{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"kubectl-ai","slug":"kubectl-ai","name":"kubectl-ai","type":"repo","url":"https://github.com/sozercan/kubectl-ai","page_url":"https://unfragile.ai/kubectl-ai","categories":["frameworks-sdks"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"kubectl-ai__cap_0","uri":"capability://code.generation.editing.natural.language.to.kubernetes.manifest.generation","name":"natural-language-to-kubernetes-manifest-generation","description":"Translates plain English descriptions into valid Kubernetes YAML manifests by sending user input to OpenAI/compatible LLM endpoints and parsing structured YAML output. The system bridges natural language intent with Kubernetes resource schemas through a stateless prompt-completion pipeline, optionally enriching prompts with Kubernetes OpenAPI specifications to improve schema accuracy and reduce hallucinations.","intents":["I want to describe a Kubernetes deployment in English and get valid YAML without memorizing the schema","I need to quickly prototype a Kubernetes resource during development without writing YAML by hand","I want to generate a complex multi-resource manifest from a single natural language description","I need to modify an existing Kubernetes resource by describing the changes in English"],"best_for":["Kubernetes operators and SREs learning new resource types","DevOps teams prototyping infrastructure rapidly","Solo developers building Kubernetes-native applications","Teams automating manifest generation in CI/CD pipelines"],"limitations":["LLM hallucinations can produce syntactically valid but semantically incorrect YAML (e.g., wrong API versions, unsupported field combinations)","Requires internet connectivity to OpenAI/Azure endpoints unless using local models, adding latency (typically 2-5 seconds per manifest)","No built-in validation against actual Kubernetes cluster capabilities or installed CRDs","Temperature hardcoded to 0 by default, limiting creative generation for complex scenarios","Cannot generate manifests for custom resources (CRDs) without explicit schema injection"],"requires":["kubectl installed and configured with cluster access","OpenAI API key OR Azure OpenAI credentials OR compatible local LLM endpoint (Ollama, vLLM, etc.)","Go runtime (for building from source) or pre-compiled binary","Network access to LLM endpoint (unless using local models)"],"input_types":["plain text natural language description","existing Kubernetes YAML (via stdin for modification)","command-line flags for configuration"],"output_types":["YAML manifest (formatted for terminal display or raw for piping)","structured Kubernetes resource definition"],"categories":["code-generation-editing","kubernetes-infrastructure"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"kubectl-ai__cap_1","uri":"capability://tool.use.integration.multi.provider.llm.endpoint.abstraction","name":"multi-provider-llm-endpoint-abstraction","description":"Abstracts LLM provider differences through a unified CLI interface supporting OpenAI, Azure OpenAI, and compatible local endpoints (Ollama, vLLM, LM Studio). Configuration is handled via environment variables and CLI flags with provider-specific mappings (e.g., AZURE_OPENAI_MAP for deployment name translation), allowing users to swap providers without code changes.","intents":["I want to use kubectl-ai with my local Ollama instance instead of paying for OpenAI API calls","I need to run kubectl-ai in an air-gapped environment without internet access","I want to switch from OpenAI to Azure OpenAI for compliance or cost reasons","I need to map OpenAI model names to my Azure deployment names automatically"],"best_for":["Organizations with air-gapped Kubernetes clusters","Teams using Azure as their cloud provider","Cost-conscious teams running local LLMs (Ollama, Mistral, Llama 2)","Enterprises with strict data residency requirements"],"limitations":["Local LLM quality varies significantly; smaller models (7B-13B parameters) produce lower-quality manifests than GPT-3.5/4","Azure OpenAI requires explicit deployment name mapping via AZURE_OPENAI_MAP, adding configuration complexity","No built-in provider auto-detection; users must explicitly set OPENAI_ENDPOINT for non-OpenAI providers","Endpoint compatibility is best-effort; non-OpenAI-compatible APIs may fail silently or produce parsing errors"],"requires":["OPENAI_API_KEY environment variable (for OpenAI/Azure) OR running local LLM on accessible endpoint","OPENAI_ENDPOINT environment variable (optional, defaults to OpenAI's public endpoint)","OPENAI_DEPLOYMENT_NAME or AZURE_OPENAI_MAP for Azure OpenAI","Network connectivity to chosen endpoint"],"input_types":["environment variables","CLI flags (--openai-api-key, --openai-endpoint, --openai-deployment-name, --azure-openai-map)"],"output_types":["LLM completion response (YAML manifest)"],"categories":["tool-use-integration","kubernetes-infrastructure"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"kubectl-ai__cap_10","uri":"capability://tool.use.integration.openai.and.azure.openai.api.integration","name":"openai-and-azure-openai-api-integration","description":"Integrates with OpenAI and Azure OpenAI APIs using the go-openai client library, supporting both public OpenAI endpoints and Azure-hosted deployments. For Azure, the system maps OpenAI model names to Azure deployment names via AZURE_OPENAI_MAP, handling the naming mismatch between OpenAI's model-centric API and Azure's deployment-centric API. Supports custom endpoints via OPENAI_ENDPOINT for compatible local services.","intents":["I want to use OpenAI's GPT models (GPT-3.5, GPT-4) for manifest generation","I need to use Azure OpenAI instead of public OpenAI for compliance or cost reasons","I want to map OpenAI model names to my Azure deployment names automatically","I need to use a custom LLM endpoint compatible with OpenAI API format"],"best_for":["Organizations using OpenAI as their primary LLM provider","Azure customers leveraging Azure OpenAI for compliance and data residency","Teams with custom LLM deployments compatible with OpenAI API format","Enterprises requiring specific model versions (GPT-3.5-turbo, GPT-4, etc.)"],"limitations":["OpenAI API calls incur per-token costs; no built-in cost tracking or budgeting","Azure OpenAI requires explicit deployment name mapping (AZURE_OPENAI_MAP); no automatic discovery","Model availability varies by region and subscription; no built-in validation of model availability","API rate limits are not handled; rapid invocations may hit rate limits without retry logic","Custom endpoints must be OpenAI API-compatible; incompatible APIs fail with unclear error messages"],"requires":["OpenAI API key (OPENAI_API_KEY) for public OpenAI","Azure OpenAI API key and endpoint for Azure deployments","Valid model/deployment name (OPENAI_DEPLOYMENT_NAME or AZURE_OPENAI_MAP)","Network access to OpenAI or Azure endpoints"],"input_types":["API credentials (API key, endpoint URL)","model/deployment name","natural language manifest description"],"output_types":["LLM completion response (YAML manifest)"],"categories":["tool-use-integration","kubernetes-infrastructure"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"kubectl-ai__cap_11","uri":"capability://text.generation.language.terminal.rendering.and.syntax.highlighting","name":"terminal-rendering-and-syntax-highlighting","description":"Uses the glamour library to render generated YAML manifests in the terminal with syntax highlighting, color coding, and formatted output. Glamour automatically detects terminal capabilities and applies appropriate formatting (ANSI colors, markdown rendering), improving readability of complex manifests without requiring external tools.","intents":["I want to see generated YAML with syntax highlighting for easier review","I need readable formatting of complex manifests in the terminal","I want to understand manifest structure through visual formatting","I need to copy-paste formatted YAML from terminal output"],"best_for":["Interactive terminal sessions where users review manifests visually","Learning scenarios where formatting helps understand YAML structure","Teams using modern terminals with color support (iTerm2, Windows Terminal, etc.)","Developers preferring formatted output over raw YAML"],"limitations":["Glamour rendering adds ~50-100ms latency for large manifests (>10KB)","Terminal color support varies; some terminals may not render colors correctly","Glamour output is not suitable for piping to other tools; use --raw flag instead","No customization of color scheme or formatting; glamour uses built-in themes","Rendering is disabled in non-TTY environments (pipes, redirects); automatic fallback to raw output"],"requires":["Terminal with TTY support","Terminal with color support (optional; falls back to plain text)","glamour library (included in binary)"],"input_types":["YAML manifest from LLM"],"output_types":["formatted YAML with syntax highlighting","colored terminal output"],"categories":["text-generation-language","kubernetes-infrastructure"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"kubectl-ai__cap_12","uri":"capability://tool.use.integration.kubernetes.cluster.api.access.and.context.management","name":"kubernetes-cluster-api-access-and-context-management","description":"Integrates with kubectl's cluster context and authentication system, using kubeconfig to access the Kubernetes cluster for applying manifests (kubectl apply) and optionally