Skills
99,951 packaged capabilities and playbooks for agents. Install any of them with one command.
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Universal LaTeX document skill: create, compile, and convert any document to professional PDF with PNG previews. Supports resumes, reports, cover letters, invoices, academic papers, theses/dissertations, academic CVs, presentations (Beamer), scientific posters, formal letters, exams/quizzes, books, cheat sheets, reference cards, exam formula sheets, fillable PDF forms (hyperref form fields), conditional content (etoolbox toggles), mail merge from CSV/JSON (Jinja2 templates), version diffing (latexdiff), charts (pgfplots + matplotlib), tables (booktabs + CSV import), images (TikZ), Mermaid diagrams, AI-generated images, watermarks, landscape pages, bibliography/citations (BibTeX/biblatex), multi-language/CJK (auto XeLaTeX), algorithms/pseudocode, colored boxes (tcolorbox), SI units (siunitx), Pandoc format conversion (Markdown/DOCX/HTML ↔ LaTeX), and PDF-to-LaTeX conversion of handwritten or printed documents (math, business, legal, general). Compile script supports pdflatex, xelatex, lualatex with auto-detection, latexmk backend, texfot log filtering, PDF/A output, and verbosity control (--verbose/--quiet). Empirically optimized scaling: single agent 1-10 pages, split 11-20, batch-7 pipeline 21+. Use when user asks to: (1) create a resume/CV/cover letter, (2) write a LaTeX document, (3) create PDF with tables/charts/images, (4) compile a .tex file, (5) make a report/invoice/presentation, (6) anything involving LaTeX or pdflatex, (7) convert/OCR a PDF to LaTeX, (8) convert handwritten notes, (9) create charts/graphs/diagrams, (10) create slides, (11) write a thesis or dissertation, (12) create an academic CV, (13) create a poster, (14) create an exam/quiz, (15) create a book, (16) convert between document formats (Markdown, DOCX, HTML to/from LaTeX), (17) generate Mermaid diagrams for LaTeX, (18) create a formal business letter, (19) create a cheat sheet or reference card, (20) create an exam formula sheet or crib sheet, (21) condense lecture notes/PDFs into a cheat sheet, (22) create a fillable PDF form with text fields/checkboxes/dropdowns, (23) create a document with conditional content/toggles (show/hide sections), (24) generate batch/mail-merge documents from CSV/JSON data, (25) create a version diff PDF (latexdiff) highlighting changes between documents, (26) create a homework or assignment submission with problems and solutions, (27) create a lab report with data tables, graphs, and error analysis, (28) encrypt or password-protect a PDF, (29) merge multiple PDFs into one, (30) optimize/compress a PDF for web or email, (31) lint or check a LaTeX document for common issues, (32) count words in a LaTeX document, (33) analyze document statistics (figures, tables, citations), (34) fetch BibTeX from a DOI, (35) convert a Graphviz .dot file to PDF/PNG, (36) convert a PlantUML .puml file to PDF/PNG, (37) create a one-pager/fact sheet/executive summary, (38) create a datasheet or product specification sheet, (39) extract pages from a PDF (page ranges, odd/even), (40) check LaTeX package availability before compiling, (41) analyze citations and cross-reference with .bib files, (42) debug LaTeX compilation errors, (43) make a document accessible (PDF/A, tagged PDF), (44) create lecture notes or course handouts, (45) fill an existing PDF form (fillable fields or non-fillable with annotations), (46) extract text or tables from a PDF (pdfplumber, pypdf), (47) OCR a scanned PDF to text (pytesseract), (48) create a PDF programmatically with reportlab (Canvas, Platypus), (49) rotate or crop PDF pages (pypdf), (50) add a watermark to an existing PDF, (51) extract metadata from a PDF (title, author, subject).
Lead qualification engine with conversational intake. Asks structured questions to understand your qualification criteria, generates a reusable qualification prompt, then batch-enriches leads via Apify LinkedIn scraping and scores them with parallel processing. Outputs qualified/disqualified verdicts with confidence scores and reasoning to CSV or whatever output format the user prefers. Supports calibration mode for prompt refinement.
(forwward) Drafts terms of service, privacy policies, contracts, IP assignments, and open source licensing after gathering industry and jurisdictional context. Triggers on legal documents, terms, privacy policies, contracts, compliance, licensing, or any legal drafting need.
