{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"giskard","slug":"giskard","name":"Giskard","type":"benchmark","url":"https://github.com/Giskard-AI/giskard","page_url":"https://unfragile.ai/giskard","categories":["testing-quality"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"giskard__cap_0","uri":"capability://safety.moderation.automated.llm.vulnerability.scanning.with.multi.detector.pattern","name":"automated llm vulnerability scanning with multi-detector pattern","description":"Giskard implements a modular detector architecture that automatically scans LLM outputs against 10+ vulnerability classes (hallucination, prompt injection, harmful content, sycophancy, information disclosure, stereotypes, faithfulness violations, implausible outputs, character injection, output formatting). Each detector inherits from a base scanner class and uses LLM-as-judge evaluation to identify issues without manual test case creation. The framework orchestrates detectors through a ScanReport that aggregates findings and generates remediation test suites.","intents":["Automatically identify hallucinations and factual errors in RAG system outputs without writing custom tests","Detect prompt injection vulnerabilities across multiple attack vectors in production LLM applications","Scan for bias, stereotypes, and harmful content generation in model responses at scale","Generate actionable test suites from vulnerability scan results to prevent regression"],"best_for":["Teams deploying RAG systems and LLM agents who need continuous vulnerability monitoring","Compliance-focused organizations requiring automated bias and safety audits","ML engineers building production LLM applications with limited security testing resources"],"limitations":["Detector accuracy depends on the quality of the LLM-as-judge model used for evaluation","Scanning all vulnerability classes requires multiple LLM API calls, increasing latency and cost","Custom vulnerability patterns require extending base detector classes — no low-code pattern definition","No built-in feedback loop to retrain detectors based on false positives in production"],"requires":["Python 3.9+","API credentials for at least one LLM provider (OpenAI, Anthropic, Mistral, AWS Bedrock, Google Gemini)","Model wrapper implementing BaseModel interface with predict() method","Dataset with representative inputs for vulnerability scanning"],"input_types":["LLM model (wrapped via BaseModel abstraction)","Text inputs (prompts, queries, documents)","Structured datasets with slicing/transformation capabilities"],"output_types":["ScanReport object with vulnerability findings","GiskardTest suite auto-generated from scan results","Structured vulnerability metadata (type, severity, affected samples)"],"categories":["safety-moderation","testing-quality"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"giskard__cap_1","uri":"capability://data.processing.analysis.rag.system.component.level.evaluation.with.automated.test.generation","name":"rag system component-level evaluation with automated test generation","description":"The RAG Evaluation Toolkit (RAGET) provides end-to-end evaluation of retrieval-augmented generation systems by decomposing them into evaluable components (Generator, Retriever, Rewriter, Router). It automatically generates diverse question types from a knowledge base (factual, multi-hop, reasoning-based) and measures component performance using metrics like correctness, faithfulness, relevancy, and context precision. The framework uses LLM-as-judge to score outputs against reference answers and generates comprehensive evaluation reports with component-level breakdowns.","intents":["Automatically generate diverse test questions from a knowledge base without manual curation","Evaluate individual RAG components (retriever, generator, rewriter) to identify performance bottlenecks","Measure hallucination rate and faithfulness of generated answers against retrieved context","Generate evaluation reports showing which RAG components are degrading performance"],"best_for":["Teams building RAG applications who need rapid evaluation without manual test set creation","Data scientists debugging RAG performance by isolating component failures","Organizations evaluating multiple RAG architectures or LLM providers for production deployment"],"limitations":["Test generation quality depends on knowledge base structure and LLM capability — sparse or poorly-formatted KBs produce weak test sets","Component isolation requires explicit model wrappers for each RAG stage; end-to-end systems require refactoring","Metrics like 'faithfulness' rely on LLM-as-judge scoring, which can be inconsistent across runs and models","No built-in optimization suggestions — reports identify problems but don't recommend fixes"],"requires":["Python 3.9+","Knowledge base in supported format (documents, structured data, or vector store)","RAG system components wrapped as BaseModel instances with predict() methods","LLM provider credentials for test generation and LLM-as-judge evaluation"],"input_types":["Knowledge base (documents, text