sg-flw

Name: sg-flw
Author: ezefranca

Verified

0stars

0forks

HTML

Added 5/29/2026

Installation

# Add to your Claude Code skills
git clone https://github.com/ezefranca/sg-flw

Getting Started

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Security ReportVerified

Last scanned: 5/30/2026

{
  "issues": [],
  "status": "PASSED",
  "scannedAt": "2026-05-30T17:06:31.399Z",
  "npmAuditRan": true,
  "pipAuditRan": true
}

README.md

Frequently Asked Questions

What is sg-flw?

sg-flw is an open-source ai agents skill for AI coding assistants such as Claude Code, Codex CLI, and ChatGPT, built by ezefranca. It has 0 GitHub stars.

Is sg-flw safe to use?

Yes. sg-flw passed SkillsLLM's automated security scan — a dependency vulnerability audit plus prompt-injection heuristics — with no high-severity issues. You can read the full report in the Security Report section on this page.

How do I install sg-flw?

Clone the repository with "git clone https://github.com/ezefranca/sg-flw" and add it to your Claude Code skills directory (see the Installation section above).

What programming language is sg-flw written in?

sg-flw is primarily written in HTML. It is open-source under ezefranca on GitHub, so you can review or fork the full source.

Are there alternatives to sg-flw?

Yes. SkillsLLM lists many other AI Agents skills you can browse and compare side by side. Open the AI Agents category from the badge at the top of this page, or use the Related Skills and comparison links further down to weigh sg-flw against similar tools.

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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.