use when: "what models are available", "show copilot models", "list github models", "check model capabilities", "switch models". Examples: - user: "What models can I use with GitHub Copilot?" → fetch and display available models - user: "Show me models with vision support" → filter models by capability - user: "Which model has the largest context window?" → compare model specifications - user: "List all GPT-5 models" → filter by model family

Create, update, and organize Google Calendar events and schedules. Check availability, book time, and manage calendars. Use when asked to schedule a meeting, set up an appointment, book a call, check gcal, or manage calendar events.

Read, write, and format Google Sheets spreadsheets. Manage cell values, ranges, formulas, pivot tables, and charts. Use when asked to update a gsheet, edit a Google spreadsheet, add formulas, or work with spreadsheet data.

Create and modify Google Docs documents. Read content, insert tables, apply heading styles, and manage formatting. Use when asked to edit a gdoc, write a Google document, update a doc, or format document content.

Generates a Mermaid flowchart diagram of dbt model lineage using MCP tools, manifest.json, or direct code parsing as fallbacks. Use when visualizing dbt model lineage and dependencies as a Mermaid diagram in markdown format.

Use when a user needs help triaging dbt-core to Fusion migration errors. Runs dbt-autofix first, then classifies remaining errors into actionable categories (auto-fixable, guided fixes, needs input, blocked).

Use when migrating a dbt project from one data platform or data warehouse to another (e.g., Snowflake to Databricks, Databricks to Snowflake) using dbt Fusion's real-time compilation to identify and fix SQL dialect differences.