Total 58,637 skills
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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.
Detects when Luminance, Kira, or a similar bulk-review tool is in use, hands off the high-volume clause extraction to it, and QAs its output per the trust level in `~/.claude/plugins/config/claude-for-legal/corporate-legal/CLAUDE.md`. Use when user says "send to Luminance", "bulk review", "AI extraction", or when diligence-issue-extraction hits a high-volume category.
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.
Evidence-based resume authenticity audit and report generation skill. This skill should be proactively used when users request resume verification, resume fraud detection, candidate experience investigation, GitHub/open source contribution analysis, identification of role inflation from contributor to maintainer/core author, verification of Sino-foreign cooperative education or school titles, business indicator checks, sorting out public disputes, or production of HTML/PDF reports from investigation results. This skill breaks down resumes, screenshots, links, posts, code repositories, and official records into atomic claims, distinguishes between facts, contradictions, accusations, and unverifiable information, and generates self-contained evidence reports that do not include doxxing information and do not present unsubstantiated accusations as facts.
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.
Dynamic path segments ($paramName), splat routes ($ / _splat), optional params ({-$paramName}), prefix/suffix patterns ({$param}.ext), useParams, params.parse/stringify, pathParamsAllowedCharacters, i18n locale patterns.
Server-side authentication primitives for TanStack Start: session cookies (HttpOnly, Secure, SameSite, __Host- prefix), session read/issue/destroy via createServerFn and middleware, OAuth authorization-code flow with state and PKCE, password-reset enumeration defense, CSRF for non-GET RPCs, rate limiting auth endpoints, session rotation on privilege change. Pairs with router-core/auth-and-guards for the routing side.
Tencent Meeting MCP assistant for meeting lifecycle management. Create, modify, cancel meetings, track attendance, export recordings, query transcripts, and generate smart minutes. Use when user mentions online meetings, video conferencing, or Tencent Meeting operations.
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.
When the user wants to write, rewrite, or improve marketing copy for any page — including homepage, landing pages, pricing pages, feature pages, about pages, or product pages. Also use when the user says "write copy for," "improve this copy," "rewrite this page," "marketing copy," "headline help," "CTA copy," "value proposition," "tagline," "subheadline," "hero section copy," "above the fold," "this copy is weak," "make this more compelling," or "help me describe my product." Use this whenever someone is working on website text that needs to persuade or convert. For email copy, see emails. For popup copy, see popups. For editing existing copy, see copy-editing.
Parse a local or remote FILE (PDF) into markdown or structured JSON with fastCRW. Use when the source is a file on disk — "parse this PDF", "extract text from this document", "read this report", "convert PDF to markdown". Routing rule: URL → use crw-scrape; file on disk → use crw-parse. Step 5 of the crw workflow ladder.