Awesome-omni-skill cursor

Translates task requirements into Cursor CLI commands. Used by cursor-driver agent to execute coding tasks via Cursor.

install

source · Clone the upstream repo

git clone https://github.com/diegosouzapw/awesome-omni-skill

Claude Code · Install into ~/.claude/skills/

T=$(mktemp -d) && git clone --depth=1 https://github.com/diegosouzapw/awesome-omni-skill "$T" && mkdir -p ~/.claude/skills && cp -r "$T/skills/data-ai/cursor" ~/.claude/skills/diegosouzapw-awesome-omni-skill-cursor && rm -rf "$T"

manifest: skills/data-ai/cursor/SKILL.md

source content

Cursor CLI Skill Guide

Baseline Rules

Always apply these for programmatic (headless) execution:

```
-p "<prompt>"
```
— required for headless mode
```
--output-format text
```
— recommended for clean output capture

Command Templates

New Task (analysis/read-only)

agent -p "<prompt>" --mode ask --output-format text

New Task (with file edits)

agent -p "<prompt>" --mode agent --output-format text

New Task (planning only)

agent -p "<prompt>" --mode plan --output-format text

With model selection

agent -p "<prompt>" --model gpt-5 --output-format text

Resume Session (latest)

agent resume -p "<prompt>" --output-format text

Resume Session (specific)

agent --resume="<chat-id>" -p "<prompt>" --output-format text

List Previous Sessions

agent ls

Execution Modes

Task Type	Flag	Notes
Analysis, review, Q&A	`--mode ask`	Read-only, no file changes
Create or edit files	`--mode agent`	Full agent capabilities
Planning, architecture	`--mode plan`	Generates plan without execution

Model Selection

When the calling agent specifies requirements, translate to flags:

Requirement	Flag	Notes
Default / high-quality	`--model gpt-5`	Best for complex reasoning
Fast / cheap	`--model gpt-4o`	Quick, straightforward tasks
Claude	`--model claude-sonnet`	Anthropic model option

If not specified, use default model (no flag needed).

Output Formats

Format	Flag	Use Case
Text	`--output-format text`	Programmatic processing, CI/automation
Default	(none)	Interactive/human-readable output

Cloud Agent Handoff

For complex tasks requiring cloud processing, prefix the prompt with

agent "& refactor the auth module and add comprehensive tests"

Interpreting Results

Success indicators

Clean text output with expected content
Exit code 0
Response addresses the original request

Failure indicators

Non-zero exit code
Error messages in output
Missing expected deliverables

Scope creep indicators

Mentions of "I also..." or "While I was at it..."
Changes to files not mentioned in the original request
Response describes work beyond the original request

Redirection indicators

Output describes different work than requested
"Instead of X, I did Y..."
Solving a different problem than specified

After Completion

Report to user: "You can resume this Cursor session by saying 'cursor resume'."

Session Management

```
agent ls
```
— List all previous conversations
```
agent resume
```
— Resume most recent session
```
agent --resume="<id>"
```
— Resume specific session by ID

Error Handling

If command exits non-zero: stop and report the error
If output contains error messages: summarize and report
If output contains warnings: summarize and ask how to proceed

Reference

Useful Patterns

# Code review (read-only)
agent -p "Review src/auth.py for security issues" --mode ask --output-format text

# Implement feature
agent -p "Add input validation to the login form" --mode agent --output-format text

# Generate plan
agent -p "Plan the migration from REST to GraphQL" --mode plan --output-format text

# Continue previous work
agent resume -p "Now add unit tests for the changes"

# Cloud-powered complex task
agent "& analyze codebase architecture and suggest improvements"

Interactive Mode

For complex multi-step tasks, you may run

agent

without

-p

to enter interactive mode:

agent

Then provide prompts conversationally. Use this when:

The task requires back-and-forth dialogue
You need to inspect intermediate results before continuing
The task scope may evolve based on findings