{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"tool_codiga","slug":"codiga","name":"Codiga","type":"product","url":"https://www.codiga.io","page_url":"https://unfragile.ai/codiga","categories":["code-review-security","testing-quality"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"tool_codiga__cap_0","uri":"capability://code.generation.editing.real.time.ide.integrated.static.code.analysis.with.ai.powered.rule.suggestions","name":"real-time ide-integrated static code analysis with ai-powered rule suggestions","description":"Codiga embeds a static analysis engine directly into IDE environments (VS Code, JetBrains, etc.) that performs incremental AST-based parsing and pattern matching on code as it's typed, surfacing violations and quality issues with sub-second latency. The system uses AI to generate contextual rule suggestions based on detected anti-patterns, reducing manual rule configuration. Analysis results are streamed to the editor as inline diagnostics without requiring full file saves or CI/CD pipeline execution.","intents":["Get instant feedback on code quality issues while typing, not after commit","Discover and apply relevant linting rules automatically without manual configuration","Catch bugs and security vulnerabilities before pushing to version control","Reduce the cognitive load of remembering team coding standards"],"best_for":["Solo developers and small teams wanting lightweight, real-time code quality feedback","Early-stage startups avoiding the operational overhead of SonarQube or Checkmarx","Teams using VS Code or JetBrains IDEs as their primary development environment"],"limitations":["Incremental analysis may miss cross-file dependency violations that require whole-codebase context","AI rule suggestions are probabilistic and may generate false positives or irrelevant rules for niche codebases","Real-time analysis adds CPU/memory overhead to IDE process, potentially impacting editor responsiveness on large files (>10K lines)","Limited to languages with AST parsers available (Python, JavaScript, TypeScript, Java, Go); no C/C++ or Rust support"],"requires":["VS Code 1.50+ or JetBrains IDE 2020.1+","Internet connection for AI rule suggestion backend","Python 3.7+, Node.js 12+, Java 8+, or Go 1.13+ depending on language being analyzed"],"input_types":["source code (Python, JavaScript, TypeScript, Java, Go)","IDE buffer/unsaved file state"],"output_types":["inline diagnostics (error/warning/info severity levels)","suggested rules with explanations","fix suggestions where applicable"],"categories":["code-generation-editing","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_codiga__cap_1","uri":"capability://code.generation.editing.multi.language.code.quality.rule.engine.with.extensible.pattern.matching","name":"multi-language code quality rule engine with extensible pattern matching","description":"Codiga implements a language-agnostic rule evaluation framework that parses source code into Abstract Syntax Trees (ASTs) for Python, JavaScript, TypeScript, Java, and Go, then applies pattern-matching rules against these trees to detect violations. Rules are defined as declarative patterns (likely YAML or JSON-based) that specify AST node types, attributes, and relationships to match. The engine supports both built-in rules and user-defined custom rules, with rules organized by category (security, performance, style, best-practices).","intents":["Define custom code quality rules specific to my team's coding standards","Enforce security best practices across multiple programming languages uniformly","Detect performance anti-patterns (e.g., N+1 queries, inefficient loops) automatically","Reuse rules across projects without duplicating configuration"],"best_for":["Teams with polyglot codebases wanting unified quality standards across languages","Organizations building internal coding standards that need enforcement automation","Development teams prioritizing security and performance rule enforcement"],"limitations":["Rule creation requires understanding AST structure and pattern syntax; no visual rule builder","Custom rules are not shared across the Codiga community due to limited ecosystem maturity","Rule performance degrades on very large files (>50K lines) due to AST traversal complexity","No support for semantic analysis (e.g., type inference, data flow) — only syntactic pattern matching"],"requires":["Knowledge of target language's AST structure","Rule definition in Codiga's pattern language (format unknown from public docs)","Internet connection to sync rules across IDE and CI/CD"],"input_types":["source code in Python, JavaScript, TypeScript, Java, or Go","rule definitions (YAML/JSON format, assumed)"],"output_types":["rule violation reports with line/column numbers","categorized rule results (security, performance, style, best-practices)","rule metadata (severity, description, remediation guidance)"],"categories":["code-generation-editing","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_codiga__cap_2","uri":"capability://automation.workflow.ci.cd.pipeline.integration.with.automated.code.quality.gates","name":"ci/cd pipeline integration with automated code quality gates","description":"Codiga integrates into CI/CD systems (GitHub Actions, GitLab CI, Jenkins, etc.) as a build step that runs static analysis on pull requests or commits, blocking merges if quality thresholds are violated. The integration uses webhook-based triggers to initiate analysis on code push events, aggregates results into a pass/fail gate, and posts inline comments on pull requests with violation details. Results are persisted and compared against baseline metrics to track