Chroma Vector Database
Orchestra-Research/AI-Research-SKILLs
Shows how to store documents and embeddings in Chroma, query them by similarity with metadata filters, and persist them to disk for RAG and semantic search projects.
Train or fine-tune SentenceTransformer, CrossEncoder, and SparseEncoder models for retrieval, similarity, clustering, classification, reranking, and related embedding tasks.
Install Train Sentence Transformers
$ npx skills add sickn33/agentic-awesome-skills --skill train-sentence-transformers -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install sickn33/agentic-awesome-skills train-sentence-transformers --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/sickn33/agentic-awesome-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/train-sentence-transformers .claude/skills/train-sentence-transformers && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "train-sentence-transformers" agent skill from https://github.com/sickn33/agentic-awesome-skills/tree/main/skills/train-sentence-transformers into .claude/skills/train-sentence-transformers/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "train-sentence-transformers", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/sickn33/agentic-awesome-skills/tree/main/skills/train-sentence-transformersType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add sickn33/agentic-awesome-skills --skill train-sentence-transformers -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install sickn33/agentic-awesome-skills train-sentence-transformers --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/sickn33/agentic-awesome-skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/train-sentence-transformers .agents/skills/train-sentence-transformers && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "train-sentence-transformers" agent skill from https://github.com/sickn33/agentic-awesome-skills/tree/main/skills/train-sentence-transformers into .agents/skills/train-sentence-transformers/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "train-sentence-transformers", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add sickn33/agentic-awesome-skills --skill train-sentence-transformers -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install sickn33/agentic-awesome-skills train-sentence-transformers --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/sickn33/agentic-awesome-skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/train-sentence-transformers .cursor/skills/train-sentence-transformers && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "train-sentence-transformers" agent skill from https://github.com/sickn33/agentic-awesome-skills/tree/main/skills/train-sentence-transformers into .cursor/skills/train-sentence-transformers/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "train-sentence-transformers", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/sickn33/agentic-awesome-skills.git --path skills/train-sentence-transformers--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add sickn33/agentic-awesome-skills --skill train-sentence-transformers -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install sickn33/agentic-awesome-skills train-sentence-transformers --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/sickn33/agentic-awesome-skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/train-sentence-transformers .gemini/skills/train-sentence-transformers && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "train-sentence-transformers" agent skill from https://github.com/sickn33/agentic-awesome-skills/tree/main/skills/train-sentence-transformers into .gemini/skills/train-sentence-transformers/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "train-sentence-transformers", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install sickn33/agentic-awesome-skills train-sentence-transformersInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add sickn33/agentic-awesome-skills --skill train-sentence-transformers -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/sickn33/agentic-awesome-skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/train-sentence-transformers .github/skills/train-sentence-transformers && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "train-sentence-transformers" agent skill from https://github.com/sickn33/agentic-awesome-skills/tree/main/skills/train-sentence-transformers into .github/skills/train-sentence-transformers/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "train-sentence-transformers", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add sickn33/agentic-awesome-skills --skill train-sentence-transformers -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install sickn33/agentic-awesome-skills train-sentence-transformers --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/sickn33/agentic-awesome-skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/train-sentence-transformers .opencode/skills/train-sentence-transformers && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "train-sentence-transformers" agent skill from https://github.com/sickn33/agentic-awesome-skills/tree/main/skills/train-sentence-transformers into .opencode/skills/train-sentence-transformers/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "train-sentence-transformers", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
train-sentence-transformers- GitHub stars
- 47k
- Used in
- 1 other repo
- Token cost
- ~2.4k tokens
- SKILL.md length
- 896 words
- Files
- 28 (incl. scripts, references)
- Skills in repo
- 1,493
- Repo updated
- First seen
- Licence
- Apache-2.0
At a glance
Train or fine-tune SentenceTransformer, CrossEncoder, and SparseEncoder models for retrieval, similarity, clustering, classification, reranking, and related embedding tasks.
