Agent workflow skills
Workflow skills teach your agent how to operate: how to plan before acting, debug methodically, dispatch parallel subagents, automate the browser, and run autonomous task loops without supervision. They are the meta-skills that make every other skill more effective.
What your agent can do with agent workflows skills installed
- Break ambiguous tasks into structured plans before touching any code
- Dispatch parallel subagents for independent work streams and coordinate their outputs
- Automate browser tasks — navigate, fill forms, extract data, take screenshots — without writing custom scripts
- Debug using a systematic hypothesis-and-test loop rather than making random edits
- Discover and install new skills from skills.sh directly inside an agent session
- Close branches cleanly: run tests, write commit messages, open pull requests, request review
- Run a ralph loop: feed your agent a prd.json task list and let it work through every item autonomously, committing passing work and retrying failures without supervision
Skills in this category
find-skills
vercel-labs/skills
Discover and install skills from skills.sh directly inside an agent session
agent-browser
vercel-labs/agent-browser
Full browser automation: navigate, click, fill forms, extract data, and screenshot
skill-creator
anthropics/skills
Create, test, and publish new skills from within your agent
brainstorming
obra/superpowers
Structured ideation and problem decomposition frameworks
browser-use
browser-use/browser-use
Browser automation with visual understanding — interacts with pages based on what it sees
systematic-debugging
obra/superpowers
Hypothesis-driven debugging loop: observe, hypothesize, test, verify
writing-plans
obra/superpowers
Write structured implementation plans before starting complex tasks
executing-plans
obra/superpowers
Execute a plan step-by-step with checkpoints and verification at each stage
test-driven-development
obra/superpowers
TDD loop: write the failing test first, implement the minimal fix, verify, then refactor
requesting-code-review
obra/superpowers
Prepare code for review: self-review, test coverage, and pull request description
subagent-driven-development
obra/superpowers
Orchestrate specialized subagents for different parts of a task
verification-before-completion
obra/superpowers
Force a verification pass before any task is marked complete
dispatching-parallel-agents
obra/superpowers
Split work across parallel subagents and coordinate their outputs
using-git-worktrees
obra/superpowers
Use git worktrees to run parallel agent sessions on separate branches
finishing-a-development-branch
obra/superpowers
Branch close checklist: tests, commit message, pull request, and review request
ralph-tui-prd
subsy/ralph-tui
Generate a structured prd.json task list for use with ralph-tui's autonomous loop
ralph-tui-create-beads
subsy/ralph-tui
Create Beads tasks (git-backed, with dependencies) for ralph-tui
ralph-tui-create-json
subsy/ralph-tui
Create JSON-format task lists for ralph-tui
ralph-wiggum
fstandhartinger/ralph-wiggum
The Ralph Wiggum loop technique: simplified autonomous agent loop with minimal setup
ralph-loop
andrelandgraf/fullstackrecipes
Ralph loop implementation with agent mode for sustained autonomous task completion
Works with your agent
Agent workflows skills are compatible with Claude Code, Cursor, GitHub Copilot, Windsurf, Cline, Codex, Gemini CLI, and all agents that support the skills CLI.
Frequently asked questions
What is the difference between agent-browser and browser-use?
agent-browser is a CLI-driven automation tool, fast and reliable for structured tasks like form filling and data extraction. browser-use adds visual understanding: the agent sees the rendered page and interacts based on appearance rather than selectors. Use agent-browser for predictable automation, browser-use when the page structure is inconsistent or unknown.
Should I install both writing-plans and executing-plans?
They are designed as a pair. writing-plans handles upfront decomposition, turning a vague goal into a concrete sequence of steps. executing-plans handles the runtime behavior, following that sequence with checkpoints rather than free-running. Either works independently, but the combination is more reliable for multi-step tasks.
Can find-skills install skills mid-session without restarting?
Yes, and that is its primary use case. Ask your agent to find a skill relevant to what you're working on and it becomes available in the same session without restarting.
Is dispatching-parallel-agents only useful for large tasks?
Even moderately sized tasks benefit from parallelism when the work divides cleanly. Writing tests in one agent while another writes the implementation is a common pattern that works well for features of any size.