quality trends over time.","intents":["Prevent low-quality code from merging into main branches automatically","Get code review feedback from static analysis before human review","Track code quality metrics over time to identify regressions","Enforce team coding standards without manual code review overhead"],"best_for":["Teams using GitHub, GitLab, or Jenkins for CI/CD automation","Organizations wanting to shift quality checks left without adding manual review burden","Projects with strict quality gates (e.g., financial, healthcare, security-critical)"],"limitations":["CI/CD integration adds 30-120 seconds per build depending on codebase size and rule complexity","Pull request comment posting requires OAuth permissions that may conflict with enterprise security policies","No support for on-premise CI/CD systems without internet connectivity (SaaS-only architecture)","Quality gate thresholds are global; no per-project or per-team customization"],"requires":["GitHub, GitLab, Jenkins, or other CI/CD platform with webhook support","Codiga API key with CI/CD permissions","Network access from CI/CD runner to Codiga backend"],"input_types":["source code diff (pull request changes)","commit metadata (branch, author, message)"],"output_types":["pass/fail gate status","inline pull request comments with violation details","quality metrics (violations per file, trend data)"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_codiga__cap_3","uri":"capability://planning.reasoning.ai.powered.code.quality.rule.generation.and.recommendation","name":"ai-powered code quality rule generation and recommendation","description":"Codiga uses machine learning models trained on code patterns and violations to automatically suggest relevant rules based on detected anti-patterns in a codebase. When the analyzer encounters repeated violations or suspicious patterns, the AI backend generates rule recommendations with explanations and severity levels. These suggestions are surfaced in the IDE and CI/CD reports, allowing developers to adopt rules with a single click rather than manually configuring them.","intents":["Discover coding standards and best practices relevant to my specific codebase automatically","Get rule recommendations without needing to research or configure rules manually","Adopt security and performance rules proactively before issues occur in production","Reduce the time spent on code review by automating standard violation detection"],"best_for":["Teams new to static analysis wanting guidance on which rules to enable","Developers working in unfamiliar languages or frameworks needing best-practice guidance","Organizations wanting to establish coding standards without hiring a dedicated standards committee"],"limitations":["AI suggestions are probabilistic and may recommend irrelevant rules for niche or legacy codebases","No transparency into which patterns triggered a rule recommendation (black-box ML model)","Rule recommendations are global; no per-team or per-project customization of suggestion criteria","Suggestions require internet connectivity to Codiga's ML backend; no offline mode"],"requires":["Internet connection to Codiga's AI backend","Sufficient code samples (>1000 lines) for ML model to generate meaningful suggestions","Opt-in to telemetry/code analysis for training data (privacy consideration)"],"input_types":["source code patterns and detected violations","codebase metadata (language, framework, project size)"],"output_types":["rule recommendations with severity and explanation","adoption suggestions (one-click enable)","confidence scores for recommendations"],"categories":["planning-reasoning","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_codiga__cap_4","uri":"capability://data.processing.analysis.incremental.codebase.analysis.with.change.based.violation.detection","name":"incremental codebase analysis with change-based violation detection","description":"Codiga implements incremental analysis that tracks code changes (diffs) and re-analyzes only modified files and their dependents, rather than scanning the entire codebase on every check. The system maintains a baseline of previous analysis results and compares new results against this baseline to identify new violations, fixed violations, and unchanged issues. This approach reduces analysis time from minutes (full scan) to seconds (incremental scan) for large codebases.","intents":["Get fast feedback on code quality changes without waiting for full codebase scans","Focus code review on new violations introduced by my changes, not pre-existing issues","Track which violations were introduced by which commits for accountability","Reduce CI/CD pipeline duration by skipping analysis of unchanged code"],"best_for":["Teams with large codebases (>100K lines) where full scans are prohibitively slow","Projects with frequent commits wanting sub-second feedback on changes","Organizations tracking code quality metrics over time and needing baseline comparisons"],"limitations":["Incremental analysis may miss violations in unchanged code that depend on modified code (e.g., type inference across files)","Baseline tracking requires persistent state storage; no support for ephemeral CI/CD runners without state persistence","Change detection is file-based; no support for semantic-level change detection (e.g., function signature changes affecting callers)","Incremental analysis is less accurate than full scans for cross-file dependency analysis"],"requires":["Git or other version control system for diff tracking","Persistent storage for baseline analysis results (local cache or cloud backend)","Network connectivity to sync baseline state