naskah-id: Skill agen AI untuk menulis dan menyunting karya ilmiah berbahasa Indonesia yang natural, baku, dan jujur

by nothinx · Agent Tool · ★ 78

Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h

How to install naskah-id

npx skills add nothinx/naskah-id        # untuk proyek di folder ini saja
npx skills add nothinx/naskah-id -g     # untuk semua proyek

Taken from the project's README on GitHub. Check the security audit before running it.

Security audit verdict: SAFE · quality 60/100

None of the 42 rules matched — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against 42 published rules built on the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →

The quality score combines nine heuristic checks of the repo and its README: completeness, clarity, specificity, examples, structure, agent readiness, step-by-step content, instruction quality and security notes. It rates how well the project explains itself to an agent, not the code.

academic-writingagent-skillsbahasa-indonesiaclaude-codeeydindonesianjurnalkbbipenulisan-ilmiahproofreading

Quick Facts

Stars78
Forks15
LanguagePython
CategoryAgent Tool
Quality Score60/100
Last Updated2026-10-23
Created2026-10-21
Platformsclaude-code, python
Est. Tokens~14k

About naskah-id

naskah-id Skill agen AI untuk menulis dan menyunting karya ilmiah berbahasa Indonesia: artikel jurnal, skripsi, tesis, proposal hibah, dan laporan penelitian. Ejaan dan kata mengikuti EYD V dan KBBI. Angka, hasil, dan sitasi Anda tidak diubah. README ini juga disunting dengan skill ini. Catatan lengkapnya, termasuk lima kesalahan yang ditemukan di versi sebelumnya, ada di contoh/readme-ini.md.

Compatible Skills

These tools work well together with naskah-id for enhanced workflows:

  • sci-ssci-skills — semantic(0.48)+complementary+same_lang+similar_pop+shared_platform (67%)

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How this security grade is produced

Grades come from a rule-based scan built on the SlowMist agent-security taxonomy: 42 published rules, covering credential harvesting, data exfiltration, curl | sh installers and more. It is a first-layer scan, not a manual audit — we say so rather than overstate it.

The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.

Sources & who's responsible:

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