fetching OpenAPI specs (--use-k8s-api). The system respects kubectl's context switching and RBAC permissions, enabling multi-cluster workflows without separate authentication configuration.","intents":["I want kubectl-ai to apply manifests to my current kubectl context","I need to switch between multiple Kubernetes clusters without reconfiguring kubectl-ai","I want to fetch OpenAPI specs from my cluster to ground manifest generation","I need kubectl-ai to respect my RBAC permissions and namespace restrictions"],"best_for":["Multi-cluster Kubernetes environments using kubectl context switching","Teams with RBAC-restricted service accounts for kubectl-ai","Organizations using kubeconfig for cluster authentication","Workflows combining kubectl-ai with other kubectl commands"],"limitations":["kubectl-ai inherits RBAC restrictions from kubeconfig; insufficient permissions fail silently","No built-in cluster validation; invalid context or unreachable cluster fails at apply time","OpenAPI spec fetching requires cluster API access; air-gapped clusters require manual spec hosting","Context switching requires explicit kubectl config use-context; kubectl-ai doesn't provide context switching UI","No built-in support for cluster-specific configuration (namespace, resource quotas, etc.)"],"requires":["kubectl installed and configured with valid kubeconfig","Current kubectl context pointing to target cluster","RBAC permissions to create/apply resources in target namespace","Network access to Kubernetes API server"],"input_types":["kubectl context (from kubeconfig)","YAML manifest to apply"],"output_types":["kubectl apply output","Kubernetes OpenAPI spec (if --use-k8s-api enabled)"],"categories":["tool-use-integration","kubernetes-infrastructure"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"kubectl-ai__cap_2","uri":"capability://automation.workflow.interactive.manifest.review.and.confirmation.workflow","name":"interactive-manifest-review-and-confirmation-workflow","description":"Implements a human-in-the-loop approval workflow where generated YAML is displayed in the terminal (with optional syntax highlighting via glamour library) and users must explicitly confirm before applying to the cluster. The --require-confirmation flag (default true) enforces this gate; users can also inspect raw YAML via --raw flag for piping to external editors or validation tools.","intents":["I want to review the generated manifest before kubectl-ai applies it to my cluster","I need to edit the generated YAML in my preferred editor before applying","I want to pipe the generated manifest to other tools (kustomize, kubeval, policy engines) for validation","I need to disable confirmation prompts for fully automated CI/CD pipelines"],"best_for":["Manual Kubernetes operations where human review is required","CI/CD pipelines with external validation steps (kubeval, Kyverno, OPA)","Teams implementing GitOps workflows with manifest review gates","Learning environments where users inspect generated YAML to understand Kubernetes"],"limitations":["Interactive confirmation requires terminal TTY; cannot be used in non-interactive contexts (cron jobs, GitHub Actions without tty) without --require-confirmation=false","No built-in diff display; users see full manifest, not delta from existing resources","Glamour rendering adds ~50-100ms latency for large manifests (>10KB)","No integration with external editors; users must manually copy/paste or use shell redirection"],"requires":["Terminal with TTY support (for interactive confirmation)","User input capability (stdin for confirmation prompt)","kubectl with apply permissions on target namespace"],"input_types":["user confirmation input (yes/no prompt)","YAML manifest from LLM"],"output_types":["formatted YAML display (terminal-rendered or raw)","kubectl apply command execution (conditional on confirmation)"],"categories":["automation-workflow","kubernetes-infrastructure"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"kubectl-ai__cap_3","uri":"capability://memory.knowledge.kubernetes.openapi.schema.grounding","name":"kubernetes-openapi-schema-grounding","description":"Optionally enriches LLM prompts with Kubernetes OpenAPI specifications (fetched from cluster or custom URL via --k8s-openapi-url) to constrain manifest generation to valid schemas. When --use-k8s-api=true, the system fetches the cluster's OpenAPI spec, extracts relevant resource schemas, and includes them in the prompt context, reducing hallucinations and improving compliance with cluster-specific API versions and field constraints.","intents":["I want kubectl-ai to generate manifests that match my cluster's installed API versions and CRDs","I need to ensure generated manifests don't use deprecated