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 consultgit 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/consult)<a href="https://agentmods.dev/skills/raine/consult-llm/consult"><img src="https://agentmods.dev/badge/skills/raine/consult-llm/consult.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 | $0.00012 | $0.00847 |
| Opus 5 | $0.00006 | $0.00424 |
| Sonnet 5 | $0.00002 | $0.00169 |
| Haiku 4.5 | $0.00001 | $0.00085 |
Grade A, and why
consult 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 2d 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 — 72 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Consult an external LLM with the user's query via the consult-llm CLI.
Load the consult-llm skill before invoking — it defines the invocation contract (stdin heredoc, flags, output format, multi-turn). 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:
Model flags: any --<selector> from the Models block above selects that model (e.g. --gemini, --openai, --deepseek, --minimax). Repeat for multiple models — they run in parallel.
Translate model flags according to the loaded consult-llm skill's model-selection rules.
Mode flags:
--browser→ use web mode (--web, copies prompt to clipboard)--background→ run the Bash call in background mode (run_in_background)
Strip all flags from the arguments to get the user query.
Workflow
0. Load consult-llm skill
Load it now. Follow its invocation contract for all CLI calls in this workflow.
1. Gather context
- Use Glob/Grep to find relevant files.
- Read key files to confirm relevance.
- Do enough research to understand how the requested behavior actually works. Before starting, think about what resources would be useful to obtain first: relevant source files, tests, logs, generated files, config, examples, command output, external docs, or authoritative upstream source. Gather the cheapest useful evidence before consulting.
- Do not stop at the first plausible file, definition, setting, or example. Follow references, callers, related tests, and runtime usage until you can explain the current behavior and the likely impact of changing it.
- Select files that let reviewers understand the behavior being discussed and verify your assumptions.
2. Invoke
One or more --<selector> flags — single call with one -m <selector> per flag, plus -f <path> for each relevant file. Multiple selectors run in parallel and the CLI returns a combined response with per-model sections.
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.
- 2d ago Changed · +4 lines e4687c798064
- 7d ago First seen · 68 lines · 12 tokens per session scan A 86fa18d65946
consult is a skill published in the GitHub repository raine/consult-llm (132 stars, last pushed 3d ago), licensed MIT. It adds 12 tokens to every session and 847 once invoked, about $0.0001 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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