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 agentmods add skills/taekwonv/fuse-skill/fusenpx skills add taekwonv/fuse-skill --skill fusegit clone --depth 1 https://github.com/taekwonv/fuse-skillWhat 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 | $0.00062 | $0.04660 |
| Opus 5 | $0.00031 | $0.02330 |
| Sonnet 5 | $0.00012 | $0.00932 |
| Haiku 4.5 | $0.00006 | $0.00466 |
Grade A, and why
fuse 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 — 695 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Claude Code Skill: Fuse
You are Claude Code running the fuse skill. When the user invokes /fuse, you are the orchestrator by default.
User arguments, if provided by Claude Code, are:
$ARGUMENTS
Parse those arguments as the Fuse request. Then follow the workflow below.
Fuse — Multi-Agent Worktree Fusion
Fuse helps coding agents produce better results by making multiple agents work in parallel, then taking the strongest parts of each result.
Instead of asking one model or agent to solve a task once, Fuse sends the same task to several agents such as Claude Code, Codex, Gemini CLI, OpenCode, Qwen Code, or other local coding agents. Each worker agent solves the task independently in its own isolated git worktree. The orchestrator compares their patches and reports, identifies the best ideas, optionally asks for peer review, and integrates the strongest final result.
User task
|
v
Orchestrator agent
|
+------------+------------+
| | |
v v v
Claude worker Codex worker Gemini worker
private tree private tree private tree
| | |
+------------+------------+
|
v
Judge + final integrator
|
v
Best final result
The non-negotiable rule is:
1 selected worker agent = 1 private git worktree
Claude, Codex, Gemini, Qwen, OpenCode, or any other worker must never share one worktree.
Invocation Syntax
Treat all of the following as Fuse requests:
/fuse agents: claude,codex task: Fix the booking status race condition.
/fuse agents: claude,gemini tests: ./gradlew test task: Refactor notification dispatch.
/fuse agents: claude@sonnet,[email protected],gemini@pro base: main task: Improve auth middleware.
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 First seen · 695 lines · 62 tokens per session scan A d4f30994dfe8
fuse is a skill published in the GitHub repository taekwonv/fuse-skill (2 stars, last pushed 2mo ago), licensed MIT. It adds 62 tokens to every session and 4,660 once invoked, about $0.0003 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.
Other skills, from other repositories
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
brainstorming
You MUST use this before any creative work - creating features, building components, adding functionality, or modifying behavior. Explores user intent, requirements and design before implementation.
auto-perf-optimize
Run agent-driven VS Code performance or memory investigations. Use when asked to launch Code OSS, automate a VS Code scenario, run the Chat memory smoke runner, capture renderer heap snapshots, take workflow screenshots, compare run summaries, or drive a repeatable scenario before heap-snapshot analysis.
chat-perf
Run chat perf benchmarks and memory leak checks against the local dev build or any published VS Code version. Use when investigating chat rendering regressions, validating perf-sensitive changes to chat UI, or checking for memory leaks in the chat response pipeline.
chat-pet-sprite-creation
Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.
cpu-profile-analysis
Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…