API versions","I want to generate manifests for custom resources (CRDs) installed in my cluster","I need to validate that generated fields are actually supported by my Kubernetes version"],"best_for":["Clusters with custom resources (CRDs) that need to be generated","Teams managing multiple Kubernetes versions with different API availability","Organizations with strict API version compliance requirements","Air-gapped environments using custom Kubernetes distributions (OpenShift, Tanzu)"],"limitations":["OpenAPI spec fetching adds 1-3 seconds latency per invocation; no caching mechanism built-in","Large OpenAPI specs (>10MB for clusters with many CRDs) can exceed LLM context windows, requiring schema filtering","Custom URL fetching (--k8s-openapi-url) requires manual spec hosting; no built-in spec server","Schema grounding improves accuracy but doesn't eliminate hallucinations; LLM may still generate invalid field combinations","Requires cluster API access (RBAC permissions to read OpenAPI endpoint)"],"requires":["kubectl configured with cluster access","--use-k8s-api=true flag enabled","Kubernetes cluster with accessible OpenAPI endpoint (usually /openapi/v2 or /openapi/v3)","RBAC permissions to read cluster OpenAPI spec","Optional: --k8s-openapi-url for custom spec URL"],"input_types":["Kubernetes cluster OpenAPI specification (JSON/YAML)","natural language manifest description"],"output_types":["schema-grounded YAML manifest","LLM prompt with embedded schema context"],"categories":["memory-knowledge","kubernetes-infrastructure"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"kubectl-ai__cap_4","uri":"capability://code.generation.editing.stdin.piping.and.manifest.modification","name":"stdin-piping-and-manifest-modification","description":"Supports reading existing Kubernetes manifests from stdin and using them as context for modification requests. Users can pipe kubectl get output or existing YAML files to kubectl-ai with a modification prompt (e.g., 'add resource limits'), and the system sends both the existing manifest and the modification request to the LLM, returning the updated YAML.","intents":["I want to modify an existing Kubernetes resource by describing the change in English","I need to add resource limits to all my deployments by piping them through kubectl-ai","I want to update a ConfigMap by describing the new configuration in natural language","I need to batch-modify multiple resources using kubectl get | kubectl-ai pattern"],"best_for":["Bulk resource modification workflows (e.g., adding labels, updating resource limits)","Teams migrating resources between namespaces or clusters","GitOps workflows where manifests are version-controlled and modified via natural language","Learning scenarios where users understand manifest structure by seeing before/after diffs"],"limitations":["No built-in diff display; users cannot see what changed between input and output manifests","LLM may inadvertently remove or modify fields not mentioned in the modification request","Large manifests (>50KB) may exceed LLM context windows, causing truncation or errors","No validation that modifications are semantically correct (e.g., adding invalid labels)","Stdin piping requires explicit --raw flag or manual redirection; not default behavior"],"requires":["Existing Kubernetes manifest in YAML format","stdin piping capability (shell redirection or kubectl get output)","Natural language modification description"],"input_types":["YAML manifest (from stdin)","natural language modification request","CLI flags"],"output_types":["modified YAML manifest","raw YAML for piping to kubectl apply"],"categories":["code-generation-editing","kubernetes-infrastructure"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"kubectl-ai__cap_5","uri":"capability://automation.workflow.raw.output.mode.for.pipeline.integration","name":"raw-output-mode-for-pipeline-integration","description":"Provides --raw flag that outputs generated YAML without terminal formatting, confirmation prompts, or additional output, enabling seamless piping to other tools (kubectl apply, kubeval, kustomize, policy engines). Raw mode suppresses glamour rendering and confirmation gates, producing machine-readable output suitable for CI/CD automation.","intents":["I want to pipe kubectl-ai output directly to kubectl apply in a CI/CD pipeline","I need to validate generated manifests with kubeval before applying","I want to process kubectl-ai output through kustomize or other manifest tools","I need to store generated manifests in a file for GitOps workflows"],"best_for":["CI/CD pipelines (GitHub Actions, GitLab CI, Jenkins) requiring non-interactive output","Manifest validation workflows with external tools (kubeval, Kyverno, OPA)","GitOps