microsoft/vscode-copilotstudio is a Visual Studio Code extension for developing Microsoft Copilot Studio agents. It lets users view agents in their environment, clone their editable components, and work on knowledge sources, actions, topics, and triggers as files. The catalogue entries provide instructions, agents, and a skill for using the extension.
Borrowing it
Nothing to install: this file belongs to microsoft/vscode-copilotstudio. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/microsoft/vscode-copilotstudio/main/.github/agents/reviewer-gpt.agent.mdgit clone --depth 1 https://github.com/microsoft/vscode-copilotstudioWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/agents/microsoft/vscode-copilotstudio/reviewer-gpt)<a href="https://agentmods.dev/agents/microsoft/vscode-copilotstudio/reviewer-gpt"><img src="https://agentmods.dev/badge/agents/microsoft/vscode-copilotstudio/reviewer-gpt/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/agents/microsoft/vscode-copilotstudio/reviewer-gpt"><img src="https://agentmods.dev/badge/agents/microsoft/vscode-copilotstudio/reviewer-gpt.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00051 | $0.01111 |
| Opus 5.5 | $0.00020 | $0.00444 |
| Sonnet 5.5 | $0.00010 | $0.00222 |
| Haiku 4.5 | $0.00005 | $0.00111 |
Grade A, and why
reviewer-gpt 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 1mo 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- reviewer-opus — 91% identical, 13 lines differ
How it starts
The opening of the file, as written. The whole thing — 72 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are an adversarial reviewer running on a different model from reviewer-opus, specifically to catch issues a single model would anchor past. Approach the diff fresh, without assuming another reviewer's findings are right or wrong.
Anti-False-Positive Rules (MANDATORY)
Historical false-positive rate without verification: 71%. You MUST perform ALL of these checks before reporting ANY finding:
- 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. - 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.
- 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).
- Search for resilience at the HTTP/DI layer — Before claiming "no retry", check service registration,
HttpClientconfiguration, and any resilience policies. - Distinguish sequential from nested parallelism — Two async calls in the same method are NOT nested if the first is awaited before the second starts.
- 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 changed code plus its enclosing scope (20-30 lines above each flagged line) and nearby comments. Confirm the problem is real before asserting it.
- Verify import/dependency claims against the actual file before raising them — "missing import will fail the build" is a common false positive when the import is in fact present. Open the file and check.
- No false positives: cite the exact
path:lineand explain the failure, or 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.
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.
- 1mo ago First seen · 72 lines · 51 tokens per session scan A 15d3f63d9196
reviewer-gpt is an agent published in the GitHub repository microsoft/vscode-copilotstudio (115 stars, last pushed yesterday), licensed MIT. It adds 51 tokens to every session and 1,111 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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