Top Agent Skills

Inventory Items in a Fabric Workspace

When the user wants to optimize, improve, or increase conversions on any marketing page or form — including homepage, landing pages, pricing pages, feature pages, lead capture forms, or contact forms. Also use when the user says 'CRO,' 'conversion rate optimization,' 'this page isn't...

Markdown project memory for AI agents. Use for decisions, architecture, conventions, monorepo scopes, `.lore/`, or `lore` commands; not native `/init`/`/compact` or generic init/compress/audit/query.

Optimize content for AI search and LLM citations across AI Overviews, ChatGPT, Perplexity, Claude, Gemini, and similar systems. Use when improving AI visibility, answer engine optimization, or citation readiness.

Use the `gh` CLI for issues, pull requests, Actions runs, and GitHub API queries.

Scanpy is a scalable Python toolkit for analyzing single-cell RNA-seq data, built on AnnData. Apply this skill for complete single-cell workflows including quality control, normalization, dimensionality reduction, clustering, marker gene identification, visualization, and trajectory analysis.

Modern Angular (v20+) expert with deep knowledge of Signals, Standalone Components, Zoneless applications, SSR/Hydration, and reactive patterns.

Build native UI with the @expo/ui package: real SwiftUI on iOS and Jetpack Compose on Android rendered from React in an Expo or React Native app. Covers universal cross-platform components (Host, Column, Row, Button, Text, List, and more imported from @expo/ui), drop-in replacements...

Human-in-the-loop web studio to tune AI-generated output by eye. Stand up a local interactive studio (sliders, pickers, drag handles) or an inline edit/highlight/comment annotation studio for prose & media, instead of guessing values or shipping a static comparison grid.

When the user wants help with pricing decisions, packaging, or monetization strategy. Also use when the user mentions 'pricing,' 'pricing tiers,' 'freemium,' 'free trial,' 'packaging,' 'price increase,' 'value metric,' 'Van Westendorp,' 'willingness to pay,' 'monetization,' 'how much...

Audit a component or page for accessibility issues and fix them

Configure and maintain Python projects, dependencies, packaging, environments, linting, formatting, type checking, testing, builds, and publishing. Use for pyproject.toml, an existing repository toolchain, or repository-level Python tooling work, not for a standalone script.

Use when answering difficult or disputed TypeScript questions about assignability, inference, narrowing, variance, generics, conditional or mapped types, declarations, or compiler-option semantics.

Use to test whether a support control actually operates, the way an auditor would test it, rather than confirming it exists on paper. Trigger for "test our controls", "does this control actually work", second-line assurance over support, control effectiveness testing, preparing for an internal audit, or a control that passed review and then failed in practice.

Use to find the activation blockers visible in support contacts from new customers, before they show up in retention numbers months later. Trigger for "why aren't new customers activating", "what do new customers contact us about", onboarding drop-off, time-to-value analysis, first-90-days support experience, or a retention problem that traces back to activation.

Use to analyse touch time vs elapsed time across the resolution path — queue wait, customer wait, back-office wait, and where elapsed time hides real delay. Trigger for "flow efficiency", "touch time vs elapsed time", "where is the time going", "customers waiting on us", back-office bottlenecks, idle time in cases, or end-to-end time much longer than handle time.

Use to verify that the chain from conversation to evaluation to decision is complete and reconstructible, so a QA score or a customer outcome can be explained months later. Trigger for "can we prove why this decision was made", "is our audit trail complete", "reconstruct how this score was produced", evidencing an AI-assisted decision, or an auditor asking how a conclusion was reached.

Use to design a safe pipeline that writes derived attributes back into a helpdesk — risk scores, segments, health flags — without corrupting agent workflows or creating an unexplainable feedback loop. Trigger for "push scores back into Zendesk", "sync warehouse attributes to the helpdesk", "write our churn risk onto the ticket", reverse ETL for support, or an automation that started firing on a synced field.

Use to write CX commentary that makes a number decision-ready — what moved, why (decomposed), so what, do what — separating noise from signal and stating confidence honestly. Trigger for "write the commentary", "explain this trend", "what should we say about the drop", weekly or monthly narrative, board pack prose, "why did QA fall", metric movement write-up, or when a report has charts but no story.

Use to find where support agents cross from information into regulated advice or a personal recommendation, and to design the boundary so they can still be helpful. Trigger for "are agents giving advice", "where's the line between information and advice", "should agents recommend a product", guidance versus advice boundary, agents answering "what would you do", or a complaint that an agent recommended something.

Full-novel craft + anti-slop pipeline with project mode (ledger, characters, foreshadow, worldbuilding bible), draft→review→revise gates, and stability-trap enforcement at scene/chapter/manuscript scale. Single-scene mode retained. Use for fiction, lore, character docs, dialogue, worldbuilding, or book projects.

Fast vector similarity search at billion scale.