repositories where manifests are generated and committed","Automation scripts combining kubectl-ai with other Kubernetes tools"],"limitations":["Raw mode disables all safety gates (confirmation prompts); generated manifests are output without review","No error messages or warnings in raw mode; failures are silent or produce invalid YAML","Requires explicit --raw flag; default behavior includes formatting and confirmation","No built-in integration with validation tools; piping to kubeval/Kyverno requires separate tool installation"],"requires":["--raw flag enabled","--require-confirmation=false (recommended for full automation)","Receiving tool capable of consuming YAML from stdin (kubectl, kubeval, kustomize, etc.)"],"input_types":["natural language manifest description","CLI flags (--raw, --require-confirmation=false)"],"output_types":["raw YAML manifest (no formatting, no prompts)","suitable for piping to other tools"],"categories":["automation-workflow","kubernetes-infrastructure"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"kubectl-ai__cap_6","uri":"capability://planning.reasoning.temperature.controlled.generation.randomness","name":"temperature-controlled-generation-randomness","description":"Exposes --temperature flag (0-1 range) to control LLM output randomness, defaulting to 0 for deterministic generation. Temperature 0 produces consistent, predictable manifests suitable for production; higher values (0.5-1.0) introduce creativity for exploratory scenarios. The flag is passed directly to the LLM API (OpenAI, Azure, or compatible endpoints).","intents":["I want deterministic manifest generation for reproducible CI/CD pipelines","I need to explore multiple manifest variations for the same description","I want to reduce hallucinations in generated manifests by using low temperature","I need creative resource naming or configuration suggestions"],"best_for":["Production CI/CD pipelines requiring deterministic output","Teams exploring multiple manifest variations for the same intent","Learning scenarios where users want to understand LLM behavior","Cost-optimization workflows where lower temperature reduces API token usage"],"limitations":["Temperature 0 doesn't eliminate hallucinations; it only reduces variance","Higher temperatures (>0.7) significantly increase manifest quality variance and error rates","Temperature is passed directly to LLM; behavior varies by model (GPT-3.5 vs Llama 2 vs Mistral)","No built-in guidance on optimal temperature for different scenarios","Local LLMs may not support temperature parameter or may interpret it differently"],"requires":["--temperature flag (optional, default 0)","LLM endpoint supporting temperature parameter"],"input_types":["temperature value (float 0-1)","natural language manifest description"],"output_types":["YAML manifest with controlled randomness"],"categories":["planning-reasoning","kubernetes-infrastructure"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"kubectl-ai__cap_7","uri":"capability://automation.workflow.debug.logging.and.diagnostic.output","name":"debug-logging-and-diagnostic-output","description":"Provides --debug flag that enables verbose logging of internal operations including LLM API requests/responses, Kubernetes API interactions, and configuration parsing. Debug output is written to stderr, allowing users to diagnose failures, inspect LLM prompts, and understand the system's decision-making without modifying code.","intents":["I want to see the exact prompt being sent to the LLM to understand why it generated incorrect YAML","I need to debug why kubectl-ai is failing to connect to my Kubernetes cluster","I want to inspect the LLM response before it's parsed into YAML","I need to troubleshoot configuration issues with my OpenAI or Azure endpoint"],"best_for":["Developers troubleshooting kubectl-ai failures","Teams debugging LLM hallucinations or incorrect manifest generation","Users configuring custom LLM endpoints or Kubernetes clusters","Support scenarios where detailed logs are needed to diagnose issues"],"limitations":["Debug output is verbose and may exceed 10KB for single invocation; not suitable for production logs","Sensitive information (API keys, cluster details) may be logged; requires careful handling in shared environments","Debug output is written to stderr, requiring shell redirection (2>&1) to capture in logs","No structured logging format (JSON); output is human-readable text, making parsing difficult","Debug flag must be explicitly enabled; no environment variable alternative"],"requires":["--debug flag enabled","stderr output capability (terminal or log file)"],"input_types":["CLI flag (--debug)"],"output_types":["verbose diagnostic logs to stderr","LLM API