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testland/giskard-llm

Use for any question or task about red-teaming or adversarial testing of an LLM app or chatbot, including prompt injection, system-prompt or data leaks, and harmful output before release. Runs giskard.scan on any model wrapped as giskard.Model to probe hallucination, harmful content, prompt injection, sensitive data disclosure, stereotypes, robustness and sycophancy, with an HTML report.

Quality

85%

Does it follow best practices?

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SecuritybySnyk

Passed

No findings from the security scan

Overview
Quality
Evals
Security
Files

Quality

Content

75%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 well-structured, code-first skill body: six clearly sequenced steps with copy-paste-ready examples, a useful detector catalog, anti-patterns table, and honest limitations. Weaknesses are minor — repeated source-attribution asides, a dated version note outside a deprecated section, small inconsistencies in the CI snippet, and no explicit triage step between scanning and test generation.

Suggestions

Trim the repeated "(Per [gk-gh][gk-gh])" attribution asides to a single sourcing note, and move the dated version/maintenance note into a dedicated 'Maintenance status' or 'Deprecated' section so time-sensitive details don't pad the main workflow.

Fix the CI gating snippet: import sys, and align the severity language — the prose says "critical findings" while the code checks `issue.level == "major"` — against the actual scan_results API.

Add an explicit triage step between the scan (Step 3) and test-suite generation (Step 5): how to read the HTML report, categorize findings, and decide which warrant regression tests.

DimensionReasoningScore

Conciseness

Every step is code-first with no explanations of concepts Claude already knows, but efficiency is dented by repeated attribution asides ("(Per [gk-gh][gk-gh].)" appears ~6 times) and a dated version note ("Important version note (2026-05-06)" quoting maintenance status) that is time-sensitive yet not placed in a deprecated/old-patterns section. Fits the 4 anchor's "minor instances that could be trimmed"; not 5 because those tokens don't earn their place, not 3 because there is no real over-explanation.

4 / 5

Actionability

Concrete executable code throughout: `pip install "giskard[llm]>2,<3"`, the full verbatim giskard.Model wrapper, `giskard.scan(giskard_model)` / `to_html(...)`, `generate_test_suite(...)`, and CI YAML. Minor gaps keep it at 4 rather than 5: the CI snippet calls `sys.exit(1)` without importing sys, and the prose says "fail CI if any critical findings" while the code checks `issue.level == "major"`.

4 / 5

Workflow Clarity

Steps 1–6 are clearly sequenced (Install → Wrap → Scan → Test suite → CI) with an explicit gating checkpoint ("fail CI if any critical findings appear") and a regression feedback loop ("re-run on every PR to prevent regression"). Not 5: there is no explicit triage/error-recovery step between scan and test-suite generation and no guidance for judge-model failures; not 3 since checkpoints are present, and no destructive/batch cap applies.

4 / 5

Progressive Disclosure

A self-contained single file (no references/, scripts/, or assets/ bundle exists) with well-organized sections and clearly labeled one-level-deep external links ([gk-gh], [gk-docs]). Fits the 4 anchor's "good structure, minor organization gaps": the ~180-line body exceeds the under-50-line simple-skill exception, and the CI integration details and detector catalog could plausibly live in reference files, but nothing is nested or buried.

4 / 5

Total

16

/

20

Passed

Description

88%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 description: it names the tool, the core API call, seven concrete vulnerability dimensions, and the output artifact, and opens with an explicit use-when clause covering natural trigger phrases. The only weakness is slightly broad trigger scoping and a few missing synonyms (e.g., jailbreaking, LLM security testing).

DimensionReasoningScore

Specificity

Quotes: "Runs giskard.scan on any model wrapped as giskard.Model to probe hallucination, harmful content, prompt injection, sensitive data disclosure, stereotypes, robustness and sycophancy, with an HTML report" — multiple concrete actions (wrap model, run scan, generate report) plus a comprehensive detector list. Matches the 5 anchor's comprehensive coverage; not 4, since no capability area is missing.

5 / 5

Completeness

Explicit "what" ("Runs giskard.scan ... to probe [seven categories] ... with an HTML report") paired with an explicit "Use for any question or task about red-teaming or adversarial testing of an LLM app or chatbot, including prompt injection, system-prompt or data leaks, and harmful output before release" trigger clause. Both what and when are concrete, matching the 5 anchor; the 4 anchor's "when could be more explicit" does not apply.

5 / 5

Trigger Term Quality

Quotes: "red-teaming", "adversarial testing", "LLM app or chatbot", "prompt injection", "system-prompt or data leaks", "harmful output" — good natural-term coverage a user would actually say. Falls short of the 5 anchor because common synonyms like "jailbreaking", "LLM security testing", or "safety scan" are missing; better than 3 since multiple distinct natural phrases are present.

4 / 5

Distinctiveness Conflict Risk

The niche (adversarial red-team scanning via Giskard, distinct from functional eval frameworks) is clear and tool-named, so above 3. Not 5: "any question or task about red-teaming ... of an LLM app" is broad and would overlap with a dedicated prompt-injection-testing or LLM-security skill if one existed alongside it.

4 / 5

Total

18

/

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.

Validation — 16 / 16 Passed

Validation for skill structure

No warnings or errors.

Reviewed

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