chunks, structured data)","RAG component models (retriever, generator, rewriter, router)","Optional: reference answers or ground truth for metric calibration"],"output_types":["Synthetic test dataset with diverse question types","Component-level evaluation metrics (correctness, faithfulness, relevancy, context precision)","Evaluation report with visualizations and component performance breakdowns"],"categories":["data-processing-analysis","testing-quality"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"giskard__cap_10","uri":"capability://data.processing.analysis.stochasticity.and.calibration.analysis.for.model.reliability.assessment","name":"stochasticity and calibration analysis for model reliability assessment","description":"Giskard detects stochasticity (inconsistent outputs for identical inputs) and calibration issues (overconfidence or underconfidence in predictions) by running models multiple times and analyzing output variance and confidence distributions. The framework identifies models that produce different outputs for the same input (indicating non-deterministic behavior) and detects overconfident models (high confidence on incorrect predictions) or underconfident models (low confidence on correct predictions). Results are reported with statistical measures of inconsistency.","intents":["Detect non-deterministic model behavior that could cause reliability issues in production","Identify overconfident models that make incorrect predictions with high confidence","Measure model calibration to ensure confidence scores reflect actual accuracy","Assess model reliability for safety-critical applications"],"best_for":["Teams deploying models in safety-critical applications (healthcare, autonomous systems) requiring reliability assessment","ML engineers debugging model inconsistency issues","Organizations with regulatory requirements for model reliability and confidence calibration"],"limitations":["Stochasticity detection requires multiple model runs, increasing evaluation cost and latency","Calibration analysis assumes confidence scores are available; not applicable to models without confidence outputs","Statistical significance of stochasticity requires sufficient sample size; small sample sizes produce unreliable results","No built-in remediation strategies; framework detects issues but doesn't suggest fixes"],"requires":["Python 3.9+","Model wrapper implementing BaseModel interface","Model that produces confidence scores or probability distributions","Representative test data"],"input_types":["Model (BaseModel subclass)","Test inputs","Optional: confidence scores or probability distributions"],"output_types":["Stochasticity metrics (output variance, inconsistency rate)","Calibration metrics (confidence vs. accuracy)","Overconfidence/underconfidence detection results","Reliability assessment report"],"categories":["data-processing-analysis","testing-quality"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"giskard__cap_11","uri":"capability://safety.moderation.data.leakage.detection.with.feature.correlation.and.information.disclosure.analysis","name":"data leakage detection with feature correlation and information disclosure analysis","description":"Giskard detects data leakage by analyzing feature correlations (identifying spurious correlations between features and targets that indicate data leakage) and information disclosure vulnerabilities (detecting when models reveal sensitive training data or unintended information). The framework uses statistical analysis to identify suspicious correlations and LLM-as-judge to detect information disclosure in model outputs. Results identify potentially leaked features and suggest remediation.","intents":["Detect data leakage in model training that could inflate performance metrics","Identify spurious correlations that indicate information leakage","Detect when models reveal sensitive training data or unintended information","Validate data pipeline integrity before model deployment"],"best_for":["ML teams validating data pipelines before model deployment","Data scientists debugging unexpectedly high model performance that may indicate leakage","Organizations with privacy requirements needing to verify models don't leak sensitive data"],"limitations":["Correlation-based detection assumes leakage manifests as statistical correlations; subtle leakage may be missed","Information disclosure detection via LLM-as-judge is subjective and may produce false positives","No automated remediation; framework identifies leakage but requires manual investigation and fixing","Requires domain knowledge to distinguish legitimate correlations from suspicious ones"],"requires":["Python 3.9+","Model wrapper implementing BaseModel interface","Dataset with features and targets","For information disclosure: LLM provider credentials"],"input_types":["Model (BaseModel subclass)","Dataset with features and targets","Model outputs (for information disclosure detection)"],"output_types":["Suspicious feature correlations","Information disclosure detection results","Data leakage risk