- Works in 5 steps: Identify the model type → Required reading → Defaults → …
- Tasks that involve Embeddings
- SKILL.md covers When to Use, 1. Identify the model type, 2. Required reading and 3. Defaults, plus 4 more sections
- Runs Python scripts from its folder; calls pip and hf; needs HF_TOKEN
What it does
Train Sentence Transformers is an agent skill from sickn33/agentic-awesome-skills. Train or fine-tune SentenceTransformer, CrossEncoder, and SparseEncoder models for retrieval, similarity, clustering, classification, reranking, and related embedding tasks.
Its SKILL.md is about 2.4k tokens, which your agent loads only when the skill is triggered. The skill folder holds 29 other files, including scripts and reference files (for example `references/base_model_selection.md`, `references/dataset_formats.md` and `references/evaluators_cross_encoder.md`).
It sits in AI & LLM Engineering, covering Embeddings and Retrieval-augmented generation. The repository describes itself as: AAS Core is the local, agent-first control plane for complete catalog discovery, agent-owned selection, stack validation, and planning, backed by 2,400+ agentic skills. Includes… The licence is Apache-2.0.
When your agent uses it
- Tasks that involve Embeddings
- Tasks that involve Retrieval-augmented generation
Example prompts
- “/train-sentence-transformers”
Requirements
- Python 3
Workflow steps
5 steps, taken from the step headings in SKILL.md.
What it can do on your machine
Read from SKILL.md and the folder at commit 680176d. It shows what the files ask for, not the result of running them.
Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
Ships 4 files in scripts/ (Python, from the files we listed), which the agent can run.
Shell commands in SKILL.md call:
piphf
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md. Its commands use pip, which can reach the network depending on how they are called.
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names these keys or tokens, usually read from environment variables:
HF_TOKEN
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Context cost
Train Sentence Transformers loads about 2.4k tokens when it runs, and up to ~31k if it reads all its reference files. Until then it costs about 50 tokens; SKILL.md has 896 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
Safety
Auto-check passed
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.
SKILL.md
The full file from sickn33/agentic-awesome-skills at commit 680176d, republished under its Apache-2.0 licence (© sickn33). 896 words, ~2,387 tokens.
Download SKILL.mdSave it as
.claude/skills/train-sentence-transformers/SKILL.md (or your agent's skills folder). This skill also uses 27 other files; get the full folder from GitHub.- name
- train-sentence-transformers
- description
- Train or fine-tune SentenceTransformer, CrossEncoder, and SparseEncoder models for retrieval, similarity, clustering, classification, reranking, and related embedding tasks.
- risk
- critical
- source
- https://github.com/huggingface/skills/tree/main/skills/train-sentence-transformers
- source_repo
- huggingface/skills
- source_type
- official
- date_added
- 2026-07-01
- license
- Apache-2.0
- license_source
- https://github.com/huggingface/skills/blob/main/LICENSE
Train a sentence-transformers Model
When to Use
Use this skill when you need train or fine-tune sentence-transformers models across SentenceTransformer (bi-encoder; dense or static embedding model; for retrieval, similarity, clustering, classification, paraphrase mining, dedup, multimodal), CrossEncoder (reranker; pair scoring for two-stage retrieval / pair...
This SKILL.md is a router, not a manual. It tells you which references and example scripts to load for your task. The actual content — recommended losses, evaluators, training-script structure, model selection, training-arg knobs, troubleshooting — lives in references/ and scripts/.
Do not synthesize a training script from this file alone. Open the per-type production template (scripts/train_<type>_example.py) and copy it as your starting point. The templates contain load-bearing scaffolding (autocast helper, model-card class, logger silencing list, force=True, seed, TF32, version-compatible imports, named-evaluator metric handling) that prior agent runs have repeatedly missed when rolling their own from a synthesized snippet.
