Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add Raidriar7170/hermes-skilleval --skill embedding-finetuninggit clone --depth 1 https://github.com/Raidriar7170/hermes-skillevalWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/raidriar7170/hermes-skilleval/embedding-finetuning)<a href="https://agentmods.dev/skills/raidriar7170/hermes-skilleval/embedding-finetuning"><img src="https://agentmods.dev/badge/skills/raidriar7170/hermes-skilleval/embedding-finetuning/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/raidriar7170/hermes-skilleval/embedding-finetuning"><img src="https://agentmods.dev/badge/skills/raidriar7170/hermes-skilleval/embedding-finetuning.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00017 | $0.00068 |
| Opus 5 | $0.00009 | $0.00034 |
| Sonnet 5 | $0.00003 | $0.00014 |
| Haiku 4.5 | $0.00002 | $0.00007 |
Grade A, and why
Embedding Fine-Tuning scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 8d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
What it actually says
Embedding Fine-Tuning
Adapt embedding models using labeled retrieval failures and contrastive pairs.
Use Cases
- Fine-tune on routing misses.
- Measure retrieval gains against off-the-shelf embeddings.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 8d ago First seen · 14 lines · 17 tokens per session scan A 03291477eff7
Embedding Fine-Tuning is a skill published in the GitHub repository Raidriar7170/hermes-skilleval (123 stars, last pushed 1mo ago), licensed MIT. It adds 17 tokens to every session and 68 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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vector-db-launch
Start the Native Python ChromaDB background server. Use when semantic search returns connection refused on port 8110, or when the user wants to enable concurrent agent read/writes.
vector-db-cleanup
Removes stale and orphaned chunks from the ChromaDB vector store for files that have been deleted or renamed. Use after files are removed or moved to keep the vector index in sync with the filesystem. user: "Clean up the vector store after I deleted some files" assistant: "I'll use vector-db-cleanup to remove orphaned…
vector-db-ingest
Ingests repository files into the ChromaDB vector store. Builds or updates the vector index from a manifest or directory scan using ingest.py. Use when new files need to be indexed or the vector store is out of date. user: "Index these new plugin files into the vector database" assistant: "I'll use vector-db-ingest to…
vector-db-search
Semantic search skill for retrieving code and documentation from the ChromaDB vector store. Use when you need concept-based search across the repository (Phase 2 of the 3-phase search protocol). V2 includes L4/L5 retrieval constraints.