137 skills · sorted by relevance
Add regression tests to AI-assisted code changes so previously found bugs are checked automatically. Test required API fields and sandbox response contracts, then run tests and builds before AI review to catch recurring omissions and inconsistent behavior.
Inspect a project's code, history, tests, and documentation, then ask three focused questions to recommend an AI-assisted development approach. Receive one combination of methods, reasons for the choice, and practical first steps tailored to the project.
Refine AI-assisted academic drafts into natural scholarly prose while preserving citations, statistics, and methodological details. Choose how extensively to revise and which sections to edit, then review a report comparing the original and revised text.
Evaluate performance fairly when output is AI-assisted — what still measures the human, what now measures the tooling, and how to run the review conversation. Use when reviewing someone whose work is heavily AI-assisted, when output volume stopped meaning anything, when calibrating a team with uneven AI adoption, or when writing review criteria for the AI era. Produces review guidance: a what-measures-whom analysis, rewritten criteria, calibration rules for mixed-adoption teams, and conversation scripts. For the general review document use performance-review; for redesigning the role itself use role-redesign-for-ai.
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Open the hosted context-mode Insight dashboard in your default browser to access analytics for AI-assisted engineering teams, including productive rates per engineer, retry waste, blockers, and views tailored to different roles.
Set up and use CircleCI Chunk for AI-assisted build and delivery workflows. Chooses web or command-line steps for configuration, failed-build troubleshooting, environment setup, and task execution, then reports results and unmet prerequisites.
Plan AI-assisted ad creative production, testing, budget allocation, and measurement across advertising platforms. It provides workflows for comparing messages, adapting assets to each channel, and refreshing ads when repeated exposure starts to reduce performance.
Turn AI-assisted coding work into optional, short learning exercises. Predict behavior, compare your approach with the implementation, or explain code in your own words, with feedback that helps clarify how the code works and why design choices were made.
Examines project code, development history, tests, and documentation, then asks three focused questions to recommend a combination of AI-assisted development methods, with practical starting steps tailored to the project.
Improve the clarity and voice of AI-assisted academic writing (papers, theses, rebuttals) and funding proposals (NSF Project Summary/Description, NIH Specific Aims): preserve scholarly conventions, match claims to evidence (and, for proposals, claims to feasibility), and match the author's own voice. It never changes a number, result, or citation, and it is not for evading AI-use disclosure. Use when editing AI-assisted academic prose or grant proposals.
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Design an AI-assisted workflow for a recurring task — which steps to hand to AI, which to keep human, and how they connect — so you get leverage without losing quality or control. Use when asked how do I use AI for [process], automate this with AI, design an AI workflow, or where does AI fit in my process. Produces a map of the task's steps split into AI-does / human-does / human-checks, the right tool/prompt for each AI step, the hand-offs and review points, the failure modes to guard against, and a start-small rollout — turning a manual process into a reliable AI-assisted one that keeps you in control.
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Research and prepare an outside contribution to an open-source repo, then stop at a queue item for a human to approve: policy check, intake, duplicate check, reproduction, patch, the repo's own checks, an independent critic rerun, and a disclosed, hashed draft of the exact text to post. Use this skill whenever the user wants to contribute to someone else's repo, fix an upstream bug, pick up a good-first-issue, draft an AI-assisted pull request for an outside project, follow up on their own outside PR, or run a gated contribution pipeline. Triggers on open source contribution, contribute to this repo, upstream fix, outside PR, external PR, good first issue, fix this issue in their repo, contribution research, AI-assisted PR, disclose AI in PR, reply to review on my PR. Also triggers when the user names an issue in a repo they do not maintain and asks for a fix, even if they do not say contribute.
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How humans and AI compose in content workflows. Where AI legitimately participates, where humans must own, hybrid workflow patterns, voice ownership preservation, the AI slop problem, disclosure and transparency, team calibration, and the ethics of intellectually honest AI-assisted content production. Triggers on AI content workflow, AI-assisted writing, hybrid content production, AI in editorial, AI slop, AI disclosure, AI usage policy, AI content ethics, voice preservation with AI, team AI calibration. Also triggers when content feels generic despite quality tools, when team AI usage has drifted into inconsistency, or when a regulated or trust-sensitive context requires explicit AI policy.
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Systematic strategies for searching scientific literature across PubMed, arXiv, Google Scholar, and AI-assisted tools. Covers PICO framework for clinical questions, three-tiered search (database-specific, AI-assisted, content extraction), PubMed field tags and MeSH, boolean query construction, and full-text extraction. Use when planning a literature search or choosing a search tier.
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Edit existing footage into vlogs, tutorials, demos, or short social clips. This skill guides segment selection, cutting, overlays, audio additions, and platform-specific framing, with final pacing and polish handled in a traditional video editor.
Work with AI as a coding partner, choosing who writes code and who guides the work. Supports role switching, test generation, ongoing code review, debugging, and step-by-step explanations for learning.
Work with AI as a coding partner, choosing who writes code and who guides the work. Supports role switching, test generation, ongoing code review, debugging, and step-by-step explanations for learning.
Learn to examine compiled programs through one interactive lesson per session. Labs and quizzes cover file formats, machine instructions, program behavior, and analysis tools, while recorded observations provide evidence for completing each lesson and resuming later.
Analyze Neuropixels neural recordings with SpikeInterface, from loading and preprocessing to motion correction and spike sorting, which separates signals by neuron. Calculate quality metrics, review candidate units, and export plots and data for further inspection.
Review and rewrite prose that feels repetitive, vague, or formulaic while preserving its meaning, evidence, and voice. Get quoted examples of problems, a revised draft, an explanation of edits, and a second review.
Review a product or major feature before development starts. Assess demand, revenue, repeat use, trust, and user reach, then identify the riskiest assumption, a small validation step, and work to delay.
Create structured Markdown specifications that AI agents can read and use. Organize requirements, constraints, interfaces, acceptance criteria, and testing plans into a self-contained document with clear definitions and examples.
Use a concise SEO reference to review content credibility, page performance, site structure, and page elements. It explains key checks, structured data types, measurement tools, and editorial practices for AI-assisted content.
Compare major AI coding tools and consult practical guidance on prompts, project instructions, debugging, workflows, and costs. Use the reference to choose suitable tools and identify why an AI-assisted coding task is stuck.