Intelligent Terminal is an experimental fork of Windows Terminal that adds native support for AI agents through the Agent Client Protocol. It is for Windows users who want to manage compatible agent command-line tools alongside terminal tabs, profiles, themes, shells, and keyboard shortcuts, and the catalogue entries provide instructions, skills, hooks, and a plugin for working with it.
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.
npx agentmods add skills/microsoft/intelligent-terminal/copilot-pr-review-loopnpx skills add microsoft/intelligent-terminal --skill copilot-pr-review-loopgit clone --depth 1 https://github.com/microsoft/intelligent-terminalWrote 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/skills/microsoft/intelligent-terminal/copilot-pr-review-loop)<a href="https://agentmods.dev/skills/microsoft/intelligent-terminal/copilot-pr-review-loop"><img src="https://agentmods.dev/badge/skills/microsoft/intelligent-terminal/copilot-pr-review-loop.svg" alt="Measured on agentmods" 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 | $0.00091 | $0.02982 |
| Opus 5 | $0.00046 | $0.01491 |
| Sonnet 5 | $0.00018 | $0.00596 |
| Haiku 4.5 | $0.00009 | $0.00298 |
Grade A, and why
copilot-pr-review-loop 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 6d 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.
How it starts
The opening of the file, as written. The whole thing — 207 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Copilot PR Review Loop
Drive any GitHub pull request through repeated rounds of Copilot code
review until the agent has done its job — every Copilot finding has
a reply from the agent (fix-acknowledgement, decline-with-rationale,
or explicit escalate-to-user hand-off). Remaining open threads, if
any, are deliberate hand-offs to the human merge owner — they're
not loop failures. Repository-agnostic — works on any repo that has
Copilot Code Review enabled, run from a machine with gh CLI
installed and authenticated (see Prerequisites).
When to Use This Skill
- The user asks to "request Copilot review" or "run a Copilot review loop" on a PR.
- A PR is functionally complete and the user wants a final correctness pass via repeated automated review rounds.
- A previous Copilot review on the PR has left open threads that need triage, fixing, replying, and resolving.
When NOT to Use This Skill
- The PR is still under active design — wait until the structure is stable; otherwise findings churn round-over-round.
- The user wants human reviewer feedback, not Copilot's.
Prerequisites
ghCLI installed and authenticated against the target repository.- PowerShell on PATH — Windows PowerShell 5.1+ (
powershell.exe) or PowerShell 7+ (pwsh). Both are tested. - Copilot Code Review is the primary use case (
01-request-review.ps1uses GraphQLrequestReviewsByLoginto trigger Copilot). It is NOT a hard requirement — if01-request-review.ps1fails because Copilot isn't enabled on the repo / account, the agent can still drive existing review threads (human, advanced-security, etc.) to completion by running steps 3–8 once as a single iteration; just skip the trigger + wait. There is no auto-detect for "Copilot unavailable" — the agent makes that decision after the trigger fails (the script can't reliably tell "Copilot disabled" from "Copilot enabled but not yet triggered" from API state alone).
What ships with it
10 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- references/03-triage-criteria.md 6.0 KB
- references/06-reply-templates.md 3.1 KB
- references/api-quirks.md 6.3 KB
- references/workflow.md 11 KB
- scripts/_lib.ps1 5.0 KB runs code
- scripts/01-request-review.ps1 9.2 KB runs code
- scripts/02-check-review-status.ps1 15 KB runs code
- scripts/03-list-open-threads.ps1 3.4 KB runs code
- scripts/08-reply-and-resolve.ps1 2.1 KB runs code
- scripts/10-cleanup-outdated.ps1 3.3 KB runs code
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.
- 6d ago First seen · 207 lines · 91 tokens per session scan A 662e11cd7a0f
copilot-pr-review-loop is a skill published in the GitHub repository microsoft/intelligent-terminal (1,925 stars, last pushed today), licensed MIT. It adds 91 tokens to every session and 2,982 once invoked, about $0.0005 per session on Opus 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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