1. Identify the model type
| Tag | Class | What it does | When to pick |
|---|---|---|---|
| [SentenceTransformer] | SentenceTransformer (bi-encoder) | Maps each input to a fixed-dim dense vector | Retrieval, similarity, clustering, classification, paraphrase mining, dedup |
| [CrossEncoder] | CrossEncoder (reranker) | Scores (query, passage) pairs jointly | Two-stage retrieval (rerank top-100 from bi-encoder), pair classification |
| [SparseEncoder] | SparseEncoder (SPLADE) | Sparse vectors over the vocabulary | Learned-sparse retrieval, inverted-index backends (Elasticsearch / OpenSearch / Lucene) |
Tiebreakers when the request is ambiguous: "embedding model" / "vector search" / "similarity" → [SentenceTransformer]. "rerank" / "ranker" / "two-stage" → [CrossEncoder]. "SPLADE" / "sparse" / "inverted index" → [SparseEncoder]. If still unclear, ask.
2. Required reading
Read these in full before writing any code. Do not triage by perceived relevance.
Per-type — always required
[SentenceTransformer]
references/losses_sentence_transformer.md— loss-to-data-shape mapping;BatchSamplers.NO_DUPLICATESrequirement for MNRL-family;Cached*↔gradient_checkpointingincompatibility.references/evaluators_sentence_transformer.md— evaluator-to-task mapping;metric_for_best_modelkey construction (named vs unnamed); per-evaluatorprimary_metricvalues.references/model_architectures.md— encoder vs decoder vs static vs Router pipelines; pooling rules (mean / cls / lasttoken); auto-mean-pooling behavior for fresh-start MLM bases.scripts/train_sentence_transformer_example.py— production template; copy this as your starting point.
[CrossEncoder]
references/losses_cross_encoder.md— pointwise / pairwise / listwise / distillation;pos_weightderivation;activation_fn=Identity()mandatory for non-BCE losses (silent eval-rank collapse otherwise).references/evaluators_cross_encoder.md—CrossEncoderRerankingEvaluatorrecipe; named-evaluator key formateval_{name}_{primary_metric}.scripts/train_cross_encoder_example.py— production template; copy this as your starting point.
[SparseEncoder]
references/losses_sparse_encoder.md—SpladeLosswrapper requirement; FLOPS regularizer weights; smoke-test active-dim ramp behavior.references/evaluators_sparse_encoder.md—SparseNanoBEIREvaluator(English-only) and the in-domain alternative;eval_{name}_{primary_metric}key format.scripts/train_sparse_encoder_example.py— production template; copy this as your starting point.
Cross-cutting — always required (regardless of task)
references/training_args.md—TrainingArgumentsknobs, precision rules (load fp32 + autocast bf16/fp16; nevertorch_dtype=bfloat16),warmup_steps(float) vs deprecatedwarmup_ratio,save_stepsmust be a multiple ofeval_stepsforload_best_model_at_end, schedulers, HPO, tracker, resume, hub-push variants.references/dataset_formats.md— column-matching rules (label name auto-detection; column-order-not-name); reshaping recipes; hard-negative mining options.references/base_model_selection.md— discovery commands; per-type model namespaces; ModernBERT-familymax_seq_length=8192trap;datasets >= 4script-loader rejection; non-English starting-point shortcuts.references/troubleshooting.md— symptom-indexed failure recipes. Skim the section headings on every run, even a healthy one; the "Metrics don't improve" and "Hub push fails" entries cover bugs that bite frequently and are cheaper to recognize before they fire than to debug after.
Cross-cutting — load when applicable
references/hardware_guide.md— VRAM sizing, multi-GPU, FSDP / DeepSpeed, HF Jobs flavors. Required for >24GB models, multi-GPU, or HF Jobs runs.references/hf_jobs_execution.md— required when running on HF Jobs.references/prompts_and_instructions.md— required when using prompt-tuned bases (E5, BGE, GTE, Qwen3-Embedding, Instructor, Nomic, etc.) or addingquery:/passage:style prefixes.
Variant scripts (open when the task matches)
- [SentenceTransformer]
scripts/train_sentence_transformer_<matryoshka|multi_dataset|with_lora|distillation|make_multilingual|static_embedding>_example.py. - [CrossEncoder]
scripts/train_cross_encoder_<distillation|listwise>_example.py. - [SparseEncoder]
scripts/train_sparse_encoder_distillation_example.py. - Hard-negative mining CLI —
scripts/mine_hard_negatives.py.