across IDE and CI/CD"],"input_types":["code diffs (git diff format)","previous analysis baseline (JSON/binary format)"],"output_types":["new violations (introduced by changes)","fixed violations (resolved by changes)","unchanged violations (pre-existing issues)","analysis time metrics"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_codiga__cap_5","uri":"capability://safety.moderation.security.vulnerability.detection.with.owasp.and.cwe.mapping","name":"security vulnerability detection with owasp and cwe mapping","description":"Codiga includes a security-focused rule set that detects common vulnerabilities (SQL injection, XSS, insecure deserialization, hardcoded secrets, etc.) and maps findings to OWASP Top 10 and CWE (Common Weakness Enumeration) standards. The detection engine uses pattern matching on ASTs to identify dangerous function calls, unsafe data flows, and insecure configurations. Security violations are prioritized with severity levels (critical, high, medium, low) and include remediation guidance.","intents":["Detect OWASP Top 10 vulnerabilities in my code automatically before deployment","Find hardcoded secrets and credentials that could leak sensitive information","Map detected vulnerabilities to industry standards (CWE, OWASP) for compliance reporting","Get actionable remediation guidance for security issues without hiring security experts"],"best_for":["Teams building security-sensitive applications (fintech, healthcare, SaaS)","Organizations needing to demonstrate security compliance (SOC 2, ISO 27001, HIPAA)","Developers new to security best practices wanting automated guidance"],"limitations":["Pattern-based detection misses sophisticated vulnerabilities requiring semantic analysis (e.g., second-order SQL injection, type confusion attacks)","No support for runtime vulnerability detection or dynamic analysis; only static patterns","Security rules are generic and may generate false positives for legitimate use cases (e.g., intentional string concatenation in logging)","No integration with vulnerability databases (NVD, CVE) for dependency scanning"],"requires":["Source code access (no binary/compiled code analysis)","Support for target language (Python, JavaScript, TypeScript, Java, Go)"],"input_types":["source code in supported languages","security rule definitions (built-in or custom)"],"output_types":["security violations with severity levels","OWASP Top 10 and CWE mappings","remediation guidance and code examples","security metrics (vulnerability count, trend data)"],"categories":["safety-moderation","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_codiga__cap_6","uri":"capability://data.processing.analysis.code.quality.metrics.aggregation.and.trend.tracking","name":"code quality metrics aggregation and trend tracking","description":"Codiga collects and aggregates code quality metrics (violation count, severity distribution, rule coverage, code duplication, complexity scores) across commits and time periods, storing historical data to enable trend analysis. The system generates dashboards and reports showing quality metrics over time, allowing teams to track improvements or regressions. Metrics are broken down by file, module, rule category, and severity level for granular visibility.","intents":["Track code quality improvements or regressions over time","Identify which files or modules have the most violations","Measure the impact of code quality initiatives on team metrics","Generate compliance reports showing code quality trends for audits"],"best_for":["Teams wanting to measure and communicate code quality improvements to stakeholders","Organizations tracking engineering metrics for performance reviews or process improvement","Projects needing historical quality data for compliance or audit purposes"],"limitations":["Metrics are based on static analysis violations, not actual bugs or production incidents (correlation, not causation)","Trend analysis requires consistent rule configuration over time; rule changes invalidate historical comparisons","No integration with runtime metrics (performance, reliability) or business metrics (user satisfaction, revenue impact)","Dashboard customization is limited; no support for custom metric definitions"],"requires":["Continuous analysis history (at least 2-4 weeks of data for meaningful trends)","Consistent rule configuration across time period being analyzed","Internet connectivity to Codiga's metrics backend"],"input_types":["analysis results from each commit/pull request","rule configuration metadata"],"output_types":["quality metrics (violation count, severity distribution, complexity scores)","trend data (improvement/regression over time)","dashboards and reports (HTML, PDF, JSON)","per-file and per-module breakdowns"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_codiga__cap_7","uri":"capability://code.generation.editing.ide.extension.with.inline.violation.diagnostics.and.quick.fix.suggestions","name":"ide extension with inline violation diagnostics and quick-fix suggestions","description":"Codiga provides IDE extensions (VS Code, JetBrains IDEs) that display code quality violations as inline diagnostics (squiggly underlines, gutter icons) and offer quick-fix suggestions via IDE code actions. When a violation is detected, the extension highlights the problematic code, displays the rule name and explanation, and provides one-click fixes where applicable (e.g., auto-formatting, removing unused variables). The extension integrates with native IDE features (problems panel, breadcrumbs, hover tooltips) for seamless user experience.","intents":["See code quality issues as I type without leaving my IDE","Apply