Agent Skill Discovery
Agent Skill Discovery
Agent Skill Discovery is the workflow of finding, ranking, reading, and selectively adapting reusable agent skills before installing or copying them. It treats public skill hubs as a research corpus, not as a package manager to trust blindly.
Workflow
- Discover from multiple indexes: Hermes Skills Hub, skills.sh, GitHub repositories, and official platform skill collections.
- Rank by both popularity and trust: install count/star count, official source, repository reputation, recency, and specificity.
- Archive raw content before interpretation so future sessions can re-read the original instructions.
- Read for patterns: trigger design, precondition checks, tool commands, fallback paths, verification gates, and “do not use for” boundaries.
- Synthesize into wiki pages before installation.
- Install or adapt later only when a specific recurring task justifies it.
Quality signals
- Concrete trigger phrases in
description:. - Clear scope boundaries and negative triggers.
- Stepwise workflow with prerequisites before action.
- Verification steps and observable success criteria.
- References/scripts separated via progressive disclosure.
- Security boundaries for credentials, writes, purchases, deployment, or browser automation.
Anti-patterns
- Installing high-install skills without reading them.
- Treating install count as equivalent to quality.
- Copying commands for tools Hermes does not have.
- Letting a script own agent orchestration instead of keeping reasoning in the main agent.
Related
2026-06-30 补充：从技能文件到技能分发基础设施
Agent Skill Ecosystem Snapshot 2026-06-30 显示，外部技能生态正在从零散 SKILL.md 发展为分发基础设施：marketplace、package manager、lockfile、curated directory、cross-agent compatibility。代表项目包括 craftdesk、cc-skills 和 skill-of-skills。
因此 discovery workflow 的重点应继续保持在“先读再装”：星数和 marketplace 只能作为发现信号，不能替代对触发条件、权限边界、验证步骤和副作用的审查。