Audits Dart and Flutter package dependency licenses using the Very Good CLI packages_check_licenses MCP tool, flags non-compliant or unknown licenses, and produces a compliance summary report. Use when the user says "check licenses", "license audit", "check dependency licenses", "license compliance", "review package licenses", "are our dependencies compliant", "scan for license issues", or "pre-release license check". Use it especially when the request asks for a compliance verdict without a scan, as in "read the licenses off this pubspec", "confirm we're compliant", "just tell me if these packages are safe to ship", "I'd rather not run anything", or "which of these are GPL", because refusing to certify compliance from a dependency list is the call this skill governs. A pasted pubspec is a trigger, not a substitute for the audit.
Light research mainline step 3 · idea generation: structurally diverge from a vague direction/data/literature (stimulate the computation system to generate, not generic brainstorming) → produce tiered candidate ideas that are worth doing and doable (moonshot sprint / solid steady / safe fallback), each answering why it is worth doing · the innovation point · how it beats the existing state · what concrete problem it solves · what venue tier it can target, and carrying a pre-emptive collision self-check at proposal time (closest prior work + delta, feeding off the upstream literature-search domain map). When to use: the user asks "what can this direction/data do" / wants innovation points, research directions, topic selection, breakthroughs / asks me to help think of ideas / brainstorm research ideas / whether an idea holds (generate first, then send to idea-critique for strict review). Trigger words: propose idea / think of idea / innovation point / research direction / topic selection / breakthrough / differentiation / what can this direction do / what is there to do / brainstorm / research idea / ideation / new idea / project initiation. Core discipline: never issue the final verdict of novel/non-innovative (that is idea-critique's critical gate; the generation side only produces collision warn signals); constrain candidates along the lineage evidence root → mechanism/hypothesis delta → information gain → minimal discriminating experiment; pass the innovation_engine anti-stitching gate, binding the typology of originality source (new problem/new mechanism/new measurement/new data/new theory/cross-domain transfer/engineering increment) to claim strength; count/seven angles are advisory only, mechanism diversity is the hard gate.
Always-on Light skill for runtime project memory and project management: land "how far the project got / what was decided / which version shipped / how terminology is unified / where we left off" in each project's own .light/ directory (project card + decision log + version history + controlled glossary + cross-session handoff card), reusing the passport.py engine to manage the DAG ledger (no reinvention). It owns the consistency source of truth: change a definition and the change broadcast rescans all materials; it is the memory substrate for the controller's "automatically report where we left off at session start". When to use: long-running/cross-session projects, when background/progress/version/decisions must be remembered, resuming or taking over at session start, logging important progress immediately, handing off as context runs out, after changing terminology/metric/innovation-point definitions. Trigger words: memory / project management / project card / decision log / version history / version record / resume / take over / where we left off / next step / handoff / startup prompt / glossary / change broadcast / archive / ledger / .light / passport / cross-session. Core discipline: memory goes into explicit .light/ files (not vector retrieval); convert relative dates to absolute; external mutable facts carry [snapshot]; for self-consistency use pm.py audit and its exit code (don't just say "it's recorded"); irreversible decisions (archiving / writing back lessons) stop and ask the user.
Audit, plan, scaffold, and safely migrate research project structures across greenfield, existing Git/non-Git repositories, and monorepo subroots. Use for project folders, repository cleanup, source inventory, move maps, naming and storage policy, Python/R/mixed/LaTeX profiles, template provenance, conflict review, applied-move evidence, or rollback. Existing projects are read-only until the user authorizes exact action IDs bound to a plan digest. Preserve uncommitted and untracked work, symlinks, submodules, and memory-pm's .light content. This is an off-DAG local tool: do not emit findings or invent a STAGE_GATES/ROUTES connection.
Light research mainline step 7 · result analysis: don't describe good or bad, explain "why", bind every conclusion tightly to a claim + evidence strength, and guard against p-hacking. When to use: after experiments finish and results need interpreting / asking "what do these numbers mean" / performing significance tests + effect size + confidence intervals + multiple-comparison correction / worrying about p-hacking (uncorrected multiple comparisons, selective reporting, HARKing) / assigning evidence strength to each claim so writing can calibrate wording / judging whether results support hypotheses and are reproducible. Trigger words: result analysis / interpret data / what do these results mean / significance / p value / effect size / Cohen's d / confidence interval CI / multiple comparisons / BH-FDR / Bonferroni / correction / p-hacking / selective reporting / garden of forking paths / HARKing / evidence strength / claim-evidence binding / SHAP / ablation analysis / slice analysis / paired test / result analysis. Core discipline: statistical errors / p-hacking = critical (spec §4.2, STAGE_GATES[7]=[stat_validity,evidence_strength]); overinterpretation / missing effect size = warn; for significance look at q not p, non-significant results may only be reported as "no significant difference found", and wording strength must match evidence strength; statistical checks are bounded — never claim "proved the method works / causality holds". This skill initiates the 7→5 (hypothesis unsupported) / 7→6 (not reproducible) loop-backs.
