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/surajssd/dotfiles/plan-multi-agent-reviewnpx skills add surajssd/dotfiles --skill plan-multi-agent-reviewgit clone --depth 1 https://github.com/surajssd/dotfilesWrote 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/surajssd/dotfiles/plan-multi-agent-review)<a href="https://agentmods.dev/skills/surajssd/dotfiles/plan-multi-agent-review"><img src="https://agentmods.dev/badge/skills/surajssd/dotfiles/plan-multi-agent-review.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 | $0.00197 | $0.04663 |
| Opus 5 | $0.00098 | $0.02331 |
| Sonnet 5 | $0.00039 | $0.00933 |
| Haiku 4.5 | $0.00020 | $0.00466 |
Grade B, and why
plan-multi-agent-review scanned grade B with 1 finding 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 5d 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.
Instruction-override phrasingmediumPrompt injection
Text telling the model to disregard its earlier instructions or safety rules is the shape of a prompt injection, whoever wrote it.
With reviewers now pointed at a live repo as cwd, that is a real exposure: a crafted "ignore previous instructions…" payload in a repo file or in the plan could drive a fully tool-enabled Downgraded: this mod is about security review, or the phrase is quoted, so it is likely naming the pattern rather than instructing it.
How it starts
The opening of the file, as written. The whole thing — 341 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Plan Multi-Agent Review
Run several AI coding CLIs as an independent review panel over a plan file — judged against the code repository it targets — then synthesize their reviews into one report. The value over a single reviewer is triangulation: a flaw three tools independently flag is almost certainly real; a concern only one raises is either a sharp catch or a false positive — and the report should make that distinction visible so the user knows where to spend attention.
The two inputs are a plan file (an explicit path — required) and the repo the plan is about. The sole artifact produced is a review of the plan. Nothing is posted anywhere; every artifact is a local file in a temp directory.
Requirements. git and at least one reviewer CLI are mandatory. A timeout/gtimeout binary
is used if present; otherwise a built-in bash watchdog enforces the per-reviewer timeout. The
scripts avoid GNU-only flags (readlink -f, mktemp --suffix) so they run on stock macOS as well
as Linux.
Trust boundary (read this). Two distinct exposures, because the panel feeds the plan and repo files into the reviewer CLIs and lets them explore a live working tree:
- Prompt-injection → tool execution. Two tools (
opencode,agy) run without a hard read-only sandbox —agywith its auto-approving--sandbox. Because this skill points every reviewer at a live repo as its working directory so it can verify the plan, a malicious file in that repo (or a crafted instruction inside the plan) could attempt to drive those tools. Run this on repos and plans you trust, or isolate those tools (throwaway worktree, network off). - Data egress. Every reviewer streams the plan and the repo content it reads to its model provider (Anthropic, OpenAI, GitHub, Google, …). This skill embeds the plan-referenced files and invites live exploration, so more repo content can leave the machine than a single diff would — don't review a plan whose repo holds secrets you can't share with the reviewers' backends.
Step 6 covers the specifics.
The orchestrator (you) never reviews the plan yourself. Your job is to gather context, dispatch the panel, and collate. If you inject your own opinions as if they were a reviewer's, you destroy the signal of how many independent tools agreed. You may reconcile and judge their findings during collation, but the findings must originate from the panel.
What ships with it
6 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.
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.
- 5d ago First seen · 341 lines · 197 tokens per session scan B 6c7b5cc340e3
plan-multi-agent-review is a skill published in the GitHub repository surajssd/dotfiles (5 stars, last pushed yesterday), licensed MIT. It adds 197 tokens to every session and 4,663 once invoked, about $0.0010 per session on Opus 5. A static security scan graded it B with 1 finding (instruction-override phrasing). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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