OpenAgentSkill
GitHub200+

Evaluasi sebelum pemasangan

david-silver Laporan evaluasi.

Keputusan pemasangan yang dapat dibaca mesin untuk Agent: kecocokan tugas, Trust Score, Audit Score, keamanan pemasangan, cakupan izin, dan rencana validasi konkret sebelum skill menyentuh workspace.

Perlu ditinjauRisiko sedangTinjau Kebijakan
70
Laporan evaluasi
70
Kepercayaan
76
Audit
64
Keamanan Agent

Tinjauan manual

Memerlukan persetujuan manusia sebelum memasang ke workspace nyata.

Gerbang wajib

Pemeriksaan yang harus dilalui Agent sebelum memasang

Buka JSON

Kecocokan tugas

84

Lulus

Task wording matches this skill metadata.

  • Evaluate david-silver before installing it in an agent workflow
  • Riset
  • RAG and knowledge workflows; Claude Code teams; builders willing to evaluate younger projects

Jalur pemasangan

92

Lulus

Install handoff is available.

  • npx skills add K-Dense-AI/mimeographs --skill david-silver

Keamanan perintah pemasangan

92

Lulus

Jalur pemasangan paket atau runtime standar

  • npx skills add K-Dense-AI/mimeographs --skill david-silver

Skor kepercayaan

70

Peringatan

Potentially useful, but at least one trust signal needs human inspection.

  • Tinjauan manual
  • 122 star GitHub
  • MIT

Skor audit

76

Peringatan

Perlu ditinjau

  • Core principles contain duplication: the first three principles all express the same 'discard human data to break performance ceiling' idea with cosmetic rewording.

Gerbang keamanan Agent

64

Peringatan

Kandidat yang dapat digunakan, tetapi Agent harus menampilkan catatan izin dan audit sebelum memasang.

  • Memerlukan persetujuan manusia sebelum memasang ke workspace nyata.
  • Core principles contain duplication: the first three principles all express the same 'discard human data to break performance ceiling' idea with cosmetic rewording.

Kejelasan lisensi

86

Lulus

MIT

  • MIT

Cakupan izin

100

Lulus

Tidak ada cakupan izin berisiko tinggi dalam metadata publik

  • Network access: medium

Rencana validasi

Langkah Agent berikutnya

  1. 1Inspect repository, README/SKILL.md, license, and recent commits before production use.
  2. 2Install in an isolated workspace or sandbox with no production secrets available.
  3. 3Run the smallest representative task and record files touched, commands run, network access, and outputs.
  4. 4Compare the selected skill against at least one alternative when the eval status is review or failed.
  5. 5Promote only after the agent reports a successful verification result and unresolved warnings are accepted.

Jangan gunakan ketika

Kondisi yang membutuhkan skill lain

  • Tim yang membutuhkan SLA dengan dukungan vendor
  • production agents without a repository review
  • Core principles contain duplication: the first three principles all express the same 'discard human data to break performance ceiling' idea with cosmetic rewording.
  • The skill lacks concrete RL engineering heuristics (e.g., baseline subtraction, eligibility traces, exploration scaling) that are present in the source corpus, making it less actionable for actual ML engineering.
  • Frameworks and heuristics sections omit source attributions (src_XXX tags) that would improve traceability and credibility.
  • Quality score needs review

Pemeriksaan pendukung

Sinyal kepercayaan di balik keputusan

Kelengkapan README/SKILL.md

Peringatan

76

Metadata publik memerlukan konteks README/SKILL.md yang lebih kuat

Pemeliharaan terbaru

Lulus

88

2 bulan sejak push

Alternatif tersedia

Lulus

82

Alternative skills are available for comparison.