assessment","Remediation suggestions"],"categories":["safety-moderation","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"giskard__cap_12","uri":"capability://safety.moderation.harmful.content.and.toxicity.detection.with.semantic.classification","name":"harmful content and toxicity detection with semantic classification","description":"Giskard detects harmful content (hate speech, violence, illegal activity, sexual content) and toxicity in model outputs using LLM-as-judge evaluation with configurable harm categories. The framework classifies detected harmful content by type and severity, enabling risk-based filtering. Detection results identify problematic outputs and can trigger automated remediation (output filtering, model retraining).","intents":["Detect harmful content in LLM outputs to prevent policy violations and reputational damage","Classify harmful content by type (hate speech, violence, sexual, illegal) for risk-based filtering","Monitor production LLM systems for harmful content generation","Generate test cases for harmful content vulnerabilities to prevent regression"],"best_for":["Teams deploying LLMs in consumer-facing applications (chatbots, content generation) with content moderation requirements","Compliance teams needing to document harmful content detection for regulatory audits","Organizations with brand protection requirements"],"limitations":["Detection accuracy depends on LLM-as-judge quality; biased judges produce biased detection","Harm categories are culturally and contextually dependent; framework requires custom configuration per use case","False positive rate can be high for edge cases (satire, educational content, technical documentation)","No built-in remediation; framework detects harmful content but doesn't automatically filter or prevent it"],"requires":["Python 3.9+","LLM provider credentials for semantic harm detection","Model wrapper implementing BaseModel interface","Configurable harm categories and severity thresholds"],"input_types":["Model outputs (text)","Model (BaseModel subclass)","Harm category definitions"],"output_types":["Harmful content detection results (detected/not detected)","Harm type classification (hate speech, violence, sexual, illegal, etc.)","Severity scores","Harmful content samples"],"categories":["safety-moderation","testing-quality"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"giskard__cap_13","uri":"capability://safety.moderation.stereotype.and.bias.detection.in.llm.outputs","name":"stereotype and bias detection in llm outputs","description":"Giskard's stereotype detector identifies when LLM outputs contain stereotypical or biased representations of groups (demographic, occupational, etc.). The detector uses LLM-as-judge evaluation with bias-specific prompts to assess whether outputs reinforce stereotypes or exhibit discriminatory language. This enables detection of subtle biases that are difficult to capture with keyword matching.","intents":["Detect stereotypical or biased language in LLM outputs","Validate that LLM applications do not reinforce harmful stereotypes","Generate test cases for bias robustness testing","Document bias vulnerabilities for fairness compliance"],"best_for":["Teams building LLM applications for diverse audiences requiring fairness","Fairness researchers studying LLM bias and stereotypes","Organizations implementing fairness policies and compliance"],"limitations":["LLM-as-judge evaluation is slow (2-5 seconds per output) and may exhibit judge bias","Judge may not recognize all forms of stereotyping or may be culturally biased","Stereotype definitions are subjective and context-dependent","False positives possible; legitimate discussion of groups may be flagged as stereotyping"],"requires":["Python 3.9+","LLM model wrapper","Test outputs (LLM responses)","API key for LLM provider (for judge evaluation)","Giskard library"],"input_types":["LLM outputs (text)","optional: demographic groups or protected attributes to evaluate"],"output_types":["stereotype detection report (pass/fail per output)","stereotype category identified (if detected)","confidence score","test cases for bias validation"],"categories":["safety-moderation","testing-quality"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"giskard__cap_14","uri":"capability://safety.moderation.information.disclosure.and.privacy.leak.detection","name":"information disclosure and privacy leak detection","description":"Giskard's information disclosure detector identifies when LLM outputs inadvertently reveal sensitive information (personal data, credentials, proprietary information). The detector uses LLM-as-judge evaluation to assess whether outputs contain information that should not be disclosed, enabling detection of privacy leaks that are difficult to capture with pattern matching. This is critical for applications handling sensitive data.","intents":["Detect accidental disclosure of personal data or credentials in LLM outputs","Validate that LLM applications do not leak proprietary or confidential information","Generate test cases for privacy robustness