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/refine-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/refine-task)<a href="https://agentmods.dev/skills/modelengine-group/fit-framework/refine-task"><img src="https://agentmods.dev/badge/skills/modelengine-group/fit-framework/refine-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/refine-task"><img src="https://agentmods.dev/badge/skills/modelengine-group/fit-framework/refine-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 | $0.00265 |
| Opus 5.5 | $0.00016 | $0.00106 |
| Sonnet 5.5 | $0.00008 | $0.00053 |
| Haiku 4.5 | $0.00004 | $0.00026 |
Grade A, and why
refine-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
修复审查问题
执行步骤
-
验证前置条件:
- .ai-workspace/active//task.md 存在
- .ai-workspace/active//review.md 存在
-
读取审查报告，整理问题:
- 🔴 Blocker 问题（必须修复）
- 🟡 Major 问题（建议修复）
- 🟢 Minor 问题（可选修复）
-
按优先级逐项修复，修复后运行相关测试验证。
-
运行测试验证:
mvn test -pl :<module-name> -
更新 implementation.md，追加"修复记录"章节。
-
更新任务状态。
-
提示重新审查（review-task）或直接提交（commit）。
注意: 严格按照审查报告修复，不要添加额外变更。不要自动 git commit。
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 · 33 lines · 39 tokens per session scan A 3e553a70892c
refine-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 265 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.
Other skills, from other repositories
pr-review
Address PR review comments systematically. Use when a PR has review feedback that needs to be resolved with code changes.
external-code-review
Run code review via an external LLM API using external-review.sh. Outputs structured JSON with verdict, summary, and per-file concerns.
critic-rules
Use when adversarial code review is needed — actively constructing failure scenarios, edge cases, and security violations to break the implementation.
review-rules
Use when reviewing code for correctness, security, performance, or style — including /review tasks, PR reviews, and post-build quality passes.
learn-from-sage
Detection-gap (miss) analysis for Code Review Sage. Learn from shipped fixes, acted-on human comments, and design outcomes to close reviewer blind spots. Inline during review stages a candidate; a human triggers a one-shot AI consolidation into the live ruleset.
make-pr-easy-to-review
Prepare PRs for review by cleaning noisy history, improving PR descriptions, and adding reviewer guidance without changing code behavior. Use for "make this easy to review", "tidy this PR", "clean up commits", or "annotate the diff".