sidecar

A skill for starting a conversation with another AI model and bringing its results back into the current conversation.

In plain words
What is it for?
Use it when you want to ask Gemini, GPT, Codex, DeepSeek, Qwen, Grok, Mistral, Claude, or another supported model to investigate a task.
Why use it?
It gives you another model's perspective or lets several models explore a question in parallel.

Skill for Claude CodeCodex

Install

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.

agentmods
npx agentmods add skills/bourbondog/amicus/sidecar
Any agent
npx skills add BourbonDog/amicus --skill sidecar
Clone the repo
git clone --depth 1 https://github.com/BourbonDog/amicus

Made for: Claude Code, Codex.

Per session 218 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 10,612 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5 $0.00218 $0.10612
Opus 5 $0.00109 $0.05306
Sonnet 5 $0.00044 $0.02122
Haiku 4.5 $0.00022 $0.01061

Measured 2d ago against content hash 81d880f73b21, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

sidecar 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.

skills/sidecar/SKILL.md · 1,038 lines

How it starts

The opening of the file, as written. The whole thing — 1,038 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Amicus: Multi-Model Sidecar Tool

Spawn parallel conversations with different LLMs (Gemini, GPT, ChatGPT, Codex, o3, etc.) and fold results back into your context.

Slash invocation (/amicus:sidecar <model> <prompt…>)

When invoked as a slash command with arguments:

  • First argument (the model): $1
  • Full argument string: $ARGUMENTS

Treat $1 as the target model alias and the remainder of $ARGUMENTS as the prompt. If $1 is not a plausible model alias (gemini, gemini-pro, gpt, codex, deepseek, qwen, grok, mistral, glm, …), treat the ENTIRE argument string as the prompt and default to gemini. Then apply the critical rules below exactly as for any other invocation (run_in_background: true, --prompt-file for long briefings, interactive by default for a single model, never o3/o3-pro unprompted).

Operating Rules

These rules are mandatory for every amicus invocation in this skill:

  1. ALWAYS launch amicus CLI commands with the Bash tool's run_in_background: true. Never run amicus start/resume/continue in the foreground.
  2. The fold summary returns on stdout when the user clicks Fold in the GUI or the headless agent finishes. Use TaskOutput to read it when the background task completes.
  3. For long or multi-line briefings, write them to a temp file and pass --prompt-file <path> (mutually exclusive with --prompt; avoids shell-quoting hazards and argument-size caps).
  4. NEVER use o3 or o3-pro unless the user explicitly asks for it by name. These models are extremely expensive ($10-60+ per request). If the user asks for o3, warn them about the cost before proceeding. Default to gemini for most tasks. The CLI enforces this in code: a built-in budget gate refuses any model above a per-$/Mtok threshold (o3-pro class) before launch unless you pass --no-cost-gate; --max-cost <$> sets a soft estimated-total ceiling. When a run is refused with BUDGET_EXCEEDED, relay the gate's message — don't silently retry with the flag.
  5. When the user asks to query MULTIPLE LLMs simultaneously (e.g., "ask Gemini AND ChatGPT", "compare Gemini vs GPT"), ALWAYS use --no-ui (headless) for all of them unless the user explicitly requests interactive. Opening multiple Electron windows at once is disruptive. Launch them all in parallel with run_in_background: true.
  6. When the SAME prompt should go to N models, use amicus fanout --models "a,b,c" --prompt-file <path> --json (one headless wave, one JSON result) instead of N separate start calls. Different prompts per model → separate parallel amicus start --no-ui calls.
  7. For a SINGLE-model sidecar, DEFAULT to interactive — omit --no-ui so the Electron UI opens and the user can watch, converse, and click Fold. Use --no-ui for a single model only when the user asks for headless/autonomous, or for unattended bulk automation. Interactive launches still use run_in_background: true.
  8. If amicus is not on PATH (typical for plugin-only installs), run every command in this skill as npx -y amicus@latest <args> (e.g. npx -y amicus@latest start --model gemini --prompt "..."), or use the MCP tools (amicus_start, amicus_wait, amicus_status, amicus_read, …) instead. Do not conclude the tool is broken because amicus is not found.

Read the full file on GitHub · 1,038 lines

Changes

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.

  1. 2d ago First seen · 1,038 lines · 218 tokens per session scan A 81d880f73b21

Subscribe to this mod's changes

sidecar is a skill published in the GitHub repository BourbonDog/amicus (2 stars, last pushed 3d ago), licensed MIT. It adds 218 tokens to every session and 10,612 once invoked, about $0.0011 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 2026-08-31.