Skill Retriever semantic skill discovery for AI agents via 10K-category taxonomy
Skill Retriever AgentSkillOS-powered semantic skill retrieval for Hermes Agent. Pre-filters 1,200+ skills (998 community corpus + 211 Hermes skills) organized in a 10,000-category capability taxonomy to the top-5 most relevant per query. Runs as a Hermes pre_llm_call plugin — zero core modification, zero additional API cost (borrows your existing Hermes LLMs via borrow-mode).
- ▪Skill Retriever AgentSkillOS-powered semantic skill retrieval for Hermes Agent.
- ▪Pre-filters 1,200+ skills (998 community corpus + 211 Hermes skills) organized in a 10,000-category capability taxonomy to the top-5 most relevant per query.
- ▪Runs as a Hermes pre_llm_call plugin — zero core modification, zero additional API cost (borrows your existing Hermes LLMs via borrow-mode).
Hacker News (AI / LLM) files mainly under ai. We currently carry 3,306 of its stories.
Story provenance
Source · retrieval · rights · ranking — open for full record
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Story provenance
Attribution is not the same as permission. This drawer separates discovery metadata, excerpts, WeSearch-generated summaries, reuse status, and whether the publisher receives the visit. Nothing here claims a legal grant the publisher has not made.
Record
| Original publisher | GitHub |
| Canonical URL | https://github.com/ChonSong/skill-retriever |
| Publication time | Wed, 08 Jul 2026 05:36:19 +0000 |
| Retrieval time | 2026-07-08T06:27:30.161Z |
| Last seen | 2026-07-08T06:27:30.161Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | j4pwYjNpWgE8 |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
Rights status (four layers)
WeSearch handling by dimension
| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
Skill Retriever AgentSkillOS-powered semantic skill retrieval for Hermes Agent. Pre-filters 1,200+ skills (998 community corpus + 211 Hermes skills) organized in a 10,000-category capability taxonomy to the top-5 most relevant per query. Runs as a Hermes pre_llm_call plugin — zero core modification, zero additional API cost (borrows your existing Hermes LLMs via borrow-mode). Why a Skill Tree? Pure semantic retrieval prioritizes textual similarity and misses skills that look unrelated in embedding space but are crucial for solving the task. Our LLM + Skill Tree navigates the capability hierarchy to surface non-obvious but functionally relevant skills. Left: Pure semantic retrieval is narrow and myopic.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at GitHub.