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/complete-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/complete-task)<a href="https://agentmods.dev/skills/modelengine-group/fit-framework/complete-task"><img src="https://agentmods.dev/badge/skills/modelengine-group/fit-framework/complete-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/complete-task"><img src="https://agentmods.dev/badge/skills/modelengine-group/fit-framework/complete-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 | $0.00039 | $0.00238 |
| Opus 5.5 | $0.00016 | $0.00095 |
| Sonnet 5.5 | $0.00008 | $0.00048 |
| Haiku 4.5 | $0.00004 | $0.00024 |
Grade A, and why
complete-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
完成任务
前置条件
- 所有工作流步骤已完成
- 代码已审查通过
- 代码已提交到 Git
- 所有测试通过
执行步骤
-
验证任务存在:
test -f .ai-workspace/active/<task-id>/task.md -
验证所有步骤标记为完成，文件完整性检查。
-
更新 task.md: status -> completed, completed_at。
-
添加完成总结。
-
归档任务:
mkdir -p .ai-workspace/completed && mv .ai-workspace/active/<task-id> .ai-workspace/completed/ -
验证归档成功。
注意: 只有在真正完成所有工作后才归档，不要过早归档未完成的任务。
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 · 36 lines · 39 tokens per session scan A bc77b74bdd9c
complete-task is a skill published in the GitHub repository ModelEngine-Group/fit-framework (2,117 stars, last pushed 7mo ago), licensed MIT. It adds 39 tokens to every session and 238 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.