A question-answering workflow that sends one difficult question to several independent models, then has another model compare and combine their answers.

Use it for hard research or analysis questions that benefit from parallel answers, web searches, command-line investigation, and a final structured review.

It exposes agreement, contradictions, overlooked details, and gaps that a single answer may miss.

fusion — Skill for Codex by duolahypercho

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 duolahypercho/fusion-fable --skill fusion

Clone the repo

git clone --depth 1 https://github.com/duolahypercho/fusion-fable

Made for: Codex.

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.

README.md

[![agentmods](https://agentmods.dev/badge/skills/duolahypercho/fusion-fable/fusion/github.svg)](https://agentmods.dev/skills/duolahypercho/fusion-fable/fusion)

Your own site

<a href="https://agentmods.dev/skills/duolahypercho/fusion-fable/fusion"><img src="https://agentmods.dev/badge/skills/duolahypercho/fusion-fable/fusion/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.

Your own site · 80×15

<a href="https://agentmods.dev/skills/duolahypercho/fusion-fable/fusion"><img src="https://agentmods.dev/badge/skills/duolahypercho/fusion-fable/fusion.svg" alt="Reviewed on agentmods" width="80" height="20"></a>

Per session 304 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.

When invoked 2,995 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. Third-party audits

NVIDIA SkillSpector warn 7 Sept 2026

SkillSpector: 2 findings, up to medium

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 →

medium Rogue Agent · line 7
Skill establishes unauthorized persistence across sessions via cron jobs, startup scripts, or state files. Session persistence allows an attacker to maintain access beyond the current interaction.

Fix: Remove any persistence mechanisms (cron jobs, startup scripts, state files). Skills should not maintain state across sessions without explicit user consent.
medium Rogue Agent · line 81
Skill establishes unauthorized persistence across sessions via cron jobs, startup scripts, or state files. Session persistence allows an attacker to maintain access beyond the current interaction.

Fix: Remove any persistence mechanisms (cron jobs, startup scripts, state files). Skills should not maintain state across sessions without explicit user consent.

How audits are shown

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.

Model	Per session	Once invoked
Fable 5.1	$0.00304	$0.02995
Opus 5	$0.00152	$0.01497
Sonnet 5	$0.00061	$0.00599
Haiku 4.5	$0.00030	$0.00299

Measured 11d ago against content hash 767da47db57c, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-10, from the pricing page.

Security

Grade A, and why

fusion 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.

The scan reads SKILL.md. This mod also ships 8 executable files (scripts/_fusion_lib.sh, scripts/_pty_run.py, scripts/agy_capture.py, …), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

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/SKILL.md · 181 lines

How it starts

The opening of the file, as written. The whole thing — 181 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Fusion

Fusion turns one prompt into a panel. The question goes to several models at the same time, each answering independently — with web search and bash, and with no knowledge of the others. Then Opus 4.8 (this very session — the orchestrator) reads every answer, extracts the structure of the panel's reasoning (what they agree on, where they conflict, what only one saw, what they all missed), and writes a final answer grounded in that analysis.

The whole mechanism is independence, then synthesis. The diversity that makes a panel beat a single model is harvested, not manufactured: running the same prompt independently yields different reasoning paths, tool calls, and sources. So there are no assigned "lenses" or personas — every panelist gets the user's task verbatim and answers it straight. (See references/panel.md.)

One hard rule: Opus 4.8 always judges and writes the final answer — the pipeline can't be reversed. The panelist models can't call back out to spawn Opus, so Opus is always the driver. The slug reads driver-first for that reason.

Throughout, <skill_dir> is the directory containing this SKILL.md (when installed: ~/.claude/skills/fusion). Write the user's question verbatim to /tmp/fusion_question.txt first — several steps reuse it.

Step 0 — Pick the panel

bash <skill_dir>/scripts/detect_panel.sh

It prints a SLUG= line recommending the richest panel possible on this machine:

Slug	Panel	Requires
`opus4.8-4.8`	the same prompt run twice as 2 independent Opus 4.8 panelists	nothing — always available
`opus4.8-gpt5.5`	Opus 4.8 + GPT-5.5 in parallel	`codex` CLI
`opus4.8-gemini3.1pro`	Opus 4.8 + Gemini 3.1 Pro in parallel	`agy` CLI
`opus4.8-gpt5.5-gemini3.1pro`	Opus 4.8 + GPT-5.5 + Gemini 3.1 Pro in parallel	`codex` + `agy` CLIs

If the user named a slug (or used a pinned /fusion-* command), honor it — but if a required CLI is missing, say so, drop that panelist, and fall back to the next-richest panel rather than failing. Otherwise use the detector's recommendation.

Read the full file on GitHub · 181 lines

Files

What ships with it

10 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

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.

11d ago First seen · 181 lines · 304 tokens per session scan A 767da47db57c

Subscribe to this mod's changes

fusion is a skill published in the GitHub repository duolahypercho/fusion-fable (471 stars, last pushed 1mo ago), licensed MIT. It adds 304 tokens to every session and 2,995 once invoked, about $0.0015 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.

Research

systematic-debugging

Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.

obra/superpowers · 21 tokens

local-ai-agents

Build local-first AI agents that run entirely on a developer workstation with Microsoft Foundry Local and Qwen function-calling models. Covers Small Language Models (SLMs), the OpenAI-compatible local endpoint, sandboxed local tools, local RAG with Chroma, local MCP servers, hybrid cloud/local routing, and the…

microsoft/ai-agents-for-beginners · 200 tokens

next-cache-components-adoption

Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…

vercel/next.js · 95 tokens

insight-error-page

Write or audit an insight-kind error page for the Next.js dev overlay. Use when creating a new errors/ .mdx page, auditing an existing one, or checking that a page matches the framework fix cards. Covers page structure, title alignment, FixCard cards with Copy prompt button, code snippets, terminology verification…

vercel/next.js · 83 tokens

next-cache-components-optimizer

Drive a Next.js route to instant navigation by setting up an agentic loop, under Cache Components / PPR, on initial load (hard navigation) and client-side navigation (soft navigation). Encode the goal as a failing @next/playwright instant() e2e and work it to green, one verified route at a time; the shipped test then…

vercel/next.js · 170 tokens

next-partial-prefetching-adoption

Turn on Partial Prefetching in a Next.js app and work through the insights it surfaces. Use when the user wants to enable or adopt Partial Prefetching, flip the partialPrefetching flag, opt routes in with export const prefetch = 'partial', audit Link prefetch={true} behavior, preserve existing prefetched UI with…

vercel/next.js · 103 tokens