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SKILL verified MIT Self-run

Cursor Cli

skill-amirkiarafiei-subagent-cli-skills-cursor-cli · by amirkiarafiei

Delegates large multi-step work to Cursor CLI like a subagent—include full goal, prior decisions, scope, and constraints in prompts so isolated sessions stay aligned with the main thread (context handoff per Cognition-style delegation). Use for heavy edits, exploration, and agentic coding. Skip for trivial one-shot tasks or when everything is already in context.

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Install

$ agentstack add skill-amirkiarafiei-subagent-cli-skills-cursor-cli

✓ scanned · ✓ verified, works with Claude Code, Cursor, and more.

Security review

✓ Passed

No issues found. Passed automated security review. · v0.1.0 How review works →

  • Prompt-injection patterns
  • Secret / credential exfiltration
  • Dangerous shell & filesystem operations
  • Untrusted network calls
  • Known-malicious package signatures

What it can access

  • Network access No
  • Filesystem access No
  • Shell / process execution No
  • Environment & secrets No
  • Dynamic code execution No

From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.

View the full security report →

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Reliability & compatibility

Security review passed
0 installs to date
no reviews yet
21d ago

Declared compatibility

Claude CodeClaude Desktop

Compatibility is declared by the source manifest. End-to-end runtime verification is coming, see below.

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About

Cursor CLI (subagent/task delegation)

Use Cursor CLI to run a separate long-horizon pass over the repo: multi-step implementation, broad refactors, batch file writes, or deep exploration—similar to handing a task to a subagent. You stay orchestrator: smaller prompts, less context burn, often lower spend than doing the same work entirely in-session.

When to use Cursor CLI

  • Large or multi-step work: several files, phases, or checkpoints (feature slice, migration, test suite, docs sweep).
  • Heavy code generation or editing: Cursor drives --yolo tool use while you summarize outcomes and merge.
  • Parallel mental lane: you continue planning or reviewing while Cursor runs a bounded task.
  • User explicitly asks for Cursor or “use Cursor for this.”

When not to use

  • Small / single-step tasks answerable with one or two edits or a short explanation.
  • Tight feedback loops where the user wants rapid back-and-forth refinement in one thread.
  • Secrets or policy-sensitive flows—avoid piping credentials; redact before delegating.
  • Already-loaded context where duplicating the whole plan adds no value—handle locally.
  • Low ROI (Return on Investment): If the task is "needle-in-a-haystack" (requires high precision over a single line) or if the time to compose the Handoff Table exceeds the time to simply edit the file locally. Delegation should only be used when the "mental offloading" outweighs the "handoff overhead."

Delegation and context (critical)

Isolated subagent context saves tokens but splits the story: Cursor does not see the main session's full thread. Poor handoffs cause misread subtasks, conflicting assumptions (stack, style, APIs), and wasted edits.

When composing the single Cursor prompt, treat it as passing enough shared state, not just a title:

| Include | Why | |--------|-----| | Original goal | Same north star as the user—not only the immediate micro-task. | | Decisions already made | Framework, patterns, naming, auth approach, “use X not Y”—anything that would otherwise be guessed wrong. | | Scope | Paths, modules, and explicit out of scope / do-not-touch areas. | | Constraints | Performance, a11y, compatibility, review gates, “no new deps,” etc. | | Verification | Explicit command (e.g. npm test, lint) the subagent must run and pass before returning. | | Expected output | e.g. “summarize then list files changed,” “report only—no edits,” or “apply edits with minimal diff.” |

Model Selection & Discovery (Mandatory)

MANDATORY: Search the web for latest Cursor model names and pricing before selecting a model. You should also consult Artificial Analysis for the most up-to-date benchmarks, pricing, and model performance data.

Programmatic usage (required)

You MUST use Cursor CLI programmatically. Do NOT start interactive sessions.

| Requirement | Flag | |-------------|------| | Non-interactive | -p or --print | | Auto-approval | --yolo or --force | | Model Selection | --model [model_name] | | Mode Selection | --mode [agent|plan|ask] |

Command pattern

agent -p "GOAL: [goal] | DECISIONS: [decisions] | SCOPE: [paths] | CONSTRAINTS: [constraints] | OUTPUT: [format]" --yolo --model [model] 2>&1

After Cursor returns

  • Review diffs and security-sensitive areas (XSS, injection, auth)—do not merge blindly.
  • Run project checks (lint, test, typecheck) as appropriate.
  • Compress results for the user: summarize results for the user instead of pasting huge logs unless asked.
  • Reconcile context: note decisions, files touched, and remaining risks so the main session stays aligned.

Quick prompts

  • Delegate implementation: agent -p "GOAL: [goal] | DECISIONS: [decisions] | SCOPE: [paths] | CONSTRAINTS: [constraints] | VERIFICATION: [test_command] | OUTPUT: [format]" --yolo
  • Investigate: agent -p "GOAL: Map how [feature] works | SCOPE: [paths] | OUTPUT: concise file:line map" --mode ask
  • Plan: agent -p "GOAL: Design architecture for [feature] | SCOPE: [paths] | OUTPUT: architecture plan" --mode plan

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

Install and usage instructions live in the source repository linked above.

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Versions

  • v0.1.0 Imported from the upstream source.