together-ai-inference

Serverless inference, fine-tuning, embeddings, image generation, and batch processing on 200+ open-source models via an OpenAI-compatible API. Use when you need fast, cost-effective access to open-source LLMs without managing infrastructure.

Quality

76%

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Securityby

Passed

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tessl review fix ./backend/cli/skills/cloud-compute/together-ai/SKILL.md

Quality

Content

65%Weight 40%Scale 1-5

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

The body is highly actionable with comprehensive executable examples across every Together AI capability, but it is a large monolithic document that trades conciseness and progressive disclosure for inline completeness. Fine-tuning and batch workflows would benefit from explicit validation checkpoints.

Suggestions

Extract the model-selection tables, CLI reference, and per-feature deep dives into one-level-deep bundle files (e.g. references/models.md, references/cli.md) and leave concise quick-start + signposted links in SKILL.md to improve progressive_disclosure and conciseness.

Add explicit validation checkpoints to the fine-tuning workflow (e.g. verify JSONL format before upload, poll status until 'COMPLETED'/'FAILED', and check the events log on failure) to satisfy the batch/destructive feedback-loop guidance and lift workflow_clarity above 3.

Reduce redundancy by importing and constructing the client once where snippets share context, and consolidate the duplicate OpenAI-SDK chat and image examples into a single OpenAI-compatibility section rather than repeating the SDK pattern.

Dimension	Reasoning	Score
Conciseness	The body is mostly efficient code and tables with little concept padding, but repeats boilerplate ('from together import Together; client = Together()') across ~15 snippets and duplicates SDK examples in the OpenAI-compatibility and image-OpenAI-SDK sections, so it could be tightened; not a 4 because the redundancy is noticeable rather than minor.	3 / 5
Actionability	Provides fully executable, copy-paste-ready code and CLI commands covering chat, streaming, function calling, JSON mode, vision, fine-tuning, embeddings, images, and batch, matching the anchor 5 ideal.	5 / 5
Workflow Clarity	Fine-tuning and batch workflows are sequenced and the batch path checks 'status == COMPLETED' before download, but fine-tuning (a batch operation) lacks explicit validate-before-proceeding checkpoints, so the destructive/batch cap holds it at 3 rather than 4.	3 / 5
Progressive Disclosure	The skill has clear section headers and an external Resources list, but no bundle files exist and large reference material (model catalogs, CLI reference, full per-feature examples) is inlined in a single ~21KB file rather than split into one-level-deep reference files; sits between anchors 2 and 3, with header navigation pulling it to 3.	3 / 5
	Total	14 / 20 Passed

Description

88%Weight 40%Scale 1-5

Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.

The description is strong: it lists concrete capabilities, provides an explicit 'Use when' trigger, and stays in third person without fluff. Its main weakness is trigger-term breadth, missing common synonyms and variants that users might naturally say.

Dimension	Reasoning	Score
Specificity	Enumerates five concrete capabilities ('Serverless inference, fine-tuning, embeddings, image generation, and batch processing') matching the comprehensive-coverage anchor; not a 4 because coverage is broad rather than having minor gaps.	5 / 5
Completeness	Explicitly answers both what (the capability list) and when ('Use when you need fast, cost-effective access to open-source LLMs without managing infrastructure') with a concrete trigger phrase, matching the anchor 5 example.	5 / 5
Trigger Term Quality	Includes natural terms like 'inference', 'fine-tuning', 'embeddings', 'open-source LLMs', and 'OpenAI-compatible API', but lacks common synonyms (e.g. 'model serving', 'LLM hosting') and file extensions, so it sits below the comprehensive anchor 5.	4 / 5
Distinctiveness Conflict Risk	The open-source / OpenAI-compatible framing carves a fairly distinct niche, but the cloud-compute category implies sibling inference-provider skills, creating minor overlap risk rather than minimal conflict.	4 / 5
	Total	18 / 20 Passed

Validation

87%

Warnings & errors only

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation — 14 / 16 Passed

Validation for skill structure

Criteria	Description	Result
skill_md_line_count	SKILL.md is long (736 lines); consider splitting into references/ and linking	Warning
frontmatter_unknown_keys	Unknown frontmatter key(s) found; consider removing or moving to metadata	Warning

	Total	14 / 16 Passed

Repository: synthetic-sciences/openscience
Path: backend/cli/skills/cloud-compute/together-ai/SKILL.md
Commit: 3a6c3a9

Reviewed: 1 day ago

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