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/commit/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/commit)<a href="https://agentmods.dev/skills/modelengine-group/fit-framework/commit"><img src="https://agentmods.dev/badge/skills/modelengine-group/fit-framework/commit/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/commit"><img src="https://agentmods.dev/badge/skills/modelengine-group/fit-framework/commit.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 | $0.00034 | $0.00448 |
| Opus 5.5 | $0.00014 | $0.00179 |
| Sonnet 5.5 | $0.00007 | $0.00090 |
| Haiku 4.5 | $0.00003 | $0.00045 |
Grade A, and why
commit scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
What it actually says
提交代码
步骤 0：检查用户本地修改（CRITICAL）
git status --short
git diff
仔细阅读 git diff 输出，在用户修改的基础上进行增量编辑，不要覆盖用户的实现。
步骤 1：版权头年份检查（CRITICAL）
current_year=$(date +%Y)
echo "当前年份: $current_year"
对每个修改的文件检查版权头：
Copyright (C) 2024-2025->Copyright (C) 2024-<current_year>Copyright (C) 2024->Copyright (C) 2024-<current_year>
步骤 2：分析变更并生成提交信息
提交消息格式（Conventional Commits）：
<type>(<scope>): <subject>，subject 使用中文且约 20 字以内- scope 参考：
.codex/->codex，framework/fit/java->fit，framework/waterflow->waterflow，framework/fel->fel
Codex 署名：
🤖 Generated with Codex
Co-Authored-By: Codex <[email protected]>
步骤 3：创建提交
git commit -m "$(cat <<'EOF'
<type>(<scope>): <subject>
<body>
🤖 Generated with Codex
Co-Authored-By: Codex <[email protected]>
EOF
)"
提交后：任务状态更新
根据情况更新任务状态：
- 最终提交 -> 执行 complete-task 归档任务
- 需要继续 -> 更新 task.md 中的 updated_at
- 需要审查 -> 更新 current_step 为 code-review
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 · 62 lines · 34 tokens per session scan A bc4b00b082a1
commit is a skill published in the GitHub repository ModelEngine-Group/fit-framework (2,117 stars, last pushed 7mo ago), licensed MIT. It adds 34 tokens to every session and 448 once invoked, about $0.0001 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.
Other skills, from other repositories
finish
Completing a development branch for merge readiness. Use when implementation and tests pass and the branch needs formal preparation for review and merge.
rebase
Focused rebase against a target branch. Use when you need to update a feature branch with upstream changes. Stops on conflicts — does not auto-resolve. Extended mode adds autonomous conflict resolution and test-fix loop.
checkpoint
Save a checkpoint — bump version, commit, push. Use when work is complete and ready to ship without the full finish protocol.
bump
Bumping project version and updating changelog atomically. Use when releasing a new version — patch, minor, or major.
worktree
Managing git worktrees for isolated parallel development. Use when creating branches, setting up agent worktrees, or cleaning up after merges.
github-pr-workflow
Prepare a GitHub pull request from a feature branch — branch hygiene, commit shape, title/body, verification notes, screenshots for UI work, and replies to review comments.