skills/consult/SKILL.md
Consult an external LLM with the user's query.
npx skillsauth add raine/consult-llm-mcp consultInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
3 of 9 scanners reported clean
Some scanners were skipped, did not run, or reported a non-clean status. Review each row below.
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.
Selectors resolvable in this environment (depends on configured API keys):
!`consult-llm models`
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.
consult-llm skillLoad it now. Follow its invocation contract for all CLI calls in this workflow.
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.
No model flag (default) — use the loaded consult-llm skill's default model-selection rules, plus -f <path> for each relevant file.
--browser — single call with --web (model flags are ignored in web mode).
data-ai
Coordinator workflow for multi-phase implementation across workmux worktrees. Generates or loads a master plan, dispatches phase agents using presets, verifies sentinels, merges serially, and performs integration verification.
testing
Interactive design session — agent facilitates a clarifying dialogue with the user, fans out to multiple LLMs in parallel for divergent approach generation, lets the user pick one, then co-designs the chosen approach with optional multi-LLM critique before saving.
development
Standalone multi-model code review of an existing diff. Multiple LLMs review in parallel; agent deduplicates, prioritizes by severity/confidence, and optionally applies localized fixes.
development
One-unit implementation workflow using presets. It writes a compact note or rich code-bearing plan, optionally consults external LLMs, optionally delegates execution to sideagent, verifies, commits, and summarizes.