Run multi-model reviews for code changes, PRs, and risky edits.
The materials indicate this is an open-source, prompt-only code review skill with no required secrets, no declared remote endpoints, and no stated local execution or write capability, so overall risk appears low. The main consideration is read-only access to workspace code changes for review, but no clear overreach or exfiltration red flags are present.
The materials explicitly state that no keys or environment variables are required, and there is no indication of requesting API tokens, account credentials, or other sensitive secrets, so credential exposure and misuse risk is low.
No remote endpoints are declared, and the system flags it as prompt-only; the README mainly describes review workflow and multi-model prompting, with no explicit indication that user code or data is sent to third-party services.
The material describes it as a read-only review skill, with no installation scripts, shell execution, local process spawning, or system capability invocation mentioned; based on the provided facts, it does not exhibit code execution behavior.
Its purpose is to review diffs, PRs, and code changes, so it would need to read repository or specified file contents as part of normal operation; this is standard read-only access for a code review skill, with no indication of write/delete actions or data access beyond its stated scope.
The source is an open-source GitHub repository and is auditable, and the system already classifies it as open-source; while 0 stars, no declared license, and unknown maintenance reduce maturity signals, there are currently no signs of closed-source exfiltration, suspicious installation chains, or clear supply-chain red flags.
Copy the install command and let the AI configure it · recommended for beginners
Please install the "council-review" skill from askskill: 1. Download https://raw.githubusercontent.com/microsoft/vscode-team-kit/main/model-council/skills/council-review/SKILL.md 2. Save it as ~/.claude/skills/council-review/SKILL.md 3. Reload skills and tell me it's ready
Please run a multi-model review on this PR, focusing on logic errors, edge cases, maintainability, and regression risks, then summarize agreements and disagreements.
A consolidated review with key issues, risk levels, fix suggestions, and a summary of model agreements and disagreements.
Have multiple models independently inspect these recent code changes, identify possible bugs, missing test cases, and unsafe implementations, then rank them by severity.
A severity-ranked issue list with triggering conditions, impact scope, and recommended additional test coverage.
Please have multiple models discuss the same review findings, compare which issues are valid, identify disputed conclusions, and recommend whether the change should be merged.
A final post-discussion conclusion stating required fixes, acceptable risks, and whether the change should be merged.
Read-only review powered by a council of model-pinned reviewers. Changes are usually not 100% correct — this skill exists to catch what slipped through. The goal is not broad commentary; it is a short list of concrete issues where the council agrees, plus transparent disclosure of where they disagree.
Spawn subagents with the model parameter to pin each to a different model. Use all three when available, at least two otherwise:
GPT-5.5Claude Opus 4.6GPT-5.3-CodexEach subagent receives the same system preamble:
You are an independent subagent for read-only code research. MUST stay read-only. Stay within the requested scope. Do not speculate. Do not suggest patches. For every finding, cite exact files and lines. Form your own view from first principles. Do not anchor to the provided context.
Before fanning out, build a preliminary orientation that each reviewer will receive. This is a starting point, not ground truth — reviewers are expected to contradict it if their own investigation leads elsewhere. Do not paste the raw diff — reviewers have tools to read code themselves.
The summary should include only:
Keep the summary under ~50 lines. Do not flag risk areas or pre-diagnose issues — leave reviewers to form independent assessments. They get better results reading code in context than scanning a pre-interpreted summary.
Spawn all subagents in parallel at once:
model parameter. Prepend the system preamble to the reviewer prompt below.Classify every finding into one of three buckets:
Drop style chatter, linter-catchable nits, and low-confidence speculation entirely.
Trigger this phase when the user asks to "discuss", "debate", or "cross-review" the findings, or when Phase 4 produces contested findings that could benefit from a second look.
This deliberation step is lightweight — it only re-examines the synthesized findings, not the full codebase.
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Set up Component Explorer with CLI, MCP, and VS Code tooling.
Add emoji reactions to GitHub issues or pull requests quickly.
Let AI agents read and write memory with environment-aware storage fallback.
Gather independent multi-model plans and debates for implementation and architecture decisions.
Get high-signal second opinions on plans, designs, and implementations early.
Fetch and review GitHub notifications quickly using the gh CLI.
Review code deeply across correctness, tests, security, performance, and product quality.
Simulate a multi-agent engineering team to review code for quality and risks.
Review implementation plans for gaps, assumptions, and sequencing before coding starts
Review repositories in natural language for security, performance, and code quality issues
Review GitHub pull requests in ChatGPT with comments, approvals, and change requests.
Review code or branches for correctness, compatibility, architecture, tests, performance, and security.