dmr

Write the best DMR code possible with all the recommended best practices, avoiding common mistakes.

Quality

57%

Does it follow best practices?

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Adds up to 20 points to the overall score

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Securityby

Passed

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Fix and improve this skill with Tessl

tessl review fix ./.agents/skills/dmr/SKILL.md

Quality

Content

65%

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

The content is highly actionable, with consistent wrong/correct code pairs and per-rule limitations, and it does not waste tokens on basics Claude already knows. Its weaknesses are verbosity from repeated imports and a monolithic single-file structure with no progressive disclosure into reference files.

Suggestions

Collapse or abbreviate the repeated import headers across examples (e.g. show full imports once, then elide them) to reduce token cost while keeping snippets executable.

Split large topic groups (Routing, Testing, Validation, Error handling) into separate reference files linked from SKILL.md so the main file is an overview with one-level-deep navigation.

Consider a brief "How to apply these patterns" workflow with a validation checkpoint (e.g. run type checking / schemathesis after applying changes) to give the catalog an overall sequence.

Dimension	Reasoning	Score
Conciseness	The body is dense with concrete code and avoids explaining concepts Claude already knows, but every one of the ~25 examples repeats full import blocks, adding substantial redundancy that could be tightened without losing clarity.	2 / 3
Actionability	Each rule pairs a complete "Wrong" and "Correct" executable Python snippet plus a Limitations note and a Docs link, giving copy-paste-ready, concrete guidance throughout.	3 / 3
Workflow Clarity	Items are individually clear wrong-to-correct patterns, but this is a flat reference catalog with no overall multi-step sequence; it lacks the explicit validation/checkpoint flow the top anchor rewards, and no risky batch operation is given a feedback loop.	2 / 3
Progressive Disclosure	The body is well-organized into themed sections, but it is a single ~1460-line monolithic file with no bundle files; large topic blocks (routing, testing, validation) that would benefit from being split into separate reference files are all inline.	2 / 3
	Total	9 / 12 Passed

Description

50%

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 third-person and names a specific domain, but it is vague and aspirational rather than concrete, and it omits any explicit trigger guidance. It reads more like a slogan than a discoverable capability statement.

Suggestions

Replace abstract phrasing with concrete capabilities, e.g. "Define DMR controllers, configure routing, handle errors, and set up throttling/validation following django-modern-rest best practices."

Add an explicit trigger clause such as "Use when writing or reviewing django-modern-rest (DMR) code, controllers, routing, or API endpoints."

Include natural user-facing variations ("DMR", "django-modern-rest", "DMR controller", "DMR API") to improve trigger discovery.

Dimension	Reasoning	Score
Specificity	The description names the domain ("DMR code") and gestures at actions ("Write the best DMR code", "recommended best practices", "avoiding common mistakes"), but these are aspirational abstractions rather than a list of concrete capabilities like defining controllers or routing.	2 / 3
Completeness	It states what the skill does (write DMR code with best practices), but lacks any explicit "Use when..." trigger clause; per the rubric guidelines a missing explicit trigger caps completeness at 2.	2 / 3
Trigger Term Quality	It surfaces some relevant natural terms ("DMR code", "best practices", "common mistakes"), but misses common variations a user would actually say ("django-modern-rest", "DMR controller", "DMR endpoints", "DMR API").	2 / 3
Distinctiveness Conflict Risk	The "DMR" niche is fairly specific, but the generic "best practices / avoiding common mistakes" framing overlaps with general coding-quality skills and does not clearly bound when it should trigger.	2 / 3
	Total	8 / 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
skill_md_line_count	SKILL.md is long (1462 lines); consider splitting into references/ and linking	Warning

	Total	15 / 16 Passed

Repository: wemake-services/django-modern-rest
Path: .agents/skills/dmr/SKILL.md
Commit: ebee1f2

Reviewed: 3 days ago

Table of Contents

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