514 skills · sorted by relevance
Install LifeOS into an existing AI agent environment, then use a guided interview to capture current circumstances and goals for the Pulse dashboard. The skill also handles installation checks, updates, and removal.
Examine articles, reports, or pasted text for bias in the data, the producing organization's interests, and the reporting. It seeks cited studies and returns a structured report separating supported findings from editorial interpretation.
Research topics across multiple sources, cross-check findings, and verify source URLs before producing a report with confidence labels. Choose from quick lookups to iterative investigations that preserve findings, with community sources prioritized for questions about public reactions.
Strengthen existing tests with fast-check by checking properties across generated inputs. Find bugs that example tests miss and reduce failing inputs to small, reproducible cases. Property testing is available; the other listed workflows remain planned.
Examine writing for familiar AI patterns, statistical features, Pangram detection scores, and hidden character signatures. Compare drafts against known human writing to interpret the evidence, with explanations and limitations rather than a definitive authorship claim.
Explore one problem through several distinct perspectives to uncover hidden requirements and edge cases. Turn the findings into testable success criteria, refinements, and failure conditions, stopping when additional perspectives no longer produce new requirements.
Organize knowledge notes with links that explain their relationships, search saved information, review conflicting claims, and prepare weekly digests. Retrieve earlier work sessions and conversations by topic or date to recover context before continuing a task.
Import existing notes and agent instructions into LifeOS by splitting content into sections and proposing destinations. Review uncertain classifications, approve selected groups, and retain source information alongside the imported material.
Improve writing, prompts, or skills through repeated review cycles that carry forward lessons and rejected approaches. Human feedback guides each pass by default, while saved history supports resuming work and tracking progress.
Find arXiv papers by topic, research category, or paper ID. Retrieve titles, authors, abstracts, and links, check new submissions, and add AlphaXiv's AI-generated overviews when available to help decide which papers to read.
Create editable tldraw canvases with boxes, arrows, sticky notes, frames, and text. Read an existing canvas to group related ideas and rearrange shapes, then open the resulting file in a compatible editor for further adjustments.
Develop varied ideas for names, writing angles, and problem solving by exploring five candidates internally and presenting the strongest. It also expands small example collections into diverse datasets for evaluation, training, or testing.
Investigate incidents and recurring defects with structured methods such as Five Whys and Fishbone cause maps. Identify multiple contributing conditions, build a blame-free account of what happened, and propose practical changes to prevent similar failures.
Gather local news, building permits, elections, legislation, and other civic information for a configured US hometown. The skill combines available sources into a daily JSON digest that Pulse can display in its LOCAL tab.
Find quotes that fit a text’s themes, with attribution and a short reason for each suggestion. Add sourced quotes, research thinkers, and organize a quote collection by theme and usage history to avoid repetition.
Use cmux on macOS to organize visible teams of AI agents, send prompts, inspect terminal output, and monitor completion. Set up role-based teams or have several agents investigate the same problem; live operation remains unproven in the supplied material.
Improve code, prompts, skills, or agent definitions through repeated changes and evaluation. Measure numerical targets such as latency or size, or use an LLM to judge pass-or-fail criteria, keeping changes that improve the result.
Turn completed research, analysis, reports, or plans from the current session into a self-contained HTML artifact. A renderer controls layout, typography, and color, using structured content and a choice of two visual styles.
Review stored LifeOS goals and personal context against available records such as sleep, app usage, work, and expenses. The skill prioritizes stale information and contradictions, proposes corrections for approval, and reviews the gap between current and ideal states.
Investigate recurring problems by mapping feedback loops, the cycles through which effects influence their causes. Identify underlying structures, match known patterns, and assess where changes could help and what unintended effects they might produce.
Explore explanations for poorly understood phenomena through repeated hypothesis generation and selection. Compare causal mechanisms, test what happens when components change, and retain distinct candidates with predictions, rival comparisons, and clearly stated unresolved questions.
Evaluate AI agent outputs with code checks and model-based grading. Run repeated trials to measure reliability, compare prompts or models, and check whether changes reduce quality, with saved transcripts for reviewing the results.
Turn the current conversation topic into an interactive lesson with spoken explanations and text diagrams in the terminal. Answer one question at each step, receive feedback, and check your understanding with a short quiz and recap.
Create and maintain LifeOS skills, from initial file structure to validation and effectiveness testing. Update workflows, improve instructions, and refine descriptions so skills activate for appropriate requests, while checking naming conventions and handling of personal data.