Agent Workflows · By cursor

Keeps a TSV decision log for long or unattended agent runs, one row per decision with what, why, evidence and result, so a reviewer can check the work later.

cursor/plugins

Starts three parallel reviewer subagents over the current conversation transcript, then turns their findings into concrete edits to existing skills.

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Adds a second, stronger model that the main agent consults before major decisions, when stuck and before finishing, controlled by /advisor commands.

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Splits a large goal into a tree of parallel Cursor cloud agents, with planners, workers and verifiers coordinated by a script and reporting through structured handoffs.

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Sketches types, signatures and module boundaries with stub bodies before real code, compares at least two designs, then implements against the chosen sketch.

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Generates a project-local skill that launches your app, exercises a feature the way a user would and captures evidence, for web, CLI, API or desktop projects.

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Runs one read-only reviewer subagent per configured model against a diff to challenge a change, then synthesizes a single verdict without applying any fixes.

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Runs several parallel attempts at the same task, picks the strongest as a base, grafts the best ideas from the others into it, then verifies the merged result.

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Designs a step-by-step playbook for large or unfamiliar work, runs it as a loop of hypotheses and measurements, and logs decisions so a human can review them later.

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Audits a project's verification skill and its feature map against the source and the live app, then ships at most one pull request of proven corrections.

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Answers how-does-this-work and where-should-this-live questions by exploring the codebase with read-only subagents and writing an onboarding-depth explanation.

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Launches many parallel cloud workers on slices of a task or on the same brief, waits for them all, and returns one aggregated report.

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Builds or refreshes a personal -mode skill that captures how you work, using patterns mined from your recent agent transcripts in the current workspace.

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Apply when context is filling up: large outputs, long files, repeated reads, fan-out planning.

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Start a Ralph Loop for iterative self-referential development.

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Explain the Ralph Loop plugin, how it works, and available skills.

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Launch both thermo-nuclear review subagents in parallel, then synthesize their findings.

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Cancel an active Ralph Loop. An agent skill from cursor/plugins.

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Orchestrate continual learning by delegating transcript mining and AGENTS.md updates to agents-memory-updater.

cursor/plugins