FIT is an enterprise AI development framework with a multilingual function engine, a flow orchestration engine, and Java tools for connecting language models, knowledge bases, and external tools. It is for Java-oriented teams building AI applications that can run locally or in distributed deployments with plugins. The catalogue add-ons provide commands, skills, instructions, and settings for working with FIT.
Borrowing it
Nothing to install: this file belongs to ModelEngine-Group/fit-framework. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/ModelEngine-Group/fit-framework/main/.agents/skills/create-task/SKILL.mdgit clone --depth 1 https://github.com/ModelEngine-Group/fit-frameworkWrote 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/skills/modelengine-group/fit-framework/create-task)<a href="https://agentmods.dev/skills/modelengine-group/fit-framework/create-task"><img src="https://agentmods.dev/badge/skills/modelengine-group/fit-framework/create-task/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.
<a href="https://agentmods.dev/skills/modelengine-group/fit-framework/create-task"><img src="https://agentmods.dev/badge/skills/modelengine-group/fit-framework/create-task.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00043 | $0.00338 |
| Opus 5.5 | $0.00017 | $0.00135 |
| Sonnet 5.5 | $0.00009 | $0.00068 |
| Haiku 4.5 | $0.00004 | $0.00034 |
Grade A, and why
create-task 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 2mo 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
创建任务
根据用户的自然语言描述创建任务并执行需求分析。
行为边界:
- 本命令的唯一产出是 task.md 和 analysis.md 两个文件
- 禁止编写、修改、创建任何业务代码或配置文件
- 禁止直接实现用户描述的功能
执行步骤
-
获取当前时间:
date '+%Y-%m-%d %H:%M:%S' date +%Y%m%d-%H%M%S -
解析用户描述:
- 任务标题: 精简的中文标题（20字以内）
- 任务类型: feature|bugfix|refactor|docs|chore
- 工作流: feature-development/bug-fix/refactoring
-
创建任务目录:
mkdir -p .ai-workspace/active/TASK-<timestamp>/基于 .agents/templates/task.md 模板创建 task.md。
-
执行需求分析（仅分析，不编写业务代码）。
-
输出分析文档到 analysis.md。
-
更新任务状态，标记 requirement-analysis 为完成。
-
提示下一步使用 plan-task skill。
STOP: 完成上述步骤后立即停止。不要继续执行 plan、implement 或任何后续步骤。
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.
- 2mo ago First seen · 43 lines · 43 tokens per session scan A 126b546dd5ed
create-task is a skill published in the GitHub repository ModelEngine-Group/fit-framework (2,117 stars, last pushed 7mo ago), licensed MIT. It adds 43 tokens to every session and 338 once invoked, about $0.0002 per session on Opus 5.5. 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.
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Create publication-quality, editable scientific and engineering schematics with explicit physical meaning, exact labels, visual hierarchy, and provenance. Use for experimental facilities, thermal-fluid mechanisms, workflows, graphical abstracts, system architectures, or research diagrams.
research-writing-literature
Write and revise rigorous research narratives, literature reviews, manuscript sections, citations, and figure discussions. Use for technical introductions, methods, results, discussions, review articles, abstracts, or related-work sections.
research-mentor-review
Provide constructive, evidence-based research mentoring feedback on student manuscripts, proposals, analyses, code, figures, and presentations. Use for advisor-style review that teaches the next improvement without impersonating an advisor or making unsupported scientific judgments.
thermal-fluid-analysis
Analyze thermal-fluid engineering systems, experiments, CFD, and correlations with explicit assumptions, validity limits, uncertainty, and mechanism-based interpretation. Use for heat transfer, fluid mechanics, phase change, HVAC, energy systems, turbomachinery, piping, or thermal management.