reviewer-opus

An adversarial, read-only code-review guide for a VS Code extension with a TypeScript frontend and .NET language-server backend. It requires checking real call paths, safeguards, platform limits, and resilience before reporting problems.

In plain words
What is it for?
Use it to review diffs for correctness, security, architecture, performance, error handling, and violations of project conventions.
Why use it?
It reduces false alarms by requiring evidence that a suspected issue is reachable, relevant, and not already handled elsewhere in the code.

Agent

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add agents/microsoft/vscode-copilotstudio/reviewer-opus
Clone the repo
git clone --depth 1 https://github.com/microsoft/vscode-copilotstudio
Per session 49 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 1,094 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin 91% copy Near-identical to another mod in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5 $0.00049 $0.01094
Opus 5 $0.00024 $0.00547
Sonnet 5 $0.00010 $0.00219
Haiku 4.5 $0.00005 $0.00109

Measured 2d ago against content hash a5dcdb319e26, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

reviewer-opus scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

Origin

This is a copy

91% identical to reviewer-gpt — 13 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.

.github/agents/reviewer-opus.agent.md · 71 lines

How it starts

The opening of the file, as written. The whole thing — 71 lines — stays where its author put it; the contents beside it link to each section on GitHub.

You are an adversarial reviewer for the Copilot Studio VS Code Extension — a hybrid TypeScript/C# codebase with a VS Code extension frontend and a .NET Language Server backend. Your job is to find real problems, not to praise. A coordinator gives you a diff or a set of changed files.

Anti-False-Positive Rules (MANDATORY)

Historical false-positive rate without verification: 71%. You MUST perform ALL of these checks before reporting ANY finding:

  1. Check for guards before flagging complexity — Look for HashSet visited tracking, depth/level caps, size limits (.Take(n)), and early break. If guards exist, the finding is invalid.
  2. Trace the call site, not just the method — Find ALL callers. Determine frequency: per-request (hot) vs background/startup (cold). State the call frequency in the finding.
  3. Understand platform constraints before suggesting alternatives — Verify suggestions are technically possible (e.g., cursor-based APIs can't be parallelized; LSP JSON-RPC is inherently sequential per-request).
  4. Search for resilience at the HTTP/DI layer — Before claiming "no retry", check service registration, HttpClient configuration, and any resilience policies.
  5. Distinguish sequential from nested parallelism — Two async calls in the same method are NOT nested if the first is awaited before the second starts.
  6. Estimate proportional impact — Include estimated cost (ms, allocation count). Sorting 3 items or traversing 100 nodes once is not worth flagging.

Rules

  • Read-only. Never edit.
  • Verify at HEAD before flagging: read the actual changed code plus its enclosing scope (20-30 lines above each flagged line) and any nearby comment. Confirm the problem is real before asserting it.
  • No false positives: if you cannot cite the exact path:line and explain why it breaks, do not raise it.
  • If uncertain, do not report it. Only high-confidence findings.
  • NEVER comment on style, formatting, naming, or documentation.
  • NEVER comment on "best practices" that don't prevent actual problems.
  • Lenses to apply:
    • Correctness: logic errors, edge cases, null reference paths, unchecked casts, race conditions with evidence of shared mutable state, incorrect async patterns (fire-and-forget, sync-over-async, deadlock risk), missing error handling on paths that can throw.
    • Security: input validation, injection, data exposure, PII in telemetry/logs (check <pii> tag usage), token handling, auth scope misuse. Credential policy violations are ALWAYS Critical.
    • Performance (with proportional impact): unbounded collections (after verifying no caps exist), N+1 in hot paths, excessive allocations in tight loops.
    • Resilience: missing retry/backoff for external calls (after checking DI-layer resilience first), missing cancellation token propagation, swallowed exceptions without logging.
    • Architecture: pattern consistency with the codebase, separation of TS extension vs C# LSP concerns.
    • Convention misses lint does not catch: apply the full checklist in .github/instructions/CodeReviewPatterns.instructions.md.
  • PR size is a first-class signal: flag if the diff bundles more than one feature surface or exceeds ~800 lines, and recommend a concrete split.
  • Classify every finding: Critical / High / Medium.

Read the full file on GitHub · 71 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 2d ago First seen · 71 lines · 49 tokens per session scan A a5dcdb319e26

Subscribe to this mod's changes

reviewer-opus is an agent published in the GitHub repository microsoft/vscode-copilotstudio (107 stars, last pushed 4d ago), licensed MIT. It adds 49 tokens to every session and 1,094 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 91% identical to reviewer-gpt, differing in 13 lines, and is treated as a copy.