Skills
100,344 packaged capabilities and playbooks for agents. Install any of them with one command.
All
Blue Professional — Cream paper background with electric cobalt blue accents; clean modern professional. Anything that should feel modern-considered and lightly authoritative: B2B SaaS pitches, consulting deliverables, advisory updates, investor reports.
Playful — Sun-warm peach background with Syne display: a friendly indie launch deck. Anything that should feel warm, indie, and approachable: creator portfolios, indie product launches, lifestyle brands, small-business pitches, newsletter / community decks.
Model development orchestration Skill covering asset query, model training, inference deployment, and real-robot evaluation in any combination. Supports full end-to-end pipeline or partial stages (e.g., train+deploy only, deploy+eval only). When user requirements involve two or more stages, prefer this Skill over individual module Skills. Triggers include: "用XX机器人训练XX任务", "so101 插笔", "训练部署", "部署评测", "训练评测部署", "模型开发流程", "端到端训练", "训练推理评测", "只训练不评测", "训练完部署", "model workflow", "end-to-end training", "train and deploy", "deploy and eval".
Help a human understand and inspect completed AI-authored repository work through actual results, before-and-after explanations, and supporting evidence. Use when the user asks to understand the work before accepting it, examine assumptions beyond an AI summary, or resume a human review after switching tasks. Also use for explicit requests to judge unresolved product behavior. Ordinary change summaries, automated defect hunting, and non-repository content do not need this skill.
Remove signs of AI-generated writing from text. Use when editing or reviewing text to make it sound more natural and human-written. Based on Wikipedia's comprehensive "Signs of AI writing" guide. Detects and fixes patterns including: inflated symbolism, promotional language, superficial -ing analyses, vague attributions, em dash overuse, rule of three, AI vocabulary words, negative parallelisms, and excessive conjunctive phrases. Credits: Original skill by @blader - https://github.com/blader/humanizer
Implements internationalization with Rails I18n for multi-language support. Use when adding translations, managing locales, localizing dates/currencies, pluralization, or when user mentions i18n, translations, locales, or multi-language. WHEN NOT: English-only applications without localization needs, formatting handled by presenters, or date/number formatting in non-user-facing code.
Score the QUALITY of a research idea at the idea stage — given a Markdown file with Title / Motivation / Method sections (no experiments needed), produce a per-axis quality assessment with cited evidence and an overall 0–100 score plus a verdict; or, given two such idea files, a blind head-to-head comparison. TRIGGER when the user asks "how good is this idea", "rate / score / grade this research idea or proposal", "review my idea .md before I write it up", "is this contribution strong enough to pursue", "which of these two idea files is stronger", or hands over an idea Markdown file (Title/Motivation/Method) and wants a quality judgment. Use this even when the user does not say the word "score" but clearly wants an assessment of how strong an idea is. DO NOT trigger for prior-art / overlap / "is this novel vs existing work" checks (that is a literature-collision task), for full reviews of finished papers that already have results, or for generating the idea itself.
Read your Instagram niche and profile from real data via Apify, no login. Scan a hashtag for the posts traveling now (likes, comments, owner) to see the format and hook that works. Pull profile stats for any handle, yours or a competitor's: followers, posts, bio, category. Instagram hides who liked or commented on other accounts, so this is discovery plus profiles, not engagers. Triggers on "what works in my niche", "scan the hashtag", "competitor stats". Not for writing captions (use ig-caption-writer)
Validates and implements a GitHub issue against current repository evidence, repeats code review and black-box testing, runs the project's completion gate, and opens a draft pull request. Use when the user runs /implement-issue with an issue number.
Implementation entry point. Use to execute a completed specification. Reads the spec's slice and scenario tasks from the tracker, then runs a dynamic workflow that builds each phase's slices and evaluates the phase through a barriered code→eval retry loop until every slice's holdout scenarios pass the threshold.
Implement a feature from a product/tech spec, URL, GitHub issue, or by auto-selecting the next papercut enhancement. Reads the spec, implements the feature following Streamlit patterns, and creates a merge-ready PR. Use when given a spec folder path, document URL, or issue link to implement, or when asked to implement a feature or papercut without a spec.
Analyze industry sectors (industry indices, constituent stocks, fund flow rankings, historical performance) to judge sector strength and rotation. Use when the user asks about sector performance, sector rotation, sector fund flows, sector constituents, or needs sector comparisons.
Initialize projects with agentic coding structure. Use when setting up a new project, adding AI agent support to existing project, or when user says "init", "initialize", "setup project", or "scaffold". Creates AGENTS folder, documentation templates, and _NOTES scratch space.
