agentsclimarketplace

Cross review

Skill corticalstack/flow/.claude/skills/cross-review

Run an OPTIONAL, advisory cross-vendor code review of the current branch's diff vs main using a configurable second-opinion backend (Azure Foundry next-gen, GitHub Models, or Copilot CLI - all OAuth, all keyless). Explicit-only; run only when invoked via /cross-review or when the user explicitly asks for a cross-vendor review, never autonomously.From its SKILL.md

Install
npx -y skills add corticalstack/flow --skill cross-review

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

2 things to look at

  • 1 stars1 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.
  • runs commandsInstructs the agent to run 1 command, including `bash ${CLAUDE_SKILL_DIR}/scripts/review.sh "$1" 2>&1`.

SKILL.md

1.9 KB, 367 tokens by cl100k_base, as published. Nobody here has run it

Cross-vendor review

You are running a cross-vendor code review on the current branch's diff vs main, using the reviewer profile the user named (or the default from profiles.json). This is a second opinion from a model in a different vendor lineage. It is advisory only: surface findings, do not apply changes.

Run the review

!bash ${CLAUDE_SKILL_DIR}/scripts/review.sh "$1" 2>&1

Present the result

The script printed the findings above and saved a markdown copy under flow/reviews/. In your reply:

  1. Surface the findings clearly to the user, with the saved file path so they can re-read it.
  2. Do NOT modify any code based on what the reviewer said. The reviewer is a second opinion, not an instruction.
  3. If the user asks you to act on a specific finding, treat it as a separate task and apply your own judgement; the reviewer may be wrong.

If the script errored

Common causes (the printed error points at one of these):

  • profiles.json missing: copy .claude/skills/cross-review/profiles.example.json to profiles.json and edit endpoints/models.
  • Authentication: az login (Foundry), gh auth login (GitHub Models), copilot auth login (Copilot CLI).
  • Tool not installed: az, gh, copilot, jq, or curl missing.
  • No diff vs main: the branch has no changes to review.

Tell the user the specific error from the script output and the fix; full setup is in docs/cross-vendor-review.md.

What ships with it: 2 files

11.6 KB alongside SKILL.md, 1 of them executable

scripts/

Gives 0 of the 12 instructions most review quality skills give in 367 tokens

Counted across 1,273 of the 2,403 authors here whose files we hold, read 2026-09-06

  • Ask one question at a timein 63 of 1273, across 62 files
  • Provide a recommended answer for each questionin 47 of 1273, across 45 files
  • Rank findings by severityin 44 of 1273
  • Use parameterized queries for database accessin 38 of 1273, across 20 files
  • Validate all user input with schemasin 33 of 1273, across 15 files
  • Store secrets in environment variablesin 32 of 1273, across 14 files
  • Explore the codebase to answer questionsin 31 of 1273, across 29 files
  • Store tokens in httpOnly cookiesin 30 of 1273, across 12 files
  • Implement rate limiting on API endpointsin 30 of 1273, across 12 files
  • Sanitize user-provided HTMLin 29 of 1273, across 11 files
  • Return generic error messages to usersin 28 of 1273, across 10 files
  • Cite file and line for every findingin 28 of 1273, across 25 files

Said here and by no other author read

  • Surface findings clearly to the user
  • Provide saved file path to the user
  • Report script errors and suggested fixes

Grouped from the skills themselves: near-identical wordings counted once, and counted by distinct author, so one author publishing three of these counts once. Length counted with cl100k_base; the agent that loads this file may tokenize it differently.

Keep looking

Skills are one crate of 325,949. Ordering is by how many stacks a row turns up in, so the top of any crate is what has actually been picked rather than what has the most stars.