asm vs SkillNet
Scriptable skill manager for AI coding agents — install, search, dedupe-audit and security-scan skills across 19 providers, with --json/--yes on every command so agents and CI can drive it. — versus — Open skill infrastructure from ZJU NLP: search a 500K+ indexed skill library, install, generate skills from repos/docs/traces, score their quality, and compose/orchestrate them. SDK + CLI + MCP.
Same job — search and install agent skills from a large catalog via CLI/SDK. asm is the scriptable ops tool (dedupe audits, security scans, --json everywhere for CI); SkillNet adds generation, scoring and graph analysis on top of discovery.
| asm | SkillNet | |
|---|---|---|
| Stars | 730 | 1.1k |
| Forks | 61 | 127 |
| Language | TypeScript | Python |
| License | MIT | MIT |
| Last activity | 5 days ago | 5 days ago |
| Topics | coding, skills | skills |
| Curated connections | 5 | 3 |
asm — the curator's take
Pick it when the CLI consumer is a machine: every command is non-interactive and JSON-emitting, so your agent or CI pipeline can inventory, install, and dedupe skills without a human — that plus `asm audit` (finds duplicate/stale skills scattered across 19 providers' hidden dirs) is the differentiator. NOT the breadth play: ~4.3K curated skills vs skillkit's 400K firehose, and no format translation between agent dialects. Solo-maintainer project — fine for personal/team tooling, but if you need org-level reproducibility and governance, apm's manifest+policy model is the grown-up answer.
SkillNet — the curator's take
Use it when you want skills treated like packages, not pasted folders: semantic search over a 500K+ GitHub-skill index is credential-free, and `evaluate` gives a safety/completeness/executability score before you drop a random skill into an agent. Create/evaluate/analyze need an OpenAI-compatible endpoint; `orchestrate` additionally needs a Claude-Agent-SDK-capable gateway and ships exactly one scene (sciatlas) so far. Prefer skillkit or asm when you just want install plus cross-agent format translation — SkillNet's edge is the research layer: skill creation from execution traces, quality scoring, and skill-relationship graphs, with an arXiv report behind it.