structured-content-storage

Enforces structured, highly documented storage for code and data projects. Use when working on machine learning scripts, data processing, code creation, or script modification that should preserve clear structure and documentation.

Quality

61%

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tessl review fix ./bundled/skills/structured-content-storage/SKILL.md

Quality

Content

57%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 skill is highly actionable with concrete templates and a complete code example, and it correctly points to real reference files. Its weaknesses are redundancy from repeated directory trees inlined alongside the reference bundle, and missing validation/verification checkpoints in its destructive modification workflow.

Suggestions

Add explicit validation checkpoints to the Modification Protocol and Example 3 (e.g., 'Run existing tests', 'Verify project still executes', 'Confirm no orphaned references before finalizing'), forming a validate->fix->retry loop.

Move the duplicated directory trees and the full code-comment example out of SKILL.md into references/directory-templates.md and references/comment-guidelines.md, keeping only one representative tree inline as an overview.

Consolidate the overlapping core directory layout and Pattern 1/Pattern 2 trees into a single parameterized template or a pointer to the templates reference.

Dimension	Reasoning	Score
Conciseness	The body is mostly efficient and free of basic-concept padding, but it repeats near-identical directory tree diagrams (core layout, Pattern 1 ML, Pattern 2 data cleaning) and a long inline code example that could be consolidated or moved to references.	3 / 5
Actionability	It provides concrete directory templates, a complete executable Python example, and specific file-content specs (README/PROCESS/DATA_DICTIONARY/CHANGELOG bullet lists), but only one full code example and the rest is structural guidance rather than runnable commands.	4 / 5
Workflow Clarity	Multi-step sequences are present (Modification Protocol steps 1-5, numbered Examples), but modification of existing projects is a destructive/batch operation with no validation or verification checkpoints (no 'run tests', 'confirm structure intact', or validate->fix->retry loop), which caps this at 3 per the rubric.	3 / 5
Progressive Disclosure	Structure and a References section with real bundle files exist, but substantial template content (three full directory trees, a full code comment example, a CHANGELOG template) is inlined in SKILL.md despite overlapping with references/directory-templates.md, comment-guidelines.md, and assets/templates/, fitting the 'content that should be separate is inline' anchor.	3 / 5
	Total	13 / 20 Passed

Description

66%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 clearly answers both what the skill does and when to use it, with a good set of natural trigger terms. Its main weaknesses are an abstract 'what' statement and a broad trigger domain that raises overlap risk with general coding skills.

Suggestions

Replace the abstract 'Enforces structured, highly documented storage' with concrete capabilities (e.g., 'Scaffolds project directories, generates README/PROCESS/DATA_DICTIONARY files, and templates code comments').

Narrow the trigger domain or add distinguishing qualifiers so it does not compete with general coding skills (e.g., 'Use when the task benefits from a documented project skeleton rather than a single standalone script').

Add common synonyms/file types users mention (e.g., 'ETL', 'Jupyter notebooks', '.py scripts') to broaden natural trigger coverage.

Dimension	Reasoning	Score
Specificity	The description names the domain ('code and data projects') and one governance action ('Enforces structured, highly documented storage'), but does not enumerate multiple concrete capabilities, fitting the 'names domain and 1-2 concrete actions, but not comprehensive' anchor.	3 / 5
Completeness	Both 'what' ('Enforces structured, highly documented storage') and 'when' ('Use when working on machine learning scripts, data processing, code creation, or script modification...') are explicitly present with concrete trigger scenarios, but the 'what' is abstract governance language rather than concrete capabilities, keeping it just below the anchor 5 ideal.	4 / 5
Trigger Term Quality	It lists natural phrases users would say ('machine learning scripts', 'data processing', 'script modification'), giving good keyword coverage, though 'code creation' is slightly less natural than 'write code' and a few synonyms are missing.	4 / 5
Distinctiveness Conflict Risk	The niche (structured documented storage) is somewhat specific, but the trigger domain ('code creation', 'script modification', 'data processing') is broad and could overlap with general coding skills, matching the 'somewhat specific but could still overlap' anchor.	3 / 5
	Total	14 / 20 Passed

Validation

100%

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 — 16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository: foryourhealth111-pixel/Vibe-Skills
Path: bundled/skills/structured-content-storage/SKILL.md
Commit: ddcaa2a

Reviewed: 4 days ago

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