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testland/giskard-llm

Authors and runs Giskard LLM scans - adversarial test-case generation for LLM applications via `giskard.scan(model)` covering 7 vulnerability categories (hallucination, harmful_content, prompt_injection, sensitive_information_disclosure, stereotypes, robustness, basic_sycophancy); wraps any callable model behind `giskard.Model(model_predict, model_type="text_generation", ...)`; emits HTML report. Use when the user needs adversarial / red-team coverage on top of functional eval suites.

75

Quality

94%

Does it follow best practices?

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SecuritybySnyk

Low

Low-risk findings worth noting

Overview
Quality
Evals
Security
Files

Quality

Content

85%

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

The body is highly actionable with a clear sequenced workflow and clean single-file organization, but loses conciseness points for an inline dated version note that should live in a deprecation section and for light redundancy with the description's category list.

Suggestions

Move the dated version note ('2026-05-06', v2 maintenance status) into a dedicated 'Deprecated / old patterns' or 'Version status' section so time-sensitive content does not penalize the lean overview.

Trim the 7-category enumeration in the description now that the body's detector table covers them, or cross-reference the table instead of repeating the list in both places.

Consider pulling the CI integration and test-suite steps into a referenced file if the skill grows, keeping SKILL.md as a lean overview pointing one level deep.

DimensionReasoningScore

Conciseness

Mostly efficient with copy-paste code, but the inline dated version note ('Important version note (2026-05-06)') is time-sensitive information placed outside any 'old patterns'/'deprecated' section, and the 7-category detector table partly repeats categories already enumerated in the description.

2 / 3

Actionability

Provides fully executable, copy-paste-ready guidance throughout: the pinned install command, the verbatim model-wrapping snippet, scan()/to_html()/generate_test_suite() calls, and a concrete CI artifact + gating script.

3 / 3

Workflow Clarity

Steps are clearly sequenced Step 1 through Step 6 with a CI gating feedback loop (fail the build on critical findings) and an Anti-patterns table covering error recovery; scanning is non-destructive so no further validation checkpoints are required.

3 / 3

Progressive Disclosure

No bundle files exist, so the single-file structure is appropriate; sections are well-organized with one-level-deep external references ([gk-gh], [gk-docs]) and no nested 'see X which says see Y' chains.

3 / 3

Total

11

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12

Passed

Description

100%

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 third-person, concrete, and explicit about both capability and trigger, hitting the top anchor on every dimension. It names specific actions and a clear use-when clause tied to adversarial/red-team needs.

DimensionReasoningScore

Specificity

Lists multiple concrete actions — 'Authors and runs Giskard LLM scans', 'adversarial test-case generation', 'wraps any callable model behind giskard.Model(...)', 'emits HTML report' — each a distinct executable capability rather than vague language.

3 / 3

Completeness

Explicitly answers what ('Authors and runs Giskard LLM scans ... emits HTML report') and when ('Use when the user needs adversarial / red-team coverage on top of functional eval suites'), satisfying the both-what-and-when anchor.

3 / 3

Trigger Term Quality

Covers natural terms a user would say ('adversarial / red-team coverage', 'Giskard LLM scans', 'vulnerability', 'functional eval suites') alongside the tool name, matching the good-example pattern of including common variations.

3 / 3

Distinctiveness Conflict Risk

Targets a clear niche (adversarial/red-team LLM scanning via giskard.scan) explicitly distinguished from functional eval suites, making conflict with general eval skills unlikely.

3 / 3

Total

12

/

12

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.

Reviewed

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