Show full SKILL.md (362 more words)Show less
3. Defaults
Override only if the user specifies otherwise:
- Local execution. Pitch HF Jobs only if local hardware can't fit the job.
- Single run. After it completes, propose experimentation if the user would benefit (weak/marginal verdict, "see how high you can push it" framing, etc.). Iteration rules in
references/training_args.md(Experimentation section). - Public Hub push at end-of-run, wrapped in try-except. On HF Jobs (ephemeral env) ALSO enable in-trainer push (
push_to_hub=True+hub_strategy="every_save"); details inreferences/hf_jobs_execution.md.
4. Constraints the produced script must satisfy
These are non-negotiable contracts. Implementation lives in the production templates and references — do not reinvent.
- Capture the pre-training evaluator score as
baseline_evalbeforetrainer.train(). - Emit a single end-of-run line:
VERDICT: WIN|MARGINAL|REGRESSION | score=... | baseline=... | delta=.... A monitor scrapes for this. - Silence
httpx,httpcore,huggingface_hub,urllib3,filelock,fsspecto WARNING (otherwise HF download URLs flood the agent's context). - Tee logs to
logs/{RUN_NAME}.log. - End with
model.push_to_hub(...)wrapped intry/except. - Smoke-test before any long run (
max_steps=1+ tiny dataset slice). The production templates show one common pattern (SMOKE_TESTenv var). - [CrossEncoder] Include
EarlyStoppingCallback(patience>=3)— CE rerankers often peak mid-training and regress. - [SparseEncoder] Log
query_active_dims/corpus_active_dimson the verdict line; high nDCG with collapsed sparsity is not a win. The keys come back name-prefixed (e.g...._query_active_dims); use suffix matching to pluck them — see the SPARSE production template for the exact pattern.
5. Workflow
- Identify the model type (§1). Ask if ambiguous.
- Load the §2 required-reading files for that type.
- Open
scripts/train_<type>_example.pyand copy it as your starting point. - Replace
MODEL_NAME,DATASET_NAME,RUN_NAME, the loss, and the evaluator with the user's task. Cross-check loss/data-shape match againstreferences/losses_<type>.md; cross-check themetric_for_best_modelkey againstreferences/evaluators_<type>.md(named evaluators format the key aseval_{name}_{primary_metric}). - Smoke-test (
max_steps=1). - Run.
- After the run, append to
logs/experiments.mdand propose iteration if the verdict is weak/marginal.
Prerequisites
pip install "sentence-transformers[train]>=5.0" # add [train,image] / [audio] / [video] for [SentenceTransformer] multimodal
pip install trackio # optional tracker; or wandb / tensorboard / mlflow
hf auth login # or set HF_TOKEN with write scope (for Hub push)GPU strongly recommended. CPU works only for demos and [SentenceTransformer] StaticEmbedding.
Limitations
- Use this skill only when the task clearly matches its upstream product or API scope.
- Verify commands, API behavior, pricing, quotas, credentials, and deployment effects against current official documentation before making changes.
- Do not treat generated examples as a substitute for environment-specific tests, security review, or user approval for destructive or costly actions.
© sickn33, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
SKILL.md and 27 other files (scripts, references) in skills/train-sentence-transformers of sickn33/agentic-awesome-skills.
- SKILL.md
- references/base_model_selection.md
- references/dataset_formats.md
- references/evaluators_cross_encoder.md
- references/evaluators_sentence_transformer.md
- references/evaluators_sparse_encoder.md
- references/hardware_guide.md
- references/hf_jobs_execution.md
- references/losses_cross_encoder.md
- references/losses_sentence_transformer.md
- references/losses_sparse_encoder.md
- references/model_architectures.md
- references/prompts_and_instructions.md
- references/training_args.md
- references/troubleshooting.md
- scripts/mine_hard_negatives.py
- scripts/train_cross_encoder_distillation_example.py
- scripts/train_cross_encoder_example.py
- scripts/train_cross_encoder_listwise_example.py
- … and 9 more
Open the folder on GitHubat commit 680176d
Used in 1 other repository
We found 5 copies of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in sickn33/agentic-awesome-skills, which our catalogue first saw on October 7, 2026.