testing","Document information disclosure vulnerabilities for privacy compliance"],"best_for":["Teams building LLM applications handling sensitive data (healthcare, finance, legal)","Privacy teams implementing data protection policies","Organizations subject to privacy regulations (GDPR, CCPA, etc.)"],"limitations":["LLM-as-judge evaluation is slow (2-5 seconds per output) and may exhibit judge bias","Judge may not recognize all forms of sensitive information (e.g., obfuscated credentials)","Requires definition of what constitutes 'sensitive information' for the domain","Does not detect information leakage through side channels or metadata"],"requires":["Python 3.9+","LLM model wrapper","Test outputs (LLM responses)","API key for LLM provider (for judge evaluation)","optional: list of sensitive information patterns or PII definitions","Giskard library"],"input_types":["LLM outputs (text)","optional: sensitive information definitions or PII patterns"],"output_types":["information disclosure detection report (pass/fail per output)","sensitive information type identified (if detected)","confidence score","test cases for privacy validation"],"categories":["safety-moderation","testing-quality"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"giskard__cap_15","uri":"capability://data.processing.analysis.output.format.validation.and.parsing","name":"output format validation and parsing","description":"Giskard's output formatting detector validates that LLM outputs conform to expected formats (JSON, XML, structured text, etc.). The detector uses LLM-as-judge or parsing-based validation to assess whether outputs are parseable and match specified schemas. This is critical for applications that depend on structured outputs for downstream processing.","intents":["Validate that LLM outputs are parseable JSON/XML/structured text","Detect when LLM outputs deviate from expected schema or format","Generate test cases for format robustness testing","Ensure downstream systems can process LLM outputs without errors"],"best_for":["Teams building LLM applications with structured output requirements (APIs, data extraction)","Data engineers integrating LLM outputs into data pipelines","Organizations implementing output validation and error handling"],"limitations":["Format validation is strict; minor deviations (extra whitespace, field order) may cause failures","Schema validation requires explicit schema definition; implicit formats are difficult to validate","LLM-as-judge validation is slow; parsing-based validation is faster but less flexible","Does not validate semantic correctness of structured outputs, only format compliance"],"requires":["Python 3.9+","LLM model wrapper","Test outputs (LLM responses)","optional: schema definition (JSON Schema, etc.)","Giskard library"],"input_types":["LLM outputs (text)","optional: expected format or schema definition"],"output_types":["format validation report (pass/fail per output)","parsing errors or schema violations identified","test cases for format robustness"],"categories":["data-processing-analysis","testing-quality"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"giskard__cap_16","uri":"capability://safety.moderation.sycophancy.and.agreement.bias.detection","name":"sycophancy and agreement bias detection","description":"Giskard's sycophancy detector identifies when LLM outputs exhibit agreement bias, where the model agrees with user statements or premises even when they are incorrect or harmful. The detector uses LLM-as-judge evaluation to assess whether outputs appropriately disagree with false or problematic premises, enabling detection of models that are overly agreeable. This is important for applications requiring critical thinking and honest feedback.","intents":["Detect when LLM models agree with false or problematic user statements","Validate that LLM applications provide honest feedback rather than just agreeing","Generate test cases for sycophancy robustness testing","Improve model reliability by identifying and mitigating agreement bias"],"best_for":["Teams building LLM applications requiring critical thinking (tutoring, analysis, feedback)","Researchers studying LLM alignment and truthfulness","Organizations implementing quality assurance for LLM outputs"],"limitations":["LLM-as-judge evaluation is slow (2-5 seconds per output) and may exhibit judge bias","Judge may not recognize all forms of sycophancy or may be overly critical","Requires explicit false or problematic premises to test against","Does not detect subtle forms of agreement bias (e.g., hedging language that implies agreement)"],"requires":["Python 3.9+","LLM model wrapper","Test prompts with false or problematic premises","API key for LLM provider (for judge evaluation)","Giskard library"],"input_types":["test prompts with false or problematic premises","LLM outputs (responses to test prompts)"],"output_types":["sycophancy detection report (pass/fail per output)","agreement bias identified (if detected)","confidence score","test cases for sycophancy