sg-flw

内容来源：README.md（说明文档） · 原始地址 · 查看安装指南

原始内容

# SG-FLW Web Tool

[![skills.sh](https://img.shields.io/badge/skills.sh-sg--flw--classifier-111827.svg)](https://github.com/ezefranca/sg-flw/tree/main/skills/sg-flw-classifier)

SG-FLW is an academic measurement framework for coding and planning evidence quality in serious games, games, and gamified interventions addressing food loss and waste. The tool profiles five dimensions:

- B: Baseline measurement quality
- K: Knowledge and attitudinal assessment
- D: Direct behavior measurement
- E: Environmental conversion
- L: Persistence / longitudinal measurement

The tool evaluates measurement-chain strength, not intervention quality, game quality, or causal effectiveness. A low SG-FLW profile means that evidence is missing or weakly reported; it does not mean the intervention failed.

## Suggested Citation

Santos, E.; Moreira, M.; Duque, D.; Sevivas, C.; Carvalho, V. (2026). *A Measurement Framework for Serious Games Addressing Food Loss and Waste (SG-FLW)*. Published in the IEEE International Conference on Serious Games and Applications for Health (SeGAH 2026).

Supporting review:

Santos, E.; Sevivas, C.; Carvalho, V. (2025). Managing Food Waste Through Gamification and Serious Games: A Systematic Literature Review. *Information*, 16(3), 246. https://doi.org/10.3390/info16030246

## Machine-Readable Metadata

- `llms.txt`: AI-oriented summary, scope limits, keywords, and citation guidance.
- `llm.txt`: compatibility copy for tools that look for the singular filename.
- `CITATION.cff`: citation metadata for scholarly and software repositories.
- `codemeta.json`: software/research-tool metadata.
- `skills.sh.json`: skills.sh repository-page grouping for the classifier skill.
- `sitemap.xml` and `robots.txt`: crawler discovery and access policy.

## SG-FLW Classifier Skill

The repository hosts a Codex / SKILL.md-compatible classifier skill at `skills/sg-flw-classifier/`. It helps AI agents classify papers, protocols, and boundary cases in serious games and food waste research under SG-FLW without over-crediting awareness outcomes, in-game outputs, or modeled environmental indicators as measured food-waste reduction evidence.

The skill includes separate reference files for SG-FLW foundations, the detailed coding protocol, the published reference corpus, a source-linked corpus knowledge base, and prospective study/telemetry planning. Agents should read those files before assigning B/K/D/E/L scores.

Install with the `skills` CLI:

```bash
npx skills add https://github.com/ezefranca/sg-flw --skill sg-flw-classifier
```

Install globally for Codex:

```bash
npx skills add https://github.com/ezefranca/sg-flw \
  --skill sg-flw-classifier \
  -a codex \
  -g \
  -y
```

Installation instructions are available on `skill.html`.

The skill page documents installation for Codex/OpenAI agent setups, Claude Code, Cursor, GitHub Copilot, upload-based clients, and custom SKILL.md-compatible directories. A ZIP package is available at `downloads/sg-flw-classifier-skill.zip` for clients that support custom skill upload.

SG-FLW Web Tool

SG-FLW is an academic measurement framework for coding and planning evidence quality in serious games, games, and gamified interventions addressing food loss and waste. The tool profiles five dimensions:

B: Baseline measurement quality
K: Knowledge and attitudinal assessment
D: Direct behavior measurement
E: Environmental conversion
L: Persistence / longitudinal measurement

The tool evaluates measurement-chain strength, not intervention quality, game quality, or causal effectiveness. A low SG-FLW profile means that evidence is missing or weakly reported; it does not mean the intervention failed.

Suggested Citation

Santos, E.; Moreira, M.; Duque, D.; Sevivas, C.; Carvalho, V. (2026). A Measurement Framework for Serious Games Addressing Food Loss and Waste (SG-FLW). Published in the IEEE International Conference on Serious Games and Applications for Health (SeGAH 2026).

Supporting review:

Santos, E.; Sevivas, C.; Carvalho, V. (2025). Managing Food Waste Through Gamification and Serious Games: A Systematic Literature Review. Information, 16(3), 246. https://doi.org/10.3390/info16030246

Machine-Readable Metadata

llms.txt: AI-oriented summary, scope limits, keywords, and citation guidance.
llm.txt: compatibility copy for tools that look for the singular filename.
CITATION.cff: citation metadata for scholarly and software repositories.
codemeta.json: software/research-tool metadata.
skills.sh.json: skills.sh repository-page grouping for the classifier skill.
sitemap.xml and robots.txt: crawler discovery and access policy.

SG-FLW Classifier Skill

The repository hosts a Codex / SKILL.md-compatible classifier skill at skills/sg-flw-classifier/. It helps AI agents classify papers, protocols, and boundary cases in serious games and food waste research under SG-FLW without over-crediting awareness outcomes, in-game outputs, or modeled environmental indicators as measured food-waste reduction evidence.

The skill includes separate reference files for SG-FLW foundations, the detailed coding protocol, the published reference corpus, a source-linked corpus knowledge base, and prospective study/telemetry planning. Agents should read those files before assigning B/K/D/E/L scores.

Install with the skills CLI:

npx skills add https://github.com/ezefranca/sg-flw --skill sg-flw-classifier

Install globally for Codex:

npx skills add https://github.com/ezefranca/sg-flw \
  --skill sg-flw-classifier \
  -a codex \
  -g \
  -y

Installation instructions are available on skill.html.

The skill page documents installation for Codex/OpenAI agent setups, Claude Code, Cursor, GitHub Copilot, upload-based clients, and custom SKILL.md-compatible directories. A ZIP package is available at downloads/sg-flw-classifier-skill.zip for clients that support custom skill upload.