quick fixes to violations with a single click","Understand why a violation was flagged with inline explanations","Integrate code quality feedback into my existing IDE workflow"],"best_for":["Developers using VS Code or JetBrains IDEs as primary development environment","Teams wanting real-time feedback without context-switching to separate tools","Individual developers and small teams avoiding the overhead of external SAST tools"],"limitations":["IDE extension performance depends on IDE resources; may slow down editor on large files or underpowered machines","Quick-fix suggestions are limited to simple transformations (formatting, variable removal); complex refactorings not supported","No support for other IDEs (Sublime Text, Vim, Emacs, Visual Studio); limited to VS Code and JetBrains","Extension requires internet connectivity for AI rule suggestions and backend analysis"],"requires":["VS Code 1.50+ or JetBrains IDE 2020.1+","Codiga extension installed from marketplace","Codiga account and API key for authentication"],"input_types":["source code in editor buffer","IDE events (file open, text change, save)"],"output_types":["inline diagnostics (squiggly underlines, gutter icons)","quick-fix suggestions (code actions)","hover tooltips with rule explanations","problems panel entries"],"categories":["code-generation-editing","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_codiga__cap_8","uri":"capability://automation.workflow.free.tier.with.community.rule.library.and.limited.analysis.capacity","name":"free tier with community rule library and limited analysis capacity","description":"Codiga offers a free tier that includes basic static analysis with built-in rules, IDE extension access, and limited CI/CD integration (e.g., 1-2 runs per day or 100 analyses per month). The free tier includes access to a community rule library (though limited compared to paid tiers) and basic metrics tracking. Paid tiers unlock unlimited analysis runs, advanced rules, team collaboration features, and priority support.","intents":["Try Codiga without upfront cost to evaluate if it fits my workflow","Use Codiga for personal projects or small teams without budget constraints","Get basic code quality feedback for open-source projects"],"best_for":["Individual developers and open-source projects with limited budgets","Early-stage startups evaluating code quality tools before committing to paid solutions","Teams wanting to pilot Codiga before enterprise-wide adoption"],"limitations":["Free tier has strict rate limits (1-2 CI/CD runs per day or 100 analyses per month) that make it impractical for active development","Community rule library is smaller than paid tiers; advanced security and performance rules may be unavailable","No team collaboration features (shared dashboards, role-based access) on free tier","Free tier may have longer analysis latency or lower priority in backend queues"],"requires":["Codiga account (free signup)","No payment method required for free tier"],"input_types":["source code in supported languages"],"output_types":["basic analysis results","limited metrics and dashboards","community rules"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":40,"verified":false,"data_access_risk":"high","permissions":["VS Code 1.50+ or JetBrains IDE 2020.1+","Internet connection for AI rule suggestion backend","Python 3.7+, Node.js 12+, Java 8+, or Go 1.13+ depending on language being analyzed","Knowledge of target language's AST structure","Rule definition in Codiga's pattern language (format unknown from public docs)","Internet connection to sync rules across IDE and CI/CD","GitHub, GitLab, Jenkins, or other CI/CD platform with webhook support","Codiga API key with CI/CD permissions","Network access from CI/CD runner to Codiga backend","Internet connection to Codiga's AI backend"],"failure_modes":["Incremental analysis may miss cross-file dependency violations that require whole-codebase context","AI rule suggestions are probabilistic and may generate false positives or irrelevant rules for niche codebases","Real-time analysis adds CPU/memory overhead to IDE process, potentially impacting editor responsiveness on large files (>10K lines)","Limited to languages with AST parsers available (Python, JavaScript, TypeScript, Java, Go); no C/C++ or Rust support","Rule creation requires understanding AST structure and pattern syntax; no visual rule builder","Custom rules are not shared across the Codiga community due to limited ecosystem maturity","Rule performance degrades on very large files (>50K lines) due to AST traversal complexity","No support for semantic analysis (e.g., type inference, data flow) — only syntactic pattern matching","CI/CD integration adds 30-120 seconds per build depending on codebase size and rule complexity","Pull request comment posting requires OAuth permissions that may conflict with enterprise security policies","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.31666666666666665,"quality":0.67,"ecosystem":0.25,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.1,"match_graph":0.35,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:29.717Z","last_scraped_at":"2026-04-05T13:23:42.561Z","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=codiga","compare_url":"https://unfragile.ai/compare?artifact=codiga"}},"signature":"QRCxofTY4DZiDzlWV0+OrJLL0+v53OOXg0mPaGtYkcbLGven9gdMRU/mwodSFA1jASsFlnbUH5YOPd0G6CwaCA==","signedAt":"2026-06-22T18:11:31.218Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/codiga","artifact":"https://unfragile.ai/codiga","verify":"https://unfragile.ai/api/v1/verify?slug=codiga","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"}}