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/analyze-issue/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/analyze-issue)<a href="https://agentmods.dev/skills/modelengine-group/fit-framework/analyze-issue"><img src="https://agentmods.dev/badge/skills/modelengine-group/fit-framework/analyze-issue/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/analyze-issue"><img src="https://agentmods.dev/badge/skills/modelengine-group/fit-framework/analyze-issue.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 | $0.00042 | $0.00288 |
| Opus 5.5 | $0.00017 | $0.00115 |
| Sonnet 5.5 | $0.00008 | $0.00058 |
| Haiku 4.5 | $0.00004 | $0.00029 |
Grade A, and why
analyze-issue 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
分析 Issue
分析指定的 GitHub Issue 并创建任务文件。
执行步骤
-
获取 Issue 信息:
gh issue view <issue-number> --json number,title,body,labels -
生成任务ID:
date +%Y%m%d-%H%M%S -
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，填写任务元数据。
-
执行需求分析（仅分析，不编写业务代码）:
- 阅读并理解 Issue 描述
- 搜索相关代码文件（只读不改）
- 分析代码结构和影响范围
- 识别技术风险和依赖
-
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。
注意: 禁止编写或修改任何业务代码，只做分析。
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 · 41 lines · 42 tokens per session scan A 0bcff9fda556
analyze-issue is a skill published in the GitHub repository ModelEngine-Group/fit-framework (2,117 stars, last pushed 7mo ago), licensed MIT. It adds 42 tokens to every session and 288 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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reviewer-author-loop
Iterative manuscript improvement workflow where a reviewer agent critiques, an author agent revises, a verifier checks the revision, and the loop repeats until acceptance or a human-pause condition is reached. Use for virtual peer review, reviewer-author revision loops, rebuttal preparation, resubmission polishing…
mechanical-engineering-research
Apply source-aware mechanical-engineering judgment to research, analysis, coding, writing, teaching, research identity, and release work. Use for thermal-fluid systems, heat transfer, fluid mechanics, thermodynamics, HVAC, energy systems, turbomachinery, piping, multiphase flow, experiments, correlations, CFD…
research-schematic-design
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.