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 skills add raine/consult-llm --skill review-panelgit clone --depth 1 https://github.com/raine/consult-llmWrote 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/raine/consult-llm/review-panel)<a href="https://agentmods.dev/skills/raine/consult-llm/review-panel"><img src="https://agentmods.dev/badge/skills/raine/consult-llm/review-panel/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/skills/raine/consult-llm/review-panel"><img src="https://agentmods.dev/badge/skills/raine/consult-llm/review-panel.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Excessive Agency · line 183 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
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 | $0.00039 | $0.02555 |
| Opus 5.5 | $0.00016 | $0.01022 |
| Sonnet 5.5 | $0.00008 | $0.00511 |
| Haiku 4.5 | $0.00004 | $0.00255 |
Grade A, and why
review-panel 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.
How it starts
The opening of the file, as written. The whole thing — 215 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Run a standalone multi-model review of a diff. Reviewers receive the same prompt independently; the agent synthesizes duplicate findings into a prioritized checklist and can optionally apply unambiguous fixes.
Load the consult-llm skill before proceeding — it defines the invocation contract (stdin heredoc, flags, output format, multi-model calls). Do not call the CLI without loading it first.
Available models
Selectors resolvable in this environment (depends on configured API keys):
!`consult-llm models`
Argument handling
Arguments: $ARGUMENTS
Check $ARGUMENTS for flags:
Reviewer flags: any --<selector> from the Models block above selects that reviewer (e.g. --gemini, --openai, --deepseek). Repeat for multiple. Translate model flags and defaults according to the loaded consult-llm skill's model-selection rules.
Diff flags:
--diff-base <ref>— base ref for review. Default is auto-detected so a feature branch is reviewed in full (see Phase 1 for the resolution order). Pass an explicit ref (HEAD,HEAD~3, a branch name, a SHA) to override.--fix— opt in to applying unambiguous localized fixes formust-fixfindings. Default is read-only report.
Strip all flags from arguments to get any user-supplied review focus. If no focus remains, review for correctness, regressions, security, and maintainability.
Phase 0: Load consult-llm skill
Load it now. Follow its invocation contract for all CLI calls in this workflow.
Phase 1: Identify changed files
Resolve <diff-base>:
- If
--diff-basewas passed, use it as-is. - Otherwise detect the repo's main branch (
git symbolic-ref refs/remotes/origin/HEAD→ striprefs/remotes/origin/, fall back tomainthenmaster) and usegit merge-base HEAD <main>(preferorigin/<main>if it exists locally, else the local<main>). The branch may not be pushed and may have no upstream — don't rely on@{upstream}. - If HEAD has no divergence from the resolved base (already on the main branch), fall back to
<diff-base>=HEADso the skill still reviews uncommitted changes. - Stacked branches and feature-off-feature workflows are not auto-detected — pass
--diff-base <parent>explicitly in those cases.
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 · 215 lines · 39 tokens per session scan A 2794d2f73f1e
review-panel is a skill published in the GitHub repository raine/consult-llm (142 stars, last pushed 14d ago), licensed MIT. It adds 39 tokens to every session and 2,555 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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