stop-slop-v3

Produce clean, human-register prose by routing into specific voice basins. Use when drafting, editing, or reviewing text for AI patterns, or when matching a writer's voice from a sample. Replaces restriction-based anti-slop methods with retrieval-shaped positive constraints, with an optional editorial pass against an expanded diagnostic taxonomy.

Quality

76%

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Quality

Content

85%Scale 1-3

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

This is a strong, well-architected skill with excellent actionability and workflow clarity. The six routing constraints are concrete and example-driven, the multi-phase workflow (derive → write → check → audit) is clearly sequenced with appropriate validation, and references are well-organized. The main weakness is moderate verbosity: the architecture principle explanation, source attribution section, and some theoretical framing could be trimmed without losing any operational value.

Suggestions

Trim or remove the 'Architecture Principle' section — Claude understands negative prompting dynamics; a single sentence ('This skill uses positive routing constraints, not prohibition lists') suffices.

Move the 'Source Basis' and 'License' sections to a separate ATTRIBUTION.md or remove them entirely, as they consume tokens without aiding task execution.

Dimension	Reasoning	Score
Conciseness	The skill is well-written but verbose for its purpose. The 'Architecture Principle' section explains negative prompting theory Claude already understands, the 'Source Basis' section is attribution that doesn't help Claude execute the skill, and the 'Voice Within the Register' section could be tightened. However, the core constraints and routing checks are efficiently stated.	2 / 3
Actionability	The six core routing constraints each include concrete ✓/✗ examples showing exactly what passes and fails. The derivation fields are specific and enumerated, the quick routing checks are concrete presence tests, and the Final Audit Pass gives a precise two-step procedure. The scoring rubric provides a clear numeric threshold for re-derivation.	3 / 3
Workflow Clarity	The workflow is clearly sequenced: (1) complete derivation fields before writing, (2) apply six simultaneous constraints during generation, (3) run quick routing checks before delivery, (4) optionally run the Final Audit Pass on finished drafts. The audit pass includes an explicit feedback loop (step 1 diagnoses, step 2 fixes). The separation of generation context from diagnostic context is a critical validation checkpoint that is clearly enforced.	3 / 3
Progressive Disclosure	The skill provides a clear overview with well-signaled one-level-deep references to four separate files (derivation.md, routing-patterns.md, examples.md, diagnostic-taxonomy.md). Each reference includes a brief description of its contents and when to use it. The diagnostic taxonomy is explicitly scoped to editorial review only, showing thoughtful content separation.	3 / 3
	Total	11 / 12 Passed

Description

67%Scale 1-3

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 has good structural completeness with an explicit 'Use when' clause and covers both what and when. However, it relies heavily on insider jargon ('voice basins', 'retrieval-shaped positive constraints', 'anti-slop') that obscures the concrete capabilities and would not match natural user language. The description would benefit from plainer language describing specific actions and more natural trigger terms.

Suggestions

Replace jargon like 'voice basins', 'retrieval-shaped positive constraints', and 'diagnostic taxonomy' with plain descriptions of what the skill actually does (e.g., 'matches a specific writing style', 'removes AI-sounding patterns', 'applies voice guidelines').

Add more natural trigger terms users would actually say, such as 'writing style', 'tone of voice', 'sound more human', 'rewrite', 'too robotic', or 'AI-generated text'.

Dimension	Reasoning	Score
Specificity	Names the domain (prose/writing) and some actions ('drafting, editing, or reviewing text'), but the core capabilities are described in abstract, jargon-heavy language ('voice basins', 'retrieval-shaped positive constraints', 'diagnostic taxonomy') rather than concrete, understandable actions.	2 / 3
Completeness	Explicitly answers both what ('Produce clean, human-register prose by routing into specific voice basins') and when ('Use when drafting, editing, or reviewing text for AI patterns, or when matching a writer's voice from a sample'). The 'Use when' clause is present and provides clear trigger guidance.	3 / 3
Trigger Term Quality	Includes some useful trigger terms like 'drafting', 'editing', 'reviewing text', 'AI patterns', and 'writer's voice', but relies heavily on specialized jargon ('voice basins', 'anti-slop', 'retrieval-shaped positive constraints') that users would rarely use naturally. Missing common terms like 'writing style', 'tone', 'rewrite', 'sound more human'.	2 / 3
Distinctiveness Conflict Risk	The focus on AI-pattern detection and voice matching gives it some distinctiveness, but 'drafting, editing, or reviewing text' is broad enough to overlap with general writing or editing skills. The specialized jargon helps differentiate but may not be recognized by the selection mechanism in practice.	2 / 3
	Total	9 / 12 Passed

Validation

81%

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 — 9 / 11 Passed

Validation for skill structure

Criteria	Description	Result
metadata_version	'metadata.version' is missing	Warning
frontmatter_unknown_keys	Unknown frontmatter key(s) found; consider removing or moving to metadata	Warning

	Total	9 / 11 Passed

Repository: Julz7727/Stop-Slop-v3
Path: SKILL.md
Commit: 27257fd

Reviewed: 2 months ago

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