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thermos

thermos

github.com/cursorTesting & quality14.5k2.2k installsUpdated today
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About thermos

A skill for coordinating two parallel code-review passes and combining their findings. It is intended for reviewing a requested scope, pull request, branch, or changed files, with separate reviews for risks and code quality.

Key features

  1. 01Determine review scope
  2. 02Gather diff and file context
  3. 03Run two review agents in parallel
  4. 04Deduplicate and prioritize findings
  5. 05Summarize verdict and uncertainty

Use cases

  1. 01Review a pull request for bugs and security risks
  2. 02Assess changed files for maintainability issues
  3. 03Combine parallel reviewer results

Install

Copy the command and run it in a terminal.

$npx skills add https://github.com/cursor/plugins --skill thermos

After installing, type/thermosin Claude Code to confirm it loaded.

Details

From the project README

Thermos

Run the two thermo review passes as async background subagents in parallel, then synthesize their results.

Workflow

  1. Determine the review scope from the user request, PR, current branch, or relevant changed files.
  2. Gather the diff and any file/context excerpts needed for reviewers to evaluate the change without guessing.
  3. Launch both subagents in the same message with run_in_background: true:
    • subagent_type: "thermo-nuclear-review-subagent" for bugs, breakages, security, devex regressions, feature-flag leaks, and other branch-audit risks.
    • subagent_type: "thermo-nuclear-code-quality-review-subagent" for maintainability, structure, file-size growth, spaghetti, abstractions, and codebase-health risks.
  4. Pass each subagent the same scoped diff/file context and ask it to return prioritized findings with file references and evidence.
  5. After both finish, synthesize the results with findings first, deduplicated across reviewers. Weight overlapping findings more heavily, resolve disagreements with your own judgment, and keep summaries brief.

If individual background summaries are already visible to the user, do not restate them wholesale. Surface the unified verdict, the highest-signal findings, and any remaining uncertainty.

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