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fusion-selector

fusion-selector is a skill for Claude Code from tboome33/openrouter-fusion-mcp. It costs 123 tokens per session (743 once invoked), scanned A, original, MIT.

An interactive selector for OpenRouter Fusion, a service that sends a question to several AI models and combines their responses. It lists available presets, shows estimated cost and model roles, and guides you through choosing one before running it.

In plain words
What is it for?
Use it whenever you want Fusion to deliberate on a question, including requests such as “use Fusion” or “ask Fusion,” and need to choose the preset and reasoning settings interactively.
Why use it?
It prevents the wrong multi-model setup from being chosen by making the available options and indicative costs visible. It also keeps the user in control instead of selecting a preset automatically.

Skill for Claude Code

Written for Claude Code: shipped in a Claude Code plugin.

Part of the openrouter-fusion plugin — 1 skill, 1 command, 1 hook, 1 MCP server shipped together

not rated 0repo 2mo ago A scan 123 tokens original MIT

Good fit Use it whenever you want Fusion to deliberate on a question, including requests such as “use Fusion” or “ask Fusion,” and need to choose the preset and reasoning settings interactively.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/tboome33/openrouter-fusion-mcp/fusion-selector
View source ↗ tboome33/openrouter-fusion-mcp
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.

Any agent
npx skills add tboome33/openrouter-fusion-mcp --skill fusion-selector
Clone the repo
git clone --depth 1 https://github.com/tboome33/openrouter-fusion-mcp

Made for: Claude Code.

Or install openrouter-fusion, the plugin that ships this one along with the rest of its 1 skill, 1 command, 1 hook, 1 MCP server.

Wrote 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.

agentmods badge for fusion-selector

README.md
[![agentmods](https://agentmods.dev/badge/skills/tboome33/openrouter-fusion-mcp/fusion-selector/github.svg)](https://agentmods.dev/skills/tboome33/openrouter-fusion-mcp/fusion-selector)
Your own site
<a href="https://agentmods.dev/skills/tboome33/openrouter-fusion-mcp/fusion-selector"><img src="https://agentmods.dev/badge/skills/tboome33/openrouter-fusion-mcp/fusion-selector/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.

agentmods 80×15 button for fusion-selector

Your own site · 80×15
<a href="https://agentmods.dev/skills/tboome33/openrouter-fusion-mcp/fusion-selector"><img src="https://agentmods.dev/badge/skills/tboome33/openrouter-fusion-mcp/fusion-selector.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 123 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 743 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
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 5.1 $0.00123 $0.00743
Opus 5 $0.00062 $0.00371
Sonnet 5 $0.00025 $0.00149
Haiku 4.5 $0.00012 $0.00074

Measured 11d ago against content hash 3e1e7ddf7236, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

fusion-selector 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 11d 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/fusion-selector/SKILL.md · 50 lines

What it actually says

Fusion selector

When the user wants to use OpenRouter Fusion but has not explicitly named a preset, drive this interactive flow. Stop and wait for the user's answer after each question — never chain ahead.

  1. List the configs. Call fusion_list. Present every preset returned as a full TABLE (one row per preset; columns: #, preset, est. cost ~$low–$high (from cost_estimate.low/.high), panel, judge/orchestrator, reasoning_effort). ALWAYS render this full table at the moment you ask the user to choose — even if it was shown earlier in the conversation, and even when you recommend one preset: never reduce it to just the recommendation. The cost is an estimated RANGE per run (floor = little web, ceiling = full max_tool_calls web budget); it also scales with prompt size (cost_estimate.usd_per_prompt_token) and reasoning effort — say it's indicative, not a quote.

  2. Recommend + ask for the preset. Mark (⭐) the preset best suited to the user's task (with its cost_tier), then ask the user to choose (number or name) and WAIT. Never pick for them — skip this only if the user named the preset themselves (you proposing one does not count).

  3. Ask for the reasoning effort — offer xhigh · high · medium · low · minimal · none, recalling the chosen preset's default. Then WAIT.

  4. Ask for the temperature — a number 0–2, or "model default". Then WAIT.

  5. Ask for the question if it hasn't been provided yet.

  6. Run. Call fusion_start with preset, prompt (the full question, not summarized), and reasoning_effort / temperature only if the user chose an explicit value (otherwise omit them so the config/model default applies). Get the job_id.

  7. Poll. Call fusion_result with that job_id; while it returns {status:"running"}, call it again with the same job_id until the final answer (~45 s long-poll per call).

  8. Return the synthesized answer verbatim (including the Fusion usage — cost footer), without reformulating.

If the user already named a preset, skip step 2 but still confirm reasoning effort + temperature unless they specified those too. On error (unknown preset, expired job_id, 401…), explain briefly and don't loop.

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. 11d ago First seen · 50 lines · 123 tokens per session scan A 3e1e7ddf7236

Subscribe to this mod's changes

fusion-selector is a skill published in the GitHub repository tboome33/openrouter-fusion-mcp (0 stars, last pushed 2mo ago), licensed MIT. It adds 123 tokens to every session and 743 once invoked, about $0.0006 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.

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