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.xmlandrobots.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.