validation"],"categories":["safety-moderation","testing-quality"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"giskard__cap_17","uri":"capability://safety.moderation.implausible.output.detection.for.semantic.anomalies","name":"implausible output detection for semantic anomalies","description":"Giskard's implausible output detector identifies LLM outputs that are semantically anomalous or implausible given the input context. The detector uses LLM-as-judge evaluation to assess whether outputs make sense in context, enabling detection of outputs that are grammatically correct but semantically nonsensical or contradictory. This helps catch models that generate plausible-sounding but meaningless text.","intents":["Detect semantically anomalous or nonsensical LLM outputs","Validate that LLM outputs are coherent and contextually appropriate","Generate test cases for semantic robustness testing","Improve model reliability by identifying outputs that don't make sense"],"best_for":["Teams building LLM applications requiring semantic coherence (chatbots, content generation)","Researchers studying LLM semantic understanding and reasoning","Organizations implementing quality assurance for LLM outputs"],"limitations":["LLM-as-judge evaluation is slow (2-5 seconds per output) and may exhibit judge bias","Judge may not recognize all forms of semantic anomalies or may be overly lenient","Implausibility is subjective and context-dependent","Does not detect subtle semantic errors (e.g., logical inconsistencies)"],"requires":["Python 3.9+","LLM model wrapper","Test inputs and outputs","API key for LLM provider (for judge evaluation)","Giskard library"],"input_types":["test inputs (prompts or context)","LLM outputs (responses)"],"output_types":["implausibility detection report (pass/fail per output)","semantic anomalies identified (if detected)","confidence score","test cases for semantic robustness"],"categories":["safety-moderation","testing-quality"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"giskard__cap_2","uri":"capability://tool.use.integration.unified.llm.provider.abstraction.with.multi.provider.client.routing","name":"unified llm provider abstraction with multi-provider client routing","description":"Giskard implements a unified client interface that abstracts away provider-specific APIs for OpenAI, Azure OpenAI, Mistral, AWS Bedrock, and Google Gemini. The LLM integration layer handles authentication, request formatting, and response parsing for each provider through a common interface, enabling users to swap providers without code changes. The framework routes scanning and evaluation requests through the appropriate provider client based on configuration.","intents":["Switch between LLM providers (OpenAI, Mistral, Bedrock, Gemini) without rewriting evaluation code","Use different providers for different scanning tasks (e.g., cheaper model for initial scan, stronger model for validation)","Evaluate model behavior across multiple providers to identify provider-specific vulnerabilities","Reduce vendor lock-in by abstracting provider-specific APIs"],"best_for":["Teams evaluating multiple LLM providers for production deployment","Cost-conscious organizations wanting to route expensive operations to cheaper models","Enterprises with multi-cloud or multi-vendor requirements"],"limitations":["Provider-specific features (vision, function calling, streaming) require custom wrapper code — abstraction doesn't fully hide provider differences","Response latency varies significantly across providers; no built-in optimization or caching","Authentication credentials must be managed externally; framework doesn't provide secrets management","Rate limiting and quota management are provider-specific and not abstracted"],"requires":["Python 3.9+","API credentials for at least one supported provider (OpenAI, Anthropic, Mistral, AWS Bedrock, Google Gemini)","Network access to provider endpoints"],"input_types":["Provider configuration (API key, model name, endpoint)","Text prompts and requests"],"output_types":["Structured LLM responses (text, structured data)","Provider-agnostic response objects"],"categories":["tool-use-integration","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"giskard__cap_3","uri":"capability://automation.workflow.test.suite.generation.and.execution.framework.with.declarative.test.definitions","name":"test suite generation and execution framework with declarative test definitions","description":"Giskard provides a GiskardTest base class for defining reusable, declarative tests that can be executed against any model and dataset. Tests are organized into Suite containers that manage execution, result aggregation, and reporting. The framework supports both built-in tests (hallucination, bias, prompt injection) and custom tests via inheritance. ScanReport objects can automatically generate test suites from vulnerability scan results, creating a feedback loop from detection to testing.","intents":["Define reusable tests that can be executed against multiple models and datasets without code duplication","Organize tests into logical suites for different evaluation scenarios (safety, performance, bias)","Automatically generate test suites from vulnerability scan results to prevent regression","Execute tests in batch and aggregate results for reporting and compliance documentation"],"best_for":["ML teams building test-driven development practices for AI models","Compliance-focused organizations needing documented, repeatable test suites","Teams managing multiple models and wanting to enforce consistent evaluation standards"],"limitations":["Test execution is synchronous by default; no built-in parallelization for large test suites","Custom tests require Python coding; no low-code test definition interface","Test result aggregation is basic (pass/fail counts); no advanced statistical analysis or trend tracking","No built-in test scheduling or CI/CD integration — requires external orchestration"],"requires":["Python 3.9+","Model wrapper implementing BaseModel interface","Dataset with test data","For LLM tests: LLM provider credentials"],"input_types":["Model (BaseModel subclass)","Dataset with slicing and transformation capabilities","Test parameters (thresholds, sample sizes, etc.)"],"output_types":["Test execution results (pass/fail, metrics)","Suite reports with aggregated results","Generated test suites from scan reports"],"categories":["automation-workflow","testing-quality"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"giskard__cap_4","uri":"capability://data.processing.analysis.dataset.abstraction.with.slicing.and.transformation.for.stratified.testing","name":"dataset abstraction with slicing and transformation for stratified testing","description":"Giskard's Dataset abstraction provides a unified interface for test data with built-in support for slicing (filtering subsets by conditions), transformations (applying perturbations or modifications), and metadata tracking. The framework enables stratified testing by allowing tests to be executed on specific dataset slices (e.g., 'test only on low-income samples' or 'test only on non-English inputs'). Transformations enable adversarial testing by systematically modifying inputs (typos, paraphrasing, language changes) to test robustness.","intents":["Test model performance on specific subgroups (demographic slices) to detect bias","Apply adversarial transformations (typos, paraphrasing, language changes) to test robustness","Execute the same test suite across multiple dataset slices to identify performance disparities","Create synthetic test data by transforming existing datasets without manual curation"],"best_for":["Teams conducting fairness audits and needing to test performance across demographic slices","Robustness testing teams wanting to systematically apply adversarial perturbations","Organizations with limited labeled data wanting to generate synthetic test sets via transformations"],"limitations":["Slicing requires pre-computed metadata or custom slice definition logic; no automatic demographic inference","Transformations are deterministic; no probabilistic perturbation strategies for stochastic robustness testing","Large datasets may exceed memory when materializing slices; no lazy evaluation or streaming","Transformation quality depends on implementation; no built-in validation that transformations preserve semantic meaning"],"requires":["Python 3.9+","Dataset in supported format (CSV, Pandas DataFrame, or custom loader)","For slicing: metadata columns or custom slice definition functions","For transformations: transformation functions or built-in transformation library"],"input_types":["Tabular data (CSV, Pandas DataFrame)","Text data (documents, prompts)","Metadata for slicing (demographic attributes, categorical features)"],"output_types":["Dataset slices (filtered subsets)","Transformed datasets (perturbed inputs)","Slice-level evaluation metrics"],"categories":["data-processing-analysis","testing-quality"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"giskard__cap_5","uri":"capability://safety.moderation.llm.as.judge.evaluation.with.configurable.scoring.rubrics","name":"llm-as-judge evaluation with configurable scoring rubrics","description":"Giskard implements LLM-as-judge evaluation by using a separate LLM to score model outputs against criteria (correctness, faithfulness, relevancy, harmfulness, etc.). The framework provides configurable scoring rubrics that define evaluation criteria, scale (e.g., 1-5), and examples. The judge LLM processes outputs and returns structured scores that are aggregated into metrics. This approach enables flexible, semantic evaluation without manual annotation.","intents":["Score LLM outputs for subjective qualities (faithfulness, relevancy, harmfulness) without manual annotation","Evaluate RAG system outputs against reference answers using semantic similarity rather than exact matching","Create custom evaluation rubrics for domain-specific quality criteria","Aggregate judge scores into metrics for model comparison and regression detection"],"best_for":["Teams evaluating LLM outputs for subjective qualities without access to human annotators","RAG system builders needing semantic evaluation