pagen

Implements agents using Deep Agents. Use when building agents with create_deep_agent, configuring backends, defining subagents, adding middleware, or setting up human-in-the-loop workflows.

Automates the complete Blender build-go workflow, from building and packaging your extension/add-on to removing old versions, installing, enabling, and launching Blender for quick testing and iteration.

Manage Obsidian Daily Notes via obsidian-cli. Create and open daily notes, append entries (journals, logs, tasks, links), read past notes by date, and search vault content. Handles relative dates like "yesterday", "last Friday", "3 days ago".

Create supplementary materials attached to existing notes: experiments, meetings, reports, logs, conspectuses, practice sessions, annotations, AI outputs, links collections. Two-step process: (1) create aggregator space, (2) create concrete addition in base/additions/. INVOKE when user wants to attach any supplementary material to an existing note. Triggers: "addition", "create addition", "experiment", "meeting notes", "report", "conspectus", "log", "practice", "annotations", "links", "link collection", "аддишн", "конспект", "встреча", "отчёт", "эксперимент", "практика", "аннотации", "ссылки", "добавь к заметке".

Query and manage Observe using the Observe CLI. Use when the user wants to run OPAL queries, list datasets, manage objects, or interact with their Observe tenant from the command line.

AI agent that analyzes operational signals (metrics, logs, traces, alerts, SLO/SLI reports) from observability platforms (Prometheus, Datadog, New Relic, CloudWatch, Grafana, Elastic) and produces practical, risk-aware triage and recommendations. Use when reviewing system health, investigating performance issues, analyzing monitoring data, evaluating service reliability, or providing SRE analysis of operational metrics. Distinguishes between critical issues requiring action, items needing investigation, and informational observations requiring no action.

NVIDIA NIM inference microservices for deploying AI models with OpenAI-compatible APIs, self-hosted or cloud

Vectorized string manipulation using the char module and modern string alternatives, including cleaning and search operations. Triggers: string operations, numpy.char, text cleaning, substring search.

AI-orchestrated usability testing using Amazon Nova Act. The agent generates personas, runs tests to collect raw data, interprets responses to determine goal achievement, and generates HTML reports. Tests real user workflows (booking, checkout, posting) with safety guardrails. Use when asked to "test website usability", "run usability test", "generate usability report", "evaluate user experience", "test checkout flow", "test booking process", or "analyze website UX".

Create and document Jupyter notebooks for reproducible analyses

Error prevention and best practices enforcement for agent-assisted coding. Use when writing code to catch common mistakes, enforce patterns, prevent bugs, validate inputs, handle errors, follow coding standards, avoid anti-patterns, and ensure code quality through proactive checks and guardrails.

Workspace-first R statistics suite with subskills and agent-run metaskills (including run-demo for guided onboarding, explain-statistics for concept explanations, explain-results for interpreting outputs, format-document for NLSS format alignment, screen-data for diagnostics, check-assumptions for model-specific checks, and write-full-report for end-to-end reporting) that produce NLSS format tables/narratives and JSONL logs from CSV/SAV/RDS/RData/Parquet. Covers descriptives, frequencies/crosstabs, correlations, t-tests/ANOVA/nonparametric, regression/mixed models, SEM/CFA/mediation, EFA, power, reliability/scale analysis, assumptions, plots, missingness/imputation, data transforms, and workspace management.