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Found 2,044 Skills
Research questions external to the codebase across library docs (Context7), the web (Tavily), local code through semantic source search, GitHub examples (gh), and the repo wiki (hallouminate), then synthesize with explicit confidence. Use whenever the user asks to research, look up, compare, or investigate something — phrases like "research X", "look up the API for Y", "compare libraries", "what does the doc say about Z", "find examples of how to do W", "is this library maintained", or "before I implement, what's the right approach". Use even when the user only mentions a library name without saying "research". Do NOT use for a single obvious file lookup or when the user already has enough evidence.
Bridges an Overleaf project to the local skills so a paper kept on Overleaf can be checked, polished, and reviewed, then synced back. Use when a researcher says "my paper is on Overleaf", "sync my Overleaf project", "pull my paper from Overleaf", "run preflight on my Overleaf paper", "edit my Overleaf project locally", or "push my changes back to Overleaf". Walks the three real paths — Overleaf Git integration (clone as a local repo), GitHub synchronization, or download-as-ZIP for free accounts — gets the .tex into a local working copy the other skills operate on, and guides syncing changes back. Treats the Overleaf access token as a secret, never auto-pushes without confirmation, and re-verifies the current Overleaf method live since premium availability and steps change. Trigger words - overleaf, my paper is on overleaf, sync overleaf, pull from overleaf, push to overleaf, edit overleaf locally.
Analyzes code, CI/CD workflows, and container config for security vulnerabilities and compliance risks, producing fitness scores (1-10) across input validation, authentication/authorization, data protection, dependency security, error handling/logging, and cryptography. Use when the user says "security review", "check vulnerabilities", "audit security", "security issues", /review:review-security, asks to audit GitHub Actions workflows or a Dockerfile for security, or wants security fitness scores before shipping. Only reports findings with confidence >= 7/10.
Migrates skills, agents, and rules between Google Antigravity 2.0 (IDE/CLI) format and other Agentic AI Provider formats (Cursor, Windsurf, Claude Code, GitHub Copilot, Roo Code), and vice-versa. Use when asked to migrate/convert agent customizations, rules, or skills to or from Antigravity.
Deep anonymization sweep for double-blind paper submissions, plus clean de-anonymization for camera-ready. Use when a researcher says "anonymize my paper", "double-blind check", "blind this submission", "remove author names", "did I leak my identity" — or, after acceptance, "de-anonymize" / "restore authors for camera-ready". Sweeps the whole leak surface, \author/\affiliation/\email/\orcid/\thanks, acknowledgments and grant numbers, hyperref pdfauthor and compiled-PDF metadata, first-person self-citations (rewritten to third person), GitHub/dataset/homepage links vs anonymized mirrors, LaTeX comments, .bib annotations, home-directory paths, and supplementary material (.git dirs, notebooks, LICENSE/AUTHORS files). Bundles a stdlib-only scanner (scan_anonymization.py, check ids shared with preflight-check) and a reversible-edit workflow (toggle + manifest) so every change is cleanly undone at camera-ready. Advisory only; never submits anything.
Sets up CloudWatch Application Observability (also called Omni) for the first time. Covers creating an Omni Space or Domain; configuring and listing Omni access grants (who has access and at what level) and access profiles bounding what async alerts, integrations, or agents can do; instrumenting an application or AI agent with the ADOT SDK so traces reach CloudWatch Omni (Python/Node/Java/.NET on EC2/ECS/EKS/Lambda) — CloudWatch Omni only; for Application Signals (auto-instrumentation, monitored service, ServiceEvents, reporting telemetry) use aws-observability — including no-image-rebuild and .NET CoreCLR profiler env vars; ingesting Azure telemetry via the CloudWatch agent on an Azure VM or AKS cluster; connecting Slack to a Space (and whether GitHub can be connected); and registering a custom MCP tool server over HTTP or stdio and choosing its auth (API key, bearer, or OAuth2). For using a Space already set up — querying, dashboards, Omni alerts, or defining Omni resources as code — use aws-observability.
Create or revise Mermaid diagrams that render inside Markdown, README files, GitHub, GitLab, wikis, and documentation sites. Use when the user explicitly requests Mermaid or needs a Markdown-native flowchart, sequence diagram, state diagram, class diagram, ER diagram, journey, Gantt chart, mind map, timeline, or lightweight architecture diagram. Prefer PlantUML for strict UML/C4 deliverables and ProcessOn only when editable hosted output is requested.
Base44 app connectors: OAuth integrations with external services (Google, Slack, GitHub and more) in shared, app-user and BYO-shared modes, how to connect one from an external agent, and how server code reads the token with getConnection. Use when the app must act on a user's or the builder's account at a third-party service.
Base44 platform concepts for a web app: entities, backend functions or server code, workflows, pages and layouts, the project file structure, authentication (platform login vs the custom-auth boilerplate), user invites and the GitHub features. Read first when starting work on a Base44 web app.
Convert Markdown documents to professionally typeset PDF files with reportlab. Handles CJK/Latin mixed text, fenced code blocks, tables, blockquotes, cover pages, clickable TOC, PDF bookmarks, watermarks, and page numbers. Supports multiple color themes (Warm Academic, Nord, GitHub Light, Solarized, etc.) and is battle-tested for Chinese technical reports. Use this skill whenever the user wants to turn a .md file into a styled PDF, generate a report PDF from markdown, or create a print-ready document from markdown content — especially if CJK characters, code blocks, or tables are involved. Also trigger when the user mentions "markdown to PDF", "md转pdf", "报告生成", or asks for a "typeset" or "professionally formatted" PDF from markdown source.
This skill should be used when the user asks to "process beta reader feedback", "alpha reader feedback", "feedback round", "synthesize reader feedback", "reader notes", "beta feedback", "reader readiness check", "review copy", "send the draft to readers", "share with readers who don't use GitHub", "triage the reader panel", or wants to collect, reconcile, and act on external reader feedback for a story project. NOT for running the simulated reader panel itself (use reader-panel), rounds with a professional editor (use editorial-review), or for checking a manuscript is ready to query or publish (use submission or publishing).
For the editor's maintainers. Turns what users corrected into lasting fixes in this repo, for every user, enforced by code and tests rather than notes. Reads the suggestions users marked "would help everyone" (~/.ai-video-editor/suggestions.jsonl), taste rules said twice (taste.py report), preview corrections (edits/*/corrections.jsonl), review-page notes (*/review.json) and open GitHub issues labelled feedback; groups repeats; for each item the maintainer accepts, writes the general rule into PRINCIPLES.md and the skill that owns it, adds or tightens the check that enforces it, adds a test that fails before and passes after, runs the full test set and leaves a CHANGELOG.md entry to commit. Use when the maintainer says "improve the editor", "turn feedback into fixes", "process suggestions", "what are users complaining about", "go through the feedback issues". Not for one user's own preferences (that is taste), and not for editing a video.