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vector-text-fixer

Fix garbled text in PDF/SVG vector graphics caused by font encoding issues, making files editable in AI tools. Supports batch processing and JSON export for manual correction.

54

Quality

61%

Does it follow best practices?

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SecuritybySnyk

Passed

No findings from the security scan

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tessl review fix ./scientific-skills/Other/vector-text-fixer/SKILL.md
SKILL.md
Quality
Evals
Security

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 body is actionable and well-organized with concrete commands and validation/scope-checking, but it is somewhat redundant and lacks an explicit batch feedback loop and external reference splitting. Scores are solidly mid-range across dimensions.

Suggestions

De-duplicate the CLI examples (Audit-Ready Commands vs CLI Usage) and fold Quick Check into a single verification block to improve conciseness.

Add an explicit validate->fix->retry feedback loop for batch operations, e.g. re-run on failures or re-validate low-confidence blocks before final output.

Move the full JSON output schema, parameter table, and dependency list into a separate reference file referenced one level deep from SKILL.md.

DimensionReasoningScore

Conciseness

Mostly efficient and free of elementary concept explanations, but the same CLI examples appear twice (Audit-Ready Commands and CLI Usage) and Quick Check overlaps Audit-Ready Commands, so it could be tightened.

3 / 5

Actionability

Provides concrete, executable commands, a full parameter table, and a copy-paste JSON output schema; the only minor gap is the documented --encoding flag not matching the actual script's arguments.

4 / 5

Workflow Clarity

The workflow has a clear sequence and scope-validation step, but for a batch-capable skill there is no explicit validate->fix->retry feedback loop, which caps workflow clarity at 3 per the batch-operation guidance.

3 / 5

Progressive Disclosure

Well-sectioned single file with the one bundle (scripts/main.py) correctly referenced, but for a skill this size content that could live in separate reference files (full JSON schema, parameter table, dependencies) is inlined.

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 is specific and distinguishes a clear niche, but it omits explicit 'when to use' trigger guidance, which caps its completeness. Trigger-term coverage is good but lacks synonyms and file extensions.

Suggestions

Add an explicit 'Use when...' clause naming concrete triggers, e.g. 'Use when fixing garbled or box characters in PDF/SVG files caused by font embedding or encoding issues.'

Include natural synonyms and file extensions in the description ('.pdf', '.svg', 'box characters', 'weird characters') to improve trigger-term coverage.

Mention the repair-level options or interactive mode briefly to round out capability coverage.

DimensionReasoningScore

Specificity

Names the domain ('PDF/SVG vector graphics', 'font encoding issues') and several concrete actions ('Fix garbled text', 'batch processing', 'JSON export'), with only minor coverage gaps (no mention of repair levels or interactive mode).

4 / 5

Completeness

Clearly states what the skill does, but lacks any 'Use when...' clause or equivalent explicit trigger guidance, which caps completeness at 3 per the rubric.

3 / 5

Trigger Term Quality

Includes natural terms a user would say ('garbled text', 'PDF/SVG', 'font encoding'), but misses common synonyms and file extensions like '.pdf', 'boxes', or 'weird characters'.

4 / 5

Distinctiveness Conflict Risk

Occupies a clear niche (garbled-text repair in vector graphics from font encoding), mostly distinct with only minor overlap risk against general PDF/text-extraction skills.

4 / 5

Total

15

/

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.

Validation15 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

frontmatter_unknown_keys

Unknown frontmatter key(s) found; consider removing or moving to metadata

Warning

Total

15

/

16

Passed

Repository
aipoch/medical-research-skills
Reviewed

Table of Contents

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