Ink.js (React for CLI) design and implementation guide. Use when: (1) Creating or modifying Ink.js components (2) Implementing Ink-specific hooks (useInput, useApp, useFocus) (3) Handling emoji/icon width issues (string-width workarounds) (4) Building terminal-responsive layouts (5) Managing multi-screen navigation (6) Implementing animations (spinners, progress bars) (7) Optimizing performance (React.memo, useMemo) (8) Handling keyboard input and shortcuts (9) Testing CLI UI (ink-testing-library)
Use when writing or reviewing tests for external API adapters, storage adapters, database integration boundaries, or service contracts. Covers fixture design, schema validation, negative contracts, fakes, recorded responses, and CI-safe verification. Do not use for simple pure unit tests with no boundary contract.
Inworld TTS API. Covers voice cloning, audio markups, timestamps. Use when integrating Inworld text-to-speech, cloning voices, adding audio markups (SSML-like), or aligning viseme timestamps. Keywords: Inworld, text-to-speech, TTS, voice cloning, visemes.
Activates when the user asks to write, draft, translate, or revise Italian texts for public, editorial, blog, social media, newsletter, marketing, fiction, nonfiction, or informational use. It also activates when the user asks to humanize Italian texts, remove AI patterns, correct chatbot-like wording, capture or calibrate an author's voice based on a corpus of their texts, respond to an automatic detector that flagged human-written text as generated, or revise Italian text generated by language models.
Write, debug, review, profile, or shard JAX numerical code. Use when the hard part is JAX tracing, autodiff, control flow, PRNGs, compilation, array placement, or runtime performance. Do not impose JAX on a NumPy-only or unrelated GPU task.
Systematic Kubernetes troubleshooting and incident response. Use this skill whenever the user mentions Kubernetes, K8s, kubectl, pods, containers, or clusters. Triggers include diagnosing CrashLoopBackOff, ImagePullBackOff, OOMKilled, or Pending pods, responding to production incidents, troubleshooting node NotReady or DiskPressure, debugging service connectivity or networking, investigating PVC or storage failures, analyzing performance degradation, checking cluster health, troubleshooting Helm releases, and conducting post-incident reviews.
Kaggle runtime environment, paths, accelerators, and reproducibility. PROACTIVELY activate for: (1) `/kaggle/input` path errors, (2) `/kaggle/working` output placement, (3) local vs Kaggle notebook behavior, (4) GPU/TPU/accelerator selection, (5) internet enablement, (6) package/version pinning, (7) memory cleanup and timeout issues, (8) DEBUG flags for fast runs, (9) Kaggle Secrets usage guidance, (10) quota-consuming runtime settings. Provides: path conventions, runtime checklist, accelerator IDs, reproducibility patterns, and resource safeguards.
Use when asked to update CHANGELOG.md, add an entry, write release notes, or cut a version section; or when user-facing changes land that it should record. Not when the file is auto-generated in another format, and not for performing the release itself.
Stop AI coding agents from shipping sloppy code — over-abstraction, drive-by refactors, obvious comments, extra files, unrequested deps, giant unreadable files, or fishing outside the workspace for “inspiration”. Use when writing or editing code, implementing features, fixing bugs, splitting or organizing files, or when the user mentions slop, cleanup, keep it simple, minimal diff, file too big, hard to navigate, or stay in the repo.
Compile documents, notes, web content, transcripts, research materials, or code repositories into an evidence-grounded, visualization-ready knowledge graph with semantically typed entity nodes, statement-level provenance, and typed relationship edges. Use with ov compile to create or incrementally refresh `entities/*.md` node artifacts and a root `relations.jsonl` edge file for people, organizations, groups, animals, places, products, projects, systems, services, modules, datasets, standards, documents, events, and other identifiable things.
Installs and configures ktx, the open-source context layer for data agents — runs ktx setup non-interactively with hidden CLI flags, configures database connections and embeddings, installs agent integration, and verifies readiness. Use when the user asks an agent to add ktx to a project, connect data sources, install agent rules, ingest schema, or troubleshoot a local ktx install.
Use when a finished feature branch needs integration or disposition — produces a local merge, pull request, kept branch, discard, or block with reviewer-readable commits and pull-request text. Not for reviewing an existing PR (inspect-change) or cutting a version (cut-release).
INVOKE THIS SKILL when asked about and/or have to implement package versions, installation, or dependency management for LangChain, LangGraph, LangSmith, or Deep Agents. Covers required packages, minimum versions, environment requirements, versioning best practices, and common community tool packages for both Python and TypeScript.
New to MCP?
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.