analyze-model

Analyze an external model URL (typically Hugging Face) to determine implementation style and estimate SD.Next porting difficulty using the port-model workflow.

Quality

76%

Does it follow best practices?

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Securityby

High

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tessl review fix ./.github/skills/analyze-model/SKILL.md

Quality

Content

85%

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

The content is well-structured and highly actionable for an instruction-only skill, with a clear ordered procedure and concrete integration touchpoints. The main weakness is mild redundancy between the outputs and reporting-format sections that could be consolidated.

Suggestions

Merge or cross-reference the 'Required Outputs' and 'Reporting Format' sections so the output list is stated once, eliminating the duplicated classification/evidence/risks/next-steps items.

Optionally move the difficulty scale and classification buckets into a short reference table to keep the procedure section leaner.

Clarify whether the referenced 'port-model skill' is a sibling skill to load on demand, so the cross-skill dependency is unambiguous.

Dimension	Reasoning	Score
Conciseness	The body is largely lean and avoids explaining concepts Claude already knows, but the 'Required Outputs' and 'Reporting Format' sections substantially overlap (classification, evidence, porting path, risks, next steps), so it could be tightened — keeping it below the every-token-earns-its-place level.	2 / 3
Actionability	It points to concrete touchpoints (pipelines/model_name.py, modules/sd_detect.py, modules/sd_models.py, modules/modeldata.py), names specific config files (model_index.json, config.json), and gives a concrete difficulty scale, providing actionable instruction-level guidance rather than vague direction.	3 / 3
Workflow Clarity	The numbered Procedure is clearly sequenced and includes an explicit checkpoint in step 1 (retry with auth, then abort and report), plus a reporting-format checklist; this is an analysis task rather than a destructive/batch operation, so the missing feedback-loop cap does not apply.	3 / 3
Progressive Disclosure	It is a single, well-organized SKILL.md with clearly-labeled sections and one clearly signaled one-level-deep reference (Consult .github/instructions/core.instructions.md), with no nested or multi-level references.	3 / 3
	Total	11 / 12 Passed

Description

67%

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 specific and occupies a distinct niche, but it omits an explicit 'when to use' trigger and leans on some technical jargon over natural user phrasings. Adding a 'Use when...' clause and a few plain-language trigger terms would raise it.

Suggestions

Append an explicit trigger clause, e.g. 'Use when the user shares a Hugging Face model URL and asks whether/how to port it to SD.Next or wants an effort estimate.'

Add natural-language keywords users would actually say ('port a model to SD.Next', 'estimate porting effort', 'HuggingFace repo') alongside the technical terms.

Consider mentioning 'effort/difficulty estimate' phrasing explicitly so it surfaces for estimation-only requests.

Dimension	Reasoning	Score
Specificity	The description lists multiple concrete actions — 'Analyze an external model URL', 'determine implementation style', and 'estimate SD.Next porting difficulty' — matching the anchor for naming several specific actions rather than a single vague one.	3 / 3
Completeness	It clearly states what the skill does but lacks any explicit 'Use when...' trigger clause, which per the rubric caps completeness at 2 even though the what is well expressed.	2 / 3
Trigger Term Quality	It includes relevant terms like 'model URL', 'Hugging Face', and 'porting difficulty', but phrases like 'implementation style' and 'port-model workflow' are technical jargon and common user phrasings such as 'port a model' or 'effort estimate' are missing, so it is not the full-coverage level.	2 / 3
Distinctiveness Conflict Risk	The SD.Next model-porting niche is specific and unlikely to trigger for unrelated skills, matching the clear-distinct-niche anchor.	3 / 3
	Total	10 / 12 Passed

Validation

93%

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 — 15 / 16 Passed

Validation for skill structure

Criteria	Description	Result
frontmatter_unknown_keys	Unknown frontmatter key(s) found; consider removing or moving to metadata	Warning

	Total	15 / 16 Passed

Repository: vladmandic/sdnext
Path: .github/skills/analyze-model/SKILL.md
Commit: 058a7f0

Reviewed: about 23 hours ago

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