Compare with similar skills
Train Sentence Transformers next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Train Sentence Transformers this skillsickn33/agentic-awesome-skills | 47k | 1 repos | ~2.4k | Automated safety check: Pass | Apache-2.0 | |
| Chroma Vector DatabaseOrchestra-Research/AI-Research-SKILLs | 13k | 7 repos | ~2.3k | Automated safety check: Pass | MIT | |
| Ms Agent Framework RAGshuyu-labs/WebCode | 278 | — | ~1.1k | Automated safety check: Pass | Custom licence | |
| Evaluate RAGai-evals-course/evals-skills | 1.5k | — | ~1.9k | Automated safety check: Pass | Apache-2.0 | |
| Pgvector Semantic Searchtimescale/pg-aiguide | 1.9k | — | ~3.8k | Automated safety check: Pass | Apache-2.0 | |
| Memory Upgradeprofbernardoj/everclaw-community-branches | 112 | — | ~574 | Automated safety check: Pass | MIT |
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Questions about Train Sentence Transformers
What does Train Sentence Transformers do?
Train or fine-tune SentenceTransformer, CrossEncoder, and SparseEncoder models for retrieval, similarity, clustering, classification, reranking, and related embedding tasks. Train Sentence Transformers is an agent skill from sickn33/agentic-awesome-skills. Train or fine-tune SentenceTransformer, CrossEncoder, and SparseEncoder models for retrieval, similarity, clustering, classification, reranking, and related embedding tasks.
When should I use Train Sentence Transformers?
Train Sentence Transformers fits situations like: tasks that involve Embeddings; tasks that involve Retrieval-augmented generation.
How do I install Train Sentence Transformers in Claude Code?
Run `npx skills add sickn33/agentic-awesome-skills --skill train-sentence-transformers -a claude-code`. Or copy the skill folder (skills/train-sentence-transformers in sickn33/agentic-awesome-skills) into .claude/skills/train-sentence-transformers in your project. Claude Code loads it when a task matches its description.
How do I install Train Sentence Transformers in Codex?
Run `npx skills add sickn33/agentic-awesome-skills --skill train-sentence-transformers -a codex`. Or copy the skill folder (skills/train-sentence-transformers in sickn33/agentic-awesome-skills) into .agents/skills/train-sentence-transformers in your project. Codex loads it when a task matches its description.
Can I use Train Sentence Transformers in Cursor, Gemini CLI or GitHub Copilot?
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add sickn33/agentic-awesome-skills --skill train-sentence-transformers -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/train-sentence-transformers, .gemini/skills/train-sentence-transformers, .github/skills/train-sentence-transformers and .opencode/skills/train-sentence-transformers in your project.
What does Train Sentence Transformers need to run?
Going by SKILL.md and its folder, Train Sentence Transformers needs Python for the scripts in its folder, the command-line tools its instructions call (pip and hf) and credentials named HF_TOKEN. Our summary lists: Python 3.
Does Train Sentence Transformers access the network?
SKILL.md contains no URLs. Its commands use pip, which can reach the network depending on how they are called. This is read from the text; nothing was executed.
Is Train Sentence Transformers safe to install?
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.
What licence does Train Sentence Transformers use?
Train Sentence Transformers is published under the Apache-2.0 licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Train Sentence Transformers use?
About 2.4k tokens (SKILL.md is roughly 9.5k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 29k tokens, read only when the agent opens those files.
What are the alternatives to Train Sentence Transformers?
Skills that share tags, products or a category with Train Sentence Transformers: Chroma Vector Database (Orchestra-Research/AI-Research-SKILLs, 13k stars), Ms Agent Framework RAG (shuyu-labs/WebCode, 278 stars), Evaluate RAG (ai-evals-course/evals-skills, 1.5k stars) and Pgvector Semantic Search (timescale/pg-aiguide, 1.9k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Train Sentence Transformers?
sickn33 (a GitHub user) maintains it in sickn33/agentic-awesome-skills, which has 47,379 GitHub stars. The repository holds 1,493 skills in this directory. The repository was last updated on October 9, 2026.
Source: sickn33/agentic-awesome-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.