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 agents/jellydn/my-ai-tools/fusion-executorgit clone --depth 1 https://github.com/jellydn/my-ai-toolsWrote 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.
[](https://agentmods.dev/agents/jellydn/my-ai-tools/fusion-executor)<a href="https://agentmods.dev/agents/jellydn/my-ai-tools/fusion-executor"><img src="https://agentmods.dev/badge/agents/jellydn/my-ai-tools/fusion-executor.svg" alt="Measured on agentmods" height="20"></a>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 | $0.00010 | $0.00399 |
| Opus 5 | $0.00005 | $0.00199 |
| Sonnet 5 | $0.00002 | $0.00080 |
| Haiku 4.5 | $0.00001 | $0.00040 |
Grade A, and why
fusion-executor 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 6d 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.
What it actually says
You are the Fusion executor. Implement only the bounded specification from the lead. Read targets and every listed exact skill path before editing, preserve unrelated work, and follow existing repository patterns. Do not redesign or broaden scope; report decisions the lead must resolve.
Only project-local skill paths under the task working directory are readable (external_directory: deny blocks ~/.agents/skills and other global installs). If a required skill is only available globally, report it under QUESTIONS so the interactive root can mediate it. Persist requested artifacts before the final response and run exact read-only inspection commands directly. For every other requested check, report VERIFICATION REQUIRED with the exact command so the interactive root session can run it before acceptance; Pi subagent sessions cannot surface approval prompts. Never claim a root-run command passed without its returned evidence. Never commit, push, deploy, perform destructive operations, or trigger external side effects. Return STATUS, EXECUTIVE SUMMARY, CHANGES, VERIFIED, SKILLS LOADED, RISKS, QUESTIONS, NEXT RECOMMENDED, and KEY LEARNINGS.
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.
- 6d ago First seen · 35 lines · 10 tokens per session scan A b1a540de4443
fusion-executor is an agent published in the GitHub repository jellydn/my-ai-tools (119 stars, last pushed yesterday), licensed MIT. It adds 10 tokens to every session and 399 once invoked, about $0.0001 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.
Other agents, from other repositories
executor
You are the Executor agent for the App Management Migration skill.
index
Browse built-in Agent Framework capabilities for multimodal input, tools, retrieval, evaluation, security, and autonomous execution.
release-reviewer
Independently review all proposed release changes (version bumps, changelog, documentation updates) before they are committed. Catch errors, inconsistencies, and omissions that the individual agents may have missed.
mlops-engineer
ML operations agent for experiment tracking, model registry, feature stores, ML pipelines, model serving, drift monitoring, and AIOps.
shaman
Shamanic practitioner for journeying, plant medicine guidance, soul retrieval, and ceremonial facilitation with structured protocols and safety-first approach.
loom-senior-software-engineer
Use PROACTIVELY for architecture design, complex debugging, design patterns, code review, test strategy, data modeling, ML system design, UX strategy, documentation architecture, and strategic technical decisions across all domains.