beyond exact-match metrics","Organizations with domain-specific quality criteria requiring custom evaluation rubrics"],"limitations":["Judge LLM consistency varies across runs and models; no built-in calibration or inter-rater agreement measurement","Scoring is expensive (requires LLM API calls per output); no caching or batch optimization","Judge LLM can exhibit biases (e.g., preferring longer outputs, specific writing styles); no bias detection for the judge itself","Rubric quality directly impacts evaluation accuracy; poorly-defined rubrics produce unreliable scores"],"requires":["Python 3.9+","LLM provider credentials for the judge model","Evaluation rubric definition (criteria, scale, examples)","Model outputs to evaluate"],"input_types":["Model outputs (text)","Reference answers or ground truth (optional)","Evaluation rubric (criteria, scale, examples)"],"output_types":["Structured scores (numeric or categorical)","Aggregated metrics (mean, distribution)","Detailed evaluation reports with judge reasoning"],"categories":["safety-moderation","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"giskard__cap_6","uri":"capability://safety.moderation.bias.and.fairness.detection.with.demographic.slicing.and.performance.comparison","name":"bias and fairness detection with demographic slicing and performance comparison","description":"Giskard's bias detection system identifies performance disparities across demographic groups by slicing datasets by protected attributes (gender, age, income, etc.) and comparing model performance metrics across slices. The framework includes detectors for stereotypes (biased associations in outputs), performance bias (accuracy disparities), and correlation-based bias (spurious correlations with protected attributes). Results are reported with per-slice metrics and statistical significance testing.","intents":["Detect performance disparities across demographic groups to identify fairness issues","Identify stereotypes and biased associations in model outputs","Measure correlation between model predictions and protected attributes","Generate fairness reports for compliance and audit documentation"],"best_for":["Compliance teams conducting fairness audits for regulated industries (lending, hiring, healthcare)","ML teams building consumer-facing products needing fairness validation","Organizations with fairness requirements in procurement or vendor evaluation"],"limitations":["Demographic slicing requires pre-computed or inferred demographic attributes; no automatic demographic inference","Statistical significance testing assumes sufficient sample size per slice; small slices produce unreliable results","Bias detection is relative to chosen demographic groups; intersectional biases (e.g., gender + race) require custom analysis","Fairness metrics are context-dependent (e.g., equal opportunity vs. demographic parity); framework doesn't recommend which metric to use"],"requires":["Python 3.9+","Dataset with demographic attributes or custom demographic inference","Model wrapper implementing BaseModel interface","Sufficient sample size per demographic slice for statistical validity"],"input_types":["Model (BaseModel subclass)","Dataset with demographic attributes","Performance metric to compare across slices"],"output_types":["Per-slice performance metrics","Fairness metrics (performance disparity, stereotype scores)","Statistical significance tests","Fairness audit reports"],"categories":["safety-moderation","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"giskard__cap_7","uri":"capability://safety.moderation.prompt.injection.and.adversarial.input.detection.with.pattern.matching.and.semantic.analysis","name":"prompt injection and adversarial input detection with pattern matching and semantic analysis","description":"Giskard detects prompt injection attacks by combining pattern-based detection (matching known injection payloads from a curated database) with semantic analysis using LLM-as-judge to identify injection attempts that evade pattern matching. The framework includes detectors for character-based injections (special characters, encoding tricks) and semantic injections (instructions disguised as natural language). Detection results identify vulnerable inputs and suggest remediation strategies.","intents":["Detect prompt injection attacks in production LLM applications before they cause harm","Identify inputs that attempt to override system prompts or extract sensitive information","Test robustness of LLM applications against adversarial inputs","Generate test cases for prompt injection vulnerabilities to prevent regression"],"best_for":["Teams deploying LLM applications in security-sensitive contexts (customer support, financial services)","Security teams conducting adversarial testing of LLM systems","Organizations with regulatory requirements for adversarial robustness testing"],"limitations":["Pattern-based detection relies on curated payload databases; novel injection techniques may evade detection","Semantic detection via LLM-as-judge can be inconsistent and may miss sophisticated