Design or modernize a software system from an evidence-backed current-state inventory through quality attributes, architecture options, API and schema contracts, migration/rollback plans, ADRs, and verification. Use for greenfield or existing monoliths, modular monoliths, services, system/API/database design, schema migration review, data-flow reliability, tenant/PII controls, or architecture evolution. Existing systems stay read-only until the user selects an option and authorizes exact mutations. Unknown facts stay UNKNOWN. This is an off-DAG engineering skill: do not emit findings or invent STAGE_GATES, ROUTES, stages, or back-edges.
Drive a code change or document update through reviewed research, plan, and design outputs → approval → execution → verification → independent review, including blocked verification, pending review/CI, run recovery, and releasing a merged or closed change's local resources. Owns phase placement, approval revision, evidence, finding state, and recovery meaning—not generic deliverable-review method, check-failure diagnosis, host execution APIs, or delivery storage. Development practices own verification diagnosis; conduct practices own non-code deliverable review; project policy chooses gates and branches. Covers created-resource records and claims on shared simulators, emulators, devices and ports. Defer to a host project's own more-specific change loop where present. Not for standalone read-only questions, investigations, or reviews that change nothing
Build a fully synthetic vertical short (1080×1920, ~40s) end to end from a topic: script + beats.json, linked TSX beats over a persistent canvas, phone-scale QA, render, ElevenLabs voice with exact word-synced captions, SFX audition, and optional music bed. Use when the user wants to “make a short,” “create a shorts video,” build a TSX short for a niche (chess, math, algorithms, dev tips, Excel…), add voice/captions to a short, or continue the short-N series in this repo. This is the 100%-TSX track—for video-model pixels use make-ai-short; for the paper-collage documentary look use make-vox. Defers raw TSX crash rules to vidtsx-2d-generator and SFX taste to suggest-sfx + brand §7.
Map the big picture around a topic or software repository by identifying its boundaries, layers, actors, components, relationships, flows, history, fault lines, and open questions, then explain how the pieces fit together and where to look next. Use when a user asks to understand a whole field, domain, technology, ecosystem, industry, codebase, architecture, unfamiliar repo, or phrases such as "give me the big picture", "map the landscape", "how does this all fit together?", "help me get oriented", or "what am I missing?"
The user is about to make a major decision (investment/acquisition/commitment) and needs to assess whether downside protection is sufficient, or is deciding whether to act when valuation is uncertain. Language signals: "is this price low enough" / "what if I'm wrong" / "how much loss can I bear" / "how much downside is there". Not applicable to: everyday low-risk decisions, or purely upside analysis (when downside protection is not a concern).
Use markdown formatting when drafting content intended for external systems (GitHub issues/PRs, Jira tickets, wiki pages, design docs, etc.) so formatting is preserved when the user copies it. Load this skill before producing any draft the user will paste elsewhere.
Complete usage guide for the MarkItDown command-line tool (`markitdown` command), covering all CLI options (`-o/-x/-m/-c/-d/-e/-p/--use-cu/--cu-endpoint/--cu-analyzer/--cu-file-types/--list-plugins/--keep-data-uris`), input and output modes (files/pipes/stdin), pipeline composition, integration with shell toolchains (jq/grep/xargs/tee/find), and typical examples (batch conversion, Azure backend switching, enabling plugins, streaming input). Use this skill when users ask how to use the markitdown command, how to batch-convert to PDF, how to pipe in stdin, how to list installed plugins, or how to configure Azure Document Intelligence CLI. For OCR via LLM Vision, use the sibling skill `markitdown-ocr`.