Systematic methodology for auditing the Hermes Skills Hub (and external sources) to identify gaps, overlaps, redundancies, and conflicts when building a skill library for any project. Covers browsing, targeted searching, cross-referencing with web/GitHub, comparing candidates against existing inventory, verifying install safety, and planning installation order. Use when starting a new project and assembling tools, or when evaluating whether to add external skills beyond what's currently installed.

Interact with the Last.fm music data API: lookup user listening history, get artist/album/track metadata, discover similar music via collaborative filtering, explore global and per-country charts, search by artist/album/track, manage tags, and scrobble listening events. Use when the user asks about music data, listening statistics, music recommendations, similar artists, charts, or wants to scrobble or love tracks.

Search and discover movies, TV shows, and trending content via The Movie Database (TMDb) API v3. Use when the user asks about movies, TV, film, cinema, genres, certifications, ratings, cast, upcoming releases, or trending media.

Stock quotes, history, search, compare, crypto via Yahoo.

Design motivation systems using Autonomy, Mastery, and Purpose (AMP) for products and teams. Use when the user mentions "intrinsic motivation", "gamification isnt working", "team incentives", "autonomy", "mastery", "purpose-driven", "employee engagement", or "reward systems". Also trigger when designing onboarding progression systems, fixing broken gamification, or building team structures that sustain high performance. Covers why carrot-and-stick fails and how to build progress systems. For habit-forming product loops, see hooked-ux. For retention behavior design, see improve-retention.

Structure software around the Dependency Rule: source code dependencies point inward from frameworks to use cases to entities. Use when the user mentions "architecture layers", "dependency rule", "ports and adapters", "hexagonal architecture", "use case boundary", "onion architecture", "screaming architecture", or "framework independence". Also trigger when decoupling business logic from databases or frameworks, defining module boundaries, or debating where to put business rules. Covers component principles, boundaries, and SOLID. For code quality, see clean-code. For domain modeling, see domain-driven-design.

Build type-safe AI agents and graph-based workflows with PydanticAI and PydanticGraph. Agent creation, function tools, capabilities, dependency injection, structured output, streaming, multi-agent patterns, testing, evals, and graph state machines. Use whenever you are building agents, tool-using LLM workflows, or graph-based state machines in Python.

Query hyper-local weather from a WeatherFlow Tempest station: current conditions, 7-day forecast, historical observations, and real-time UDP broadcasts. Use when the user asks about the weather, temperature, rain, wind, humidity, forecast, or wants conditions from their own station rather than a generic weather service.

Systematic audit of all installed tools, packages, and dependencies for available updates. Covers Homebrew, pip, npm/pnpm, system software updates, git repos, and key CLI tools. Run when the user asks "check if anything needs updating" or similar maintenance queries.

Manage scheduled cron job pipelines — review overnight runs, detect missed jobs (machine-sleep pattern), run catch-up batches with proper spacing, and integrate results into the morning briefing. Covers the overnight maintenance pipeline common on macOS machines that sleep at night.

Track Apple devices and AirTags via FindMy.app on macOS using AppleScript and screen capture.

Set up and run HeartMuLa, the open-source music generation model family (Suno-like). Generates full songs from lyrics + tags with multilingual support.

Manage Apple Notes via the memo CLI on macOS (create, view, search, edit).

Set up GitHub authentication for the agent using git (universally available) or the gh CLI. Covers HTTPS tokens, SSH keys, credential helpers, and gh auth — with a detection flow to pick the right method automatically.

Parameter-efficient fine-tuning for LLMs using LoRA, QLoRA, and 25+ methods. Use when fine-tuning large models (7B-70B) with limited GPU memory, when you need to train <1% of parameters with minimal accuracy loss, or for multi-adapter serving. HuggingFace's official library integrated with transformers ecosystem.

Interact with X/Twitter via the x-cli terminal client using official X API credentials. Use for posting, reading timelines, searching tweets, liking, retweeting, bookmarks, mentions, and user lookups.

GGUF format and llama.cpp quantization for efficient CPU/GPU inference. Use when deploying models on consumer hardware, Apple Silicon, or when needing flexible quantization from 2-8 bit without GPU requirements.

Manage Apple Reminders via remindctl CLI (list, add, complete, delete).

Edit PDFs with natural-language instructions using the nano-pdf CLI. Modify text, fix typos, update titles, and make content changes to specific pages without manual editing.

Runs LLM inference on CPU, Apple Silicon, and consumer GPUs without NVIDIA hardware. Use for edge deployment, M1/M2/M3 Macs, AMD/Intel GPUs, or when CUDA is unavailable. Supports GGUF quantization (1.5-8 bit) for reduced memory and 4-10× speedup vs PyTorch on CPU.

Control a running TouchDesigner instance via twozero MCP — create operators, set parameters, wire connections, execute Python, build real-time visuals. 36 native tools.

Extract text from PDFs and scanned documents. Use web_extract for remote URLs, pymupdf for local text-based PDFs, marker-pdf for OCR/scanned docs. For DOCX use python-docx, for PPTX see the powerpoint skill.