injections","No built-in defense mechanisms; framework detects injections but doesn't prevent them","False positive rate can be high for legitimate inputs that resemble injection patterns (e.g., code snippets, technical documentation)"],"requires":["Python 3.9+","LLM provider credentials for semantic injection detection","Model wrapper implementing BaseModel interface","Representative inputs for testing"],"input_types":["Text inputs (prompts, user queries)","Model (BaseModel subclass)"],"output_types":["Injection detection results (detected/not detected)","Injection type classification (pattern-based, semantic)","Vulnerable input samples","Remediation suggestions"],"categories":["safety-moderation","testing-quality"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"giskard__cap_8","uri":"capability://safety.moderation.hallucination.and.faithfulness.detection.with.reference.based.and.reference.free.evaluation","name":"hallucination and faithfulness detection with reference-based and reference-free evaluation","description":"Giskard detects hallucinations (factually incorrect outputs) using two approaches: reference-based evaluation (comparing outputs against ground truth or retrieved context) and reference-free evaluation (using LLM-as-judge to assess factual consistency). For RAG systems, the framework measures faithfulness by checking if generated answers are supported by retrieved documents. Detectors identify hallucination types (contradictions, fabrications, out-of-context claims) and flag problematic outputs.","intents":["Detect hallucinations in LLM outputs to prevent misinformation in production systems","Measure faithfulness of RAG system outputs to ensure they're grounded in retrieved context","Identify which RAG components (retriever, generator) are causing hallucinations","Generate test cases for hallucination vulnerabilities to prevent regression"],"best_for":["RAG system builders needing to ensure generated answers are grounded in retrieved context","Teams deploying LLMs in high-stakes domains (healthcare, legal, financial) where hallucinations are costly","Organizations with regulatory requirements for factual accuracy (e.g., financial reporting, medical advice)"],"limitations":["Reference-based evaluation requires ground truth or high-quality retrieved context; unreliable for open-domain questions","Reference-free evaluation via LLM-as-judge is inconsistent and may miss subtle hallucinations","Hallucination detection is probabilistic; no deterministic guarantee of catching all hallucinations","Context-dependent hallucinations (correct facts but wrong context) are difficult to detect without domain knowledge"],"requires":["Python 3.9+","LLM provider credentials for reference-free evaluation","Model wrapper implementing BaseModel interface","For reference-based evaluation: ground truth answers or retrieved context"],"input_types":["Model outputs (text)","Optional: reference answers or retrieved context","Model (BaseModel subclass)"],"output_types":["Hallucination detection results (hallucinated/faithful)","Hallucination type classification (contradiction, fabrication, out-of-context)","Faithfulness scores","Hallucination samples with explanations"],"categories":["safety-moderation","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"giskard__cap_9","uri":"capability://tool.use.integration.model.wrapper.abstraction.with.unified.prediction.interface","name":"model wrapper abstraction with unified prediction interface","description":"Giskard provides a BaseModel abstraction that wraps any model (LLM, traditional ML, RAG system) behind a unified predict() interface. Wrappers handle model-specific details (API calls, batch processing, response parsing) while exposing a consistent interface for testing and evaluation. 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end-to-end systems require refactoring","Metrics like 'faithfulness' rely on LLM-as-judge scoring, which can be inconsistent across runs and models","No built-in optimization suggestions — reports identify problems but don't recommend fixes","Stochasticity detection requires multiple model runs, increasing evaluation cost and latency","Calibration analysis assumes confidence scores are available; not applicable to models without confidence outputs","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7,"quality":0.9,"ecosystem":0.39999999999999997,"match_graph":0.25,"freshness":0.52,"weights":{"adoption":0.25,"quality":0.35,"ecosystem":0.15,"match_graph":0.2,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-06-17T09:51:04.691Z","last_scraped_at":null,"last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=giskard","compare_url":"https://unfragile.ai/compare?artifact=giskard"}},"signature":"gePjBXCsUtyTTfVSP/GDHKMR2ZAl/oTWenTA8W5cXMZYHU+0cKu1hiaDtVrwbN4nwZi/wyaYfd1uzdfP8p3NBA==","signedAt":"2026-06-21T18:21:48.821Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/giskard","artifact":"https://unfragile.ai/giskard","verify":"https://unfragile.ai/api/v1/verify?slug=giskard","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}