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webshop-query-parser

This skill extracts key constraints and product requirements from a user's shopping query. It should be triggered when the agent receives a new shopping instruction, to identify product attributes (e.g., 'gluten free'), price limits (e.g., 'lower than 140.00 dollars'), and other specifications. The skill outputs a structured set of search criteria to guide subsequent product discovery.

57

Quality

65%

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SecuritybySnyk

Low

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tessl review fix ./experiments/src/skills/webshop/webshop-query-parser/SKILL.md
SKILL.md
Quality
Evals
Security

Quality

Content

63%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 a lean, well-sequenced guide with a useful worked example, but it is undermined by bundle integrity problems: the referenced parser script is absent from the bundle and the existing reference file is never linked. Fixing the script reference and wiring up the reference file would lift both actionability and progressive disclosure.

Suggestions

Ship the referenced `parse_query.py` in a scripts/ directory (or replace the script step with inline parsing guidance), and show the exact invocation command.

Link references/query_components.md from the body (e.g., under Core Task or Procedure) so the attribute/price-pattern tables are discoverable, and drop the duplicated search-strategy tips from Notes.

Add an explicit verification step, e.g., "Before clicking, confirm each result's price against price_max/price_min" to sharpen the workflow's checkpoint.

DimensionReasoningScore

Conciseness

The ~30-line body is efficient and assumes Claude's competence, with a concrete worked example. Not 5 because the "When to Use" section restates the frontmatter description and the Notes duplicate search-strategy tips that also live in references/query_components.md; not 3 since padding is minor.

4 / 5

Actionability

The core step says "Execute the bundled script `parse_query.py`", but no scripts/ directory exists in the bundle and no invocation command is given, so the primary instruction cannot be executed as written. The worked example ("{'product': 'popcorn', 'attributes': ['gluten free'], 'price_max': 140.0}" → "search[gluten free popcorn]") is concrete, which keeps it at anchor 3 rather than 2.

3 / 5

Workflow Clarity

A clear 3-step sequence (parse → review & refine → formulate search strategy) with a refine checkpoint and an explicit error-recovery fallback ("If the initial search yields no results, consider broadening the search..."). Not 5 because the checkpoint is vague ("use your judgment") and there is no explicit constraint-verification step.

4 / 5

Progressive Disclosure

Sections are well organized, but scored against the actual bundle: references/query_components.md exists yet is never linked from the body, its search-strategy guidance is partially duplicated inline in Notes, and the referenced path `parse_query.py` does not resolve to any bundled file. Fits anchor 3 (references present but not clearly signaled) better than anchor 2 since the body itself is structured, not inlined bulk content.

3 / 5

Total

14

/

20

Passed

Description

67%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 solid description that explicitly covers both what the skill does and when to trigger it, with concrete examples of attributes and price limits. Its main weakness is trigger-term coverage: it lacks common natural phrasings like "buy", "purchase", or "order" that would broaden recognition.

Suggestions

Add natural trigger synonyms such as "buy", "purchase", "order", or "find me" to the when-clause (e.g., "Use when the user gives a shopping/buying instruction...").

Tighten the when-clause into a leading explicit trigger sentence ("Use when...") rather than embedding it mid-description.

Make "other specifications" concrete (e.g., brand, size, quantity) to close the specificity gap.

DimensionReasoningScore

Specificity

Lists several concrete actions with examples — "extracts key constraints and product requirements", "identify product attributes (e.g., 'gluten free')", "price limits (e.g., 'lower than 140.00 dollars')", "outputs a structured set of search criteria". Not 5 because "other specifications" is generic and the action list is not fully comprehensive; not 3 because multiple specific capabilities are clearly named.

4 / 5

Completeness

Both parts are explicit: 'what' ("extracts key constraints... outputs a structured set of search criteria") and 'when' ("It should be triggered when the agent receives a new shopping instruction"). Not 5 because the when-clause is embedded mid-sentence and could use more concrete trigger phrasing; well above anchor 3's weakly-implied 'when'.

4 / 5

Trigger Term Quality

Relevant keywords like "shopping query", "shopping instruction", "product attributes", and "price limits" are present, but common natural variations a user would say ("buy", "purchase", "order", "find me") are missing. Fits anchor 3 (some relevant keywords, missing variations) better than anchor 4's good coverage.

3 / 5

Distinctiveness Conflict Risk

"shopping query", "product requirements", and "search criteria" carve a distinct query-parsing niche with minimal conflict risk beyond closely related shopping/search skills. Mostly distinct per anchor 4; not 5 because the domain framing is not uniquely unmistakable.

4 / 5

Total

15

/

20

Passed

Validation

100%

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository
zjunlp/SkillNet
Reviewed

Table of Contents

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