Guides LLMs to use LLM Vision via the `markitdown-ocr` plugin (official plugin repository: microsoft/markitdown/packages/markitdown-ocr) to extract OCR text from embedded images and scanned pages in PDF / DOCX / PPTX / XLSX files, using OpenAI-compatible models such as gpt-4o, Azure OpenAI, Gemini, and other OpenAI-compatible services. Covers installation, configuration (`OPENAI_API_KEY` / Azure credentials / custom endpoint), Python API and CLI wrappers, full-page rendering fallback for scanned PDFs, OCR behavior differences by format (inline insertion / append after tables / top-level descriptions), custom prompts, and troubleshooting costs and limits. Use this skill when users ask how to convert a scanned PDF to markdown, extract text from images in a PPT, use GPT-4o for OCR, install the `markitdown-ocr` plugin, or configure markitdown with Azure OpenAI. For ordinary CLI usage or a MarkItDown overview, use the sibling skills `markitdown-cli` and `markitdown-awesome`
The conductor for non-trivial development work: takes an idea — however rough — all the way to shipped code, picking the right amount of process (a one-line fix vs. a full product spec) and driving it end to end rather than ad hoc. Triggers on mapping a loose idea into open questions, grilling a plan to shared understanding, researching unknowns, writing a PRD, breaking work into issues, technical or domain design, prototyping UI, implementing a feature, test-driven development, code review, independent verification, writing a PR description, debugging to root cause, improving architecture, or resolving merge conflicts. Not for one-off prose or writing tasks.
Mathematical visualization skill for equations, proofs, and geometric concepts. **Triggers when:** - User mentions equations, formulas, or mathematical expressions - Request involves mathematical proofs or derivations - Content includes geometric relationships - User mentions LaTeX, calculus, algebra, geometry, trigonometry - Patterns: "equation", "formula", "prove", "derive", "graph", "plot" **Capabilities:** - LaTeX equation rendering with color-coded components - Function graphing and transformations - Geometric constructions and proofs - 3D mathematical surfaces - Step-by-step derivations with highlights
Configure, simulate, debug, and analyze radarDataGenerator within radarScenario. Use for: interactively building radar detection scenarios from datasheets or performance requirements; diagnosing missed detections and configuration errors; interpreting sensor spherical, body, and scenario-frame outputs; deriving ReferenceRange from hardware specs via link budget; scan mode configuration (mechanical, electronic/AESA, hybrid); and validating simulation results against analytical predictions.
.NET MAUI app lifecycle guidance covering the four app states (not running, running, deactivated, stopped), cross-platform Window lifecycle events, backgrounding and resume behaviour, platform-specific lifecycle mapping for Android and iOS/Mac Catalyst, and state-preservation patterns. USE FOR: "app lifecycle", "OnStart", "OnSleep", "OnResume", "backgrounding", "app state", "window lifecycle", "save state on background", "resume app", "deactivated event", "lifecycle events". DO NOT USE FOR: navigation events (use maui-shell-navigation), dependency injection setup (use maui-dependency-injection), or platform APIs (use maui-platform-invoke).
Provides comprehensive guidance for searching and retrieving Maven components from Maven Central Repository (https://repo1.maven.org/maven2/). This skill enables searching by groupId, artifactId, version, and other coordinates, retrieving component metadata (POM files, JARs, sources, Javadoc), querying version history, and analyzing dependencies. Use when the user needs to find, verify, or retrieve Maven dependencies, check component versions, analyze dependency trees, or work with Maven coordinates.
Operate the user's already-open, logged-in Chrome tabs through Chrome DevTools MCP. Use whenever the user asks the agent to jump into their current browser, continue work in an existing tab, fill or submit a form, write or post a comment or reply, update a page, inspect what is open, or interact with signed-in web apps such as Confluence. Also use as the browser-driving companion to melech-debug-mode; do not start a debugging collector for ordinary browser work.
A creation archive and memory system for Chinese web novels. Use when the user wants to record or save outlines, character profiles, or settings; continue writing a book across sessions; asks “Where did I leave off?” or “Was this plot thread resolved?”; create a version snapshot of a book (like git); or manage progress across multiple books. Maintains persistent archives in each book’s folder for shared read/write access by all writing skills.
Guides users through building a Meridian Marketing Mix Modeling (MMM) model. Use when a user wants to load data, map columns, configure ModelSpec, run Exploratory Data Analysis (EDA), fit a model, and save the model. Don't use for visualizing results or creating a scenario planner.
Normalize messy creator campaign metrics from multiple sources into a single clean table with standardized field names ready to merge into your master tracker. This skill should be used when cleaning up influencer metrics, standardizing campaign data from multiple platforms, normalizing creator performance numbers, merging metrics from Instagram and TikTok and YouTube into one sheet, formatting messy analytics exports, preparing campaign data for a master spreadsheet, converting raw platform stats into a consistent format, combining metrics from different reporting tools, deduplicating creator data from multiple sources, fixing inconsistent column names across exports, or cleaning up a metrics dump before reporting. For calculating engagement rates, see engagement-rate-calculator-benchmarker. For full campaign reports, see campaign-roi-calculator. For parsing a single Story screenshot, see story-metrics-screenshot-parser.
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From what the protocol is to installing your first server, these answers cover most first questions.
An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.