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 agentmods add skills/profsynapse/synaptic-tuner/embedding-trainingnpx skills add ProfSynapse/Synaptic-Tuner --skill embedding-traininggit clone --depth 1 https://github.com/ProfSynapse/Synaptic-TunerWhat 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 | $0.00143 | $0.02506 |
| Opus 5 | $0.00072 | $0.01253 |
| Sonnet 5 | $0.00029 | $0.00501 |
| Haiku 4.5 | $0.00014 | $0.00251 |
Grade A, and why
embedding-training 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 2d 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.
How it starts
The opening of the file, as written. The whole thing — 182 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Embedding Training Pipeline
Fine-tune SentenceTransformer bi-encoders for retrieval with the embedding
training method. This skill covers the end-to-end loop: obtain triplets → train
(pick a base + adapter mode) → evaluate retrieval against a labeled corpus →
read metrics → refine. Every step is a copy-pasteable CLI command or a YAML
edit; there is no ad hoc Python.
The embedding method is a sibling to sft/kto/grpo/dpo — it is wired
into the same method registry (shared/utilities/paths.py TRAINING_METHODS),
the same recipe/local-run path (tuner.py local-run), and the same output
layout (embedding_output/<timestamp>/...). What differs is the model family
(encoder, not causal-LM), the loss (contrastive, not next-token), and the
evaluation axis (corpus-level retrieval metrics, not per-completion correctness).
Quick Reference
| Task | Command |
|---|---|
| Local Docker embedding smoke | python tuner.py local-run --job-config Trainers/recipes/embedding_bge_base_smoke.yaml --yes |
| List registered models | inspect Trainers/embedding/configs/model_registry.yaml |
| Validate a triplet dataset shape | run the smoke recipe --dry-run (loader validates the JSONL) |
| Retrieval eval (untrained base) | python -m Evaluator.cli --scenario embedding_retrieval_smoke.yaml (see reference/retrieval-eval.md) |
| Retrieval eval (trained adapter) | swap model.registry_name for model.path in the scenario |
The exact
local-run/ eval flags follow the same surface as the SFT path — verify againsttuner/cli/parser.pyor--helpbefore scripting; do not guess flags from memory.
Method at a Glance
| Aspect | Embedding method |
|---|---|
| Model family | encoder bi-encoder (BERT / XLM-RoBERTa / decoder-as-embedder) |
| Loss | multiple_negatives_ranking (MNRL), optionally Matryoshka-wrapped |
| Dataset | triplets {query, positive, negatives} or pairs {query, positive} |
| Adapter modes | full · lora · frozen_head (no qlora in v1) |
| Evaluation | corpus-level retrieval: recall@k / MRR / nDCG@k / MAP against qrels |
| Output | embedding_output/<timestamp>/ (adapter or merged encoder) |
What ships with it
3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 2d ago First seen · 182 lines · 143 tokens per session scan A baa1104ee76d
embedding-training is a skill published in the GitHub repository ProfSynapse/Synaptic-Tuner (27 stars, last pushed 2d ago), licensed MIT. It adds 143 tokens to every session and 2,506 once invoked, about $0.0007 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 2026-08-30.
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