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tooluniverse-sdk

Build AI scientist systems with the ToolUniverse Python SDK for scientific research. Covers the 3 calling patterns (`tu.run` portable dict API, `tu.tools.X` function API, direct class instantiation), tool loading, batch execution, MCP server integration, and embedding-based tool search. Use for SDK programming, custom tool composition, benchmarking pipelines, and integrating ToolUniverse into research workflows.

Quality

84%

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SKILL.md
Quality
Evals
Security

Quality

Content

71%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.

A lean, highly actionable SDK reference whose value is concentrated in non-obvious gotchas (load_tools requirement, error-returns-not-raises semantics, use_cache placement). Its main weaknesses are the dangling REFERENCE.md link and validation checkpoints that are taught but not wired into the batch/pipeline workflow examples.

Suggestions

Create the referenced REFERENCE.md (or remove the link): 'See [REFERENCE.md](REFERENCE.md) for detailed guides' is the body's only external reference and points to a file absent from the bundle.

Inline a validation step in the batch and pipeline examples, e.g. checking each entry of 'results' from 'tu.run_batch(calls)' for 'status == "error"', so the feedback loop is part of the workflow itself rather than a separate section.

Trim the repeated 'tu = ToolUniverse()' constructor lines in the Configuration section (it appears three times) to recover tokens without losing information.

DimensionReasoningScore

Conciseness

The body is dense with SDK-specific, non-obvious knowledge ('Always call load_tools() before using any tools', 'A failed call returns its error; it does not raise', the use_cache placement caveat), with almost no padding of concepts Claude already knows. Minor trimmable redundancy remains — the Configuration block repeats 'tu = ToolUniverse()' three times — which keeps it just below the 'every token earns its place' anchor.

4 / 5

Actionability

Quick Start, Batch Execution, and Error Handling are concrete and copy-paste ready ('tu.run({"name": "UniProt_get_entry_by_accession", "arguments": {"accession": "P05067"}})'). Minor gaps: 'tu.tools.some_tool(param="value")' placeholders and the Scientific Workflow's hardcoded 'chembl_target_ids = ["CHEMBL203", "CHEMBL230"]' leave the ID-mapping step unimplemented (though the comment flags it).

4 / 5

Workflow Clarity

The load_tools → find → call sequence is explicit and reinforced by Critical Notes, and checkpoints exist across the document (pre-flight 'Check required params', post-flight 'if isinstance(result, dict) and result.get("status") == "error"' with a retriable/next_steps retry loop). The batch and pipeline examples themselves do not inline these checks, so checkpoints must be assembled from separate sections — a minor validation gap rather than a missing one.

4 / 5

Progressive Disclosure

Sections are well organized for an overview of this size, but the body's only external reference — 'See [REFERENCE.md](REFERENCE.md) for detailed guides' — points to a file that does not exist in the bundle (no references/, scripts/, assets/, or REFERENCE.md is present), so the one navigation path it advertises is broken. That is more than the 'minor organization gaps' of the next anchor.

3 / 5

Total

15

/

20

Passed

Description

92%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.

A strong description: third-person voice, concrete multi-capability 'what', explicit 'Use for...' triggers, and a clearly bounded niche around a named SDK. The only soft spot is trigger-term coverage, which is good but not exhaustive of natural phrasings.

DimensionReasoningScore

Specificity

It enumerates multiple concrete capabilities with API-level specifics: 'the 3 calling patterns (tu.run portable dict API, tu.tools.X function API, direct class instantiation), tool loading, batch execution, MCP server integration, and embedding-based tool search'. Coverage spans the SDK's full capability surface, exceeding the 'several specific actions with minor gaps' anchor.

5 / 5

Completeness

Both halves are explicit: the 'what' lists concrete capabilities ('Build AI scientist systems... Covers the 3 calling patterns... batch execution, MCP server integration, and embedding-based tool search') and the 'when' is a concrete trigger clause ('Use for SDK programming, custom tool composition, benchmarking pipelines, and integrating ToolUniverse into research workflows'). Not 4, because the 'when' is specific and trigger-phrase-shaped rather than merely present.

5 / 5

Trigger Term Quality

Good natural keyword coverage: 'SDK programming', 'custom tool composition', 'benchmarking pipelines', 'integrating ToolUniverse into research workflows', 'MCP server integration' are phrases a user with this need would plausibly say. A few natural variations (e.g. 'scientific tool calling', 'AI scientist' appears only in the opening claim) are missing, keeping it below the comprehensive-synonyms anchor.

4 / 5

Distinctiveness Conflict Risk

It names a specific SDK ('the ToolUniverse Python SDK') with distinctive triggers (tu.run, tu.tools.X, MCP server integration), establishing a clear niche with minimal overlap risk against generic research or SDK skills.

5 / 5

Total

19

/

20

Passed

Validation

93%

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

Validation — 15 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

relative_links

Relative link issues: 1 missing

Warning

Total

15

/

16

Passed

Repository

mims-harvard/ToolUniverse

Path

plugin/skills/tooluniverse-sdk/SKILL.md

Reviewed

Table of Contents

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