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fal-restore

Restore and fix image quality — deblur, denoise, fix faces, and restore old documents using fal.ai's hosted restoration models.

56

Quality

71%

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SecuritybySnyk

Passed

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tessl review fix ./skills/fal-restore/SKILL.md
SKILL.md
Quality
Evals
Security

Quality

Content

55%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.

A very lean, well-structured catalogue stub that is token-efficient and cleanly organized, but it provides almost no actionable guidance: the executable workflow is entirely delegated to an upstream repository with no concrete commands, model identifiers, or validation steps. As a discoverability advert it is coherent; as a skill body it cannot be executed from what is written.

Suggestions

Replace the `open <URL>` block with a concrete, executable install step (e.g., the actual git clone / copy command and target skills-directory path) so the 'How to use' section can be followed without human interpretation.

Add a minimal quick-start example for the core restoration task (a concrete model endpoint or command for deblur/denoise/face restoration) instead of deferring 100% of the workflow to the external upstream README.

Include at least one verification checkpoint after installation (e.g., how to confirm the skill is registered and invocable) to give the install-then-invoke sequence a validation step.

DimensionReasoningScore

Conciseness

The body is ~30 lean lines with no padding and no explanation of concepts Claude already knows; sections are short and every statement carries information (name, upstream, category, invocation path). Matches the 'lean and efficient; every token earns its place' anchor.

5 / 5

Actionability

The only guidance is 'install the upstream bundle into your active agent's skills directory' with no install command, and the sole code block is a comment plus `open <URL>`, which executes nothing; no model endpoints, parameters, or invocation code appear. This sits at 'minimal concrete guidance; high-level hints but missing the specific steps to execute'.

2 / 5

Workflow Clarity

Only a rough sequence exists (inspect upstream README, install bundle, invoke by name/trigger) with large gaps — the actual restoration workflow is entirely deferred to the external repo — and no validation checkpoints anywhere. Fits 'rough sequence present but many gaps; validation absent'; not 1 because a recognizable sequence and invocation instruction are present.

2 / 5

Progressive Disclosure

Sections (What it does / Source / How to use) are clear and no dangling local references exist (no references/, scripts/, or assets/ files are cited or present). However, all operative content lives behind an external URL rather than well-signaled one-level-deep bundle references, which keeps it at 'good structure; minor organization gaps' rather than a clear in-bundle disclosure hierarchy.

4 / 5

Total

13

/

20

Passed

Description

75%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.

A strong, third-person description that names concrete capabilities and a distinct platform niche, with good natural trigger vocabulary embedded in the action list. Its main weakness is the missing explicit 'when to use' clause, which caps completeness and leaves invocation timing implicit rather than stated.

Suggestions

Append an explicit 'Use when...' clause (e.g., 'Use when a user has a blurry, noisy, or damaged image, an old photo with degraded faces, or an aged document to restore') to raise completeness from 'when missing' to fully answered.

Add common synonym trigger terms such as 'sharpen image', 'clean up photo', or 'enhance image quality' alongside deblur/denoise to broaden natural keyword coverage.

Consider mentioning input formats or extensions users are likely to reference (e.g., photos, scans, .jpg/.png) to sharpen trigger matching.

DimensionReasoningScore

Specificity

The description lists four concrete actions — 'deblur, denoise, fix faces, and restore old documents' — plus the delivery mechanism ('fal.ai's hosted restoration models'), comprehensively covering the restoration domain. Matches the 'multiple specific concrete actions; comprehensive coverage' anchor; the 4 anchor would require minor coverage gaps, which are not present.

5 / 5

Completeness

The 'what' is explicit and concrete, but the description contains no 'Use when...' clause or equivalent trigger guidance — that lives in a separate `triggers` frontmatter field, not in the description text. Per the judging guidelines this caps completeness at 3 ('clear what, when missing or only weakly implied'); it is not 4 because the 'when' is absent, not merely under-specified.

3 / 5

Trigger Term Quality

'deblur', 'denoise', 'fix faces', and 'restore old documents' are natural phrases users would say, giving good keyword coverage. Common synonyms ('sharpen image', 'clean up photo', 'enhance quality') and file extensions are missing, so it fits 'good keyword coverage; a few natural terms missing' rather than the comprehensive anchor 5.

4 / 5

Distinctiveness Conflict Risk

The named platform ('fal.ai's hosted restoration models') and the specific restoration task list give it a clear niche with distinct triggers and minimal conflict risk against generic image or document skills. Matches anchor 5; adjacent anchor 4 would require noticeable overlap with closely related skills.

5 / 5

Total

17

/

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.

Validation — 15 / 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

nexu-io/open-design

Path

skills/fal-restore/SKILL.md

Reviewed

Table of Contents

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