Use a live Jupyter kernel for stateful, iterative Python execution via hamelnb. Load this skill when the task involves exploration, iteration, or inspecting intermediate results — data science, ML experimentation, API exploration, or building up complex code step-by-step. Uses terminal to run CLI commands against a live Jupyter kernel. No new tools required.

Systematic multi-phase codebase audit for technical debt, architecture mapping, and defect detection. Read-only scanning before any modifications. Supports Vue/uni-app, JS/TS projects.

Save backtest results to SQLite database for comparison. Trigger when: (1) tracking backtest history, (2) comparing model performance, (3) querying best backtests.

Use when tuning RL reward function weights, fixing reward hacking, or addressing P&L gradient issues

Fix F821 undefined name errors for optional dependencies using TYPE_CHECKING

Populate symbol database with crypto symbols before training. Trigger when: (1) live trader fails with 'CRYPTO VIOLATION', (2) no crypto models in models/rl_symbols/, (3) db.get_candidates(asset_types=['crypto']) returns 0, (4) starting fresh training without crypto.

Evaluate model quality with task-appropriate metrics and systematic error analysis. Use when: (1) comparing models, (2) analyzing failures, (3) setting go/no-go thresholds. NOT for: production monitoring implementation.

Guide users through a structured workflow for co-authoring documentation. Use when user wants to write documentation, proposals, technical specs, decision docs, or similar structured content. This workflow helps users efficiently transfer context, refine content through iteration, and verify the doc works for readers. Trigger when user mentions writing docs, creating proposals, drafting specs, or similar documentation tasks.

Use the public `orca` CLI to operate Orca-managed worktrees, folder contexts, terminals, repos, automations, worktree comments, and the browser embedded inside the Orca app. Use when the user says "$orca-cli", "use orca cli", "Orca worktree", "child worktree", "cardStatus", "spawn codex/claude in a worktree", "read/wait/send Orca terminal", "terminal send", "full handoff", "handover", "give this to another agent", "another worktree", "Orca browser", or "control the browser inside Orca". Prefer this over raw `git worktree`, ad hoc PTYs, Playwright, or Computer Use when the task touches Orca-managed state. Use Computer Use for browser windows, webviews, or desktop UI outside Orca's embedded browser.

Use Orca's computer-use CLI to inspect and operate local desktop app windows through accessibility trees, screenshots, and safe UI actions. Use for desktop app interaction: list apps/windows, get app state, read visible UI, click controls, type, press keys, scroll, drag, set values, or perform accessibility actions. Also use for browser windows, webviews, Orca app UI, or other desktop UI. Triggers include "computer use", "orca computer", "read Spotify", "read Slack", "control/click/read in a desktop app", and "get app state".

Ink terminal renderer for json-render that turns JSON specs into interactive terminal UIs. Use when working with @json-render/ink, building terminal UIs from JSON, creating terminal component catalogs, or rendering AI-generated specs in the terminal.

Create comprehensive test scenarios from user stories with test objectives, starting conditions, user roles, step-by-step actions, and expected outcomes. Use when writing QA test cases, creating test plans, defining acceptance tests, or preparing for feature validation.

Guide users through writing a systematic literature review (SLR) following the PRISMA 2020 framework. Use this skill whenever the user mentions 'systematic review', 'systematic literature review', 'SLR', 'PRISMA', 'PRISMA 2020', 'PRISMA flow diagram', 'PRISMA checklist', or asks for help writing, structuring, or auditing a literature review that follows reporting guidelines. Also trigger when the user asks about inclusion/exclusion criteria for a review, search strategies for databases like Scopus/WoS/PubMed, study selection processes, risk of bias assessment, or narrative synthesis for a review paper. This skill covers the full PRISMA 2020 checklist (27 items), produces a Word document manuscript in strict journal article format, generates an annotated PRISMA flow diagram, and enforces APA 7th Edition referencing throughout. It does NOT cover meta-analysis or statistical pooling. By Chuah Kee Man.

VS-Enhanced Journal Matcher with Journal Intelligence MCP — Real-time journal data pipeline with checkpoint-based human decisions. Uses OpenAlex + Crossref APIs for live metrics. Light VS applied: Avoids IF-centric recommendations + multi-dimensional matching strategy Use when: selecting target journals, planning submissions, comparing publication options Triggers: journal, submission, impact factor, academic journal, publication, submit

Paper Retrieval Agent - Multi-database paper fetching from Semantic Scholar, OpenAlex, arXiv Handles rate limiting, deduplication, and PDF URL extraction Use when: fetching papers, searching databases, paper retrieval Triggers: fetch papers, retrieve papers, database search, Semantic Scholar, OpenAlex, arXiv

Perform an exhaustive, multi-pass proofread and copy-edit of an applied-microeconomics manuscript to top-economics-journal standards (AER, QJE, Econometrica, ReStud), checking prose, equations, table notes, footnotes, and citations for avoidable errors. Use before submitting or circulating an empirical economics paper.