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 reviewgit 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)<a href="https://agentmods.dev/skills/raine/consult-llm/review"><img src="https://agentmods.dev/badge/skills/raine/consult-llm/review/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"><img src="https://agentmods.dev/badge/skills/raine/consult-llm/review.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to high
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 →
- high Data Exfiltration · line 102 Code or instructions that leak agent conversation context to external services, potentially exposing sensitive user interactions.Fix: Remove any code that sends prompts, responses, or session data externally. Preserve user privacy; never exfiltrate conversation content.
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 | $0.00040 | $0.02275 |
| Opus 5.5 | $0.00016 | $0.00910 |
| Sonnet 5.5 | $0.00008 | $0.00455 |
| Haiku 4.5 | $0.00004 | $0.00228 |
Grade A, and why
review 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 — 221 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Collect critical, honest feedback from all LLMs on an artifact. Push back on weak arguments. Report both consensus findings and unresolved disagreements.
Phase 0: Load consult-llm skill
Load the consult-llm skill before proceeding — it defines the invocation contract (stdin heredoc, flags, output format, multi-turn). Do not call the CLI without loading it first.
Arguments: $ARGUMENTS
Check the arguments for flags:
Mode flags:
--rounds N→ number of critique rounds (default: 2, max: 3)--dry-run→ skip the final synthesis, just show raw reviews--models <list>→ comma-separated selectors/model IDs to use as reviewers (default:gemini,openai,anthropic,deepseek)
Strip all flags from arguments to get the review target — a file path, directory, or topic description.
Set variables:
REVIEWERS: list of model selectors from--modelsflag, or["gemini", "openai", "anthropic", "deepseek"]if omitted- Build the
-mflags by repeating-m <selector>for each reviewer
Available Reviewers
Discover which selectors and models are available in this environment:
!`consult-llm models`
Default reviewers (used when no --models flag is given): gemini, openai, anthropic, deepseek — all four selectors that have a configured backend.
Override with --models flag: --models gemini,openai to review with only two, or --models gemini,openai,anthropic for three. Any selector or exact model ID from the list above is accepted.
Critical Rule: No Sycophancy
This skill exists to find problems, not to validate. Instruct every LLM call with:
- Be critical. The goal is to find weaknesses, gaps, and risks — not to praise.
- Disagree openly. If something looks wrong, say so directly. Do not soften criticism to be polite.
- Push back. If another reviewer dismissed a concern too easily, challenge them.
- Unresolved disagreements are fine. Not everything needs consensus. Flag genuine disagreements clearly rather than papering over them.
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 · 221 lines · 40 tokens per session scan A b0b718f9875b
review is a skill published in the GitHub repository raine/consult-llm (142 stars, last pushed 14d ago), licensed MIT. It adds 40 tokens to every session and 2,275 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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