Total 54,409 skills, AI & Machine Learning has 9053 skills
Showing 12 of 9053 skills
Mock Interview Assistant. Conduct mock interviews based on your resume. Triggers: /hw-interview, Resume Mock Interview, Generate interview questions based on resume.
Rigorously evaluate an Agent Skill end-to-end across ANY coding-agent CLI — verify its scripts emit the documented numbers (deterministic checks), test whether its description triggers on the right prompts, and measure whether an agent following the SKILL.md beats a no-skill baseline (with/without pass-rate delta, mean ± stddev, benchmarked). Use whenever you need to test, benchmark, validate, grade, or quantify a skill's quality, check if a skill "actually works," compare two skill versions, optimize a skill's triggering, or set up an eval suite — even if the user just says "is this skill any good," "does my skill work," or "benchmark this skill." Drives Claude Code, OpenAI Codex, Antigravity (agy), Cursor, GitHub Copilot, Amp, opencode, or Grok in headless mode.
Write one model-agnostic video prompt spec, then compile it to whichever video model you can actually call. Use for cross-model prompt work, model comparison matrices, reusing one brief across providers, or when the target model is not yet available and the work must proceed on another one.
Use when a user wants to set up, configure, install, or reconfigure the opencode Fusion agent team - a strong main/build agent that plans and reviews but cannot edit files, delegating all edits to a cheaper sidekick subagent, plus an explore search agent and optional research/design/reviewer/vision specialists. Triggers include "set up fusion", "configure fusion", "install fusion", "fusion setup", "undo fusion" / "remove fusion", changing which models the main, sidekick, or explore agents use, or naming a subscription to start from a ready-made profile - e.g. "set up fusion with my OpenCode Go subscription" (also OpenCode Zen, ChatGPT Plus/Pro, GitHub Copilot). Writes the global opencode config under ~/.config/opencode/.
Read-only multi-agent review of a GitHub Pull Request, with the synthesized report posted back as a PR comment so the author is notified. Use when the user wants to review a GitHub PR (github.com or GitHub Enterprise) and post a structured review back to the PR conversation. Auto-detects the PR from the currently checked-out branch when no locator is supplied. Requires `gh`, `uuidgen`, `jq`, and `uv` or `python3` on PATH. Activates the `review-anvil` engine in read-only mode and orchestrates the shell helper for posting.
Documenting agents. Applicable when creating or editing skills, or modifying AGENTS.md or CLAUDE.md.
Divergent conversation before any artifact exists — open questions one at a time, alternatives including building nothing, converging on a routing decision and a handoff brief for the next skill. Runs before om-spec-writing and om-prepare-issue. Use when the user says "should we build this", "let's think this through", "I have an idea", "is this worth doing".
Configure safe agent behavior around PlanetScale MCP, Insights, schema recommendations, and repository work without autonomous production mutation.
Agent-callable Dropbox tools — upload, organize, find, and share files and folders. Use when the user wants to manage Dropbox content (save, move, search, share, list, or read files), including requests that don't name Dropbox explicitly, e.g. "save this report to my cloud storage" or "share that folder with Sam".
Agent-callable Alpaca trading tools — place and manage stock, crypto, and options orders, read account balances, positions, and portfolio history, look up assets and market hours, and read watchlists. Use when the user mentions Alpaca or wants to trade or inspect a brokerage account, even if they don't name Alpaca explicitly.
build a repo-local skill and install a matching iterated coding-agent GitHub Actions workflow, prompt, memory file, and reference templates
interview the user to design an agentic control loop (sensor, controller, actuator under disturbances) tailored to their codebase, then build it as locally-runnable components plus a scheduled coding-agent workflow