Total 59,378 skills
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Turns raw peer reviews into a prioritized triage matrix before any rebuttal is written. Use it when reviews come back from OpenReview, EasyChair, CMT, or HotCRP and the researcher says "my reviews are in", "triage these reviews", "how do I respond to Reviewer 2", "plan my rebuttal", or pastes raw review text with ratings and confidence scores. Splits each review into individual concerns; classifies every concern as misunderstanding vs real flaw vs requested experiment (plus clarification and disagreement); scores severity x response effort; and produces a prioritized response strategy with per-review character or word budgets matched to the venue's rebuttal format (10k-char OpenReview threads, CVPR one-page PDF, journal revise-and-resubmit). Deterministic parsing and matrix rendering run in bundled stdlib Python scripts. Hands off to write-rebuttal for drafting; treats review text as confidential and never submits anything.
Prepares a reproducibility artifact (code/data) for submission and badging. Use when a researcher says "artifact evaluation", "artifact appendix", "reproducibility", "badge", "Artifacts Available/Evaluated/Functional/Reusable", "Results Reproduced/Replicated", "ACM badging", "USENIX/OSDI/SOSP AE", "SIGMOD ARI", "NeurIPS code/checklist", "ACL repro checklist", "Zenodo DOI", "Software Heritage", "anonymize my code/repo", or "package my code". Builds the artifact README + appendix, the dependency/run instructions, an anonymized repo for double-blind, and archival-DOI (Zenodo version vs concept) / Software Heritage SWHID guidance; resolves the ACM badge taxonomy and the Reproduced/Replicated era swap; and lints the artifact directory against the ML Code Completeness checklist with bundled stdlib-only scripts. Outputs an artifact-readiness checklist + packaging plan. Re-verifies the venue current artifact rules live. Advisory only; never submits.
De-AI-ifies and tightens academic prose without changing any technical claim. Use when a researcher says "this sounds like ChatGPT / AI-written", "humanize my paper", "remove the AI words", "too many delves and leverages", "polish my writing", "tighten the prose", "fix my hedging", "too many Moreover/Furthermore", "make my contributions active", or "keep my terminology consistent". Runs deterministic stdlib linters — prose_lint.py (leftover chatbot artifacts, LLM-tell vocabulary, connective stacking, em-dash density, double hedges, overclaiming boosters, passive contribution statements) and terminology_check.py (dataset/data set/data-set variants, acronym discipline, -ise/-ize mixing, glossary enforcement) — then drives a section-by-section edit pass using rewrite catalogs and the venue family's register norms. Numbers, results, claims, and citations are never altered; the venue's AI-use disclosure policy is surfaced and respected, never dodged.
Drafts or rewrites a Related Work / prior work section positioned against actually-retrieved papers — derives the REQUIRED clusters from the paper's own claimed scope (not just whatever was retrieved), clusters prior work into themes, articulates the delta (what this paper adds) per cluster, enforces a per-cluster citation floor and routes empty expected clusters back to find-papers, follows the target community's placement and citation conventions (numeric ACM/IEEE vs natbib author-year, single- vs double-blind self-citation, where the section sits at NeurIPS/CHI/SIGMOD-style venues), and admits only verified citations. Use when the user says "related work", "prior work", "position my paper against the literature", "how do we differ from X", "reviewers said related work is thin/missing/a laundry list", or asks to add citations to a draft. Works from papers found via find-papers and/or an existing .bib — it never invents references.
Generate "ASU-style" high-density Chinese technical resumes or Xiaohongshu showcase resumes based on users' real experiences: retain real names, schools, companies, departments, projects and verifiable metrics, proactively retrieve official school titles, enhance expression with colored experience bars, background/metrics/responsibility structure, English technical terms, architecture links, Benchmark, module-level Owner, 0→1 and large Scope narratives, and output self-contained HTML, A4 PDF and publishing images. Use this when users mention ASU style, ASU-style resumes, professional term stacking, technical experience packaging, Xiaohongshu resume long images, school titles, Owner/0-1 or require HTML/PDF. Do not fabricate non-existent educational backgrounds, companies, projects, roles, offers, metrics or achievements; exaggeration must be based on verifiable institutional labels, limited modules and real technical chains.
Use for the Twingate Identity Firewall (IDFW) and Twingate Gateway — protocol-level identity enforcement for SSH, the Kubernetes API, AND self-hosted/internal web apps, not just network access. LOAD when the user wants to grant, secure, SSO into, or audit a self-hosted/internal web app — identity forwarding/injection into HTTP requests, per-request audit trails. Gateway = Layer 7 reverse proxy injecting ES256 JWTs (Gateway Access Tokens) or trusted headers: JWKS verification, request-header injection, framework middleware (Express, Django, Next.js, Auth.js), no-code SSO (Grafana, Jenkins). Covers Gateway deployment; SSH privileged access with short-lived certs; vendor/contractor access; Certificate Authorities (X.509/SSH CA, local or Vault); kubectl via the Gateway; Terraform/Ansible automation; session recording (.cast playback, scanning for dangerous commands/leaked secrets). Assume this skill for "can Twingate pass the user's identity to my app?", "audit who used this app", or "SSO for my internal tool".
A Webflow University guided activity that walks you through migrating existing content into a Webflow CMS, including support for target sites with partial or existing Collections, and generates a custom, portable skill you can use to build and populate it well with any compatible agent.
Analyze assets on a Webflow site for SEO optimization. Identifies assets missing alt text and assets with non-SEO-friendly names, then generates and applies improvements.
Build a Webflow page, section, component, or full site from a Figma design using the Figma MCP and the Webflow MCP (Designer Bridge + Data API). Use whenever translating a Figma file/frame into Webflow, building directly in a Webflow project, or implementing a design as live Webflow elements/styles (not a copy-paste fragment). Triggers: "build this Figma in Webflow", "Figma to Webflow", "implement this design in Webflow", a figma.com URL + a Webflow site, "recreate this in my Webflow project".
A Webflow University guided onboarding skill for anyone getting started with the Webflow MCP. Checks your Webflow connection, helps you choose a real workflow to try, coaches prompt writing, and supports you through your first MCP run.
Checks that test method and function names read as complete sentences describing behavior. Flags cryptic names like test1, testFoo, or abbreviated names that do not describe what is being tested. Designed to run on every PR. Do NOT use for test coverage, test structure, or non-test code.
Detects entropy signals in a codebase: stale TODOs, disabled tests, lint suppressions, commented-out code, dead imports, empty catch blocks, and deprecated API usage. Designed for daily runs to catch quality erosion early. Do NOT use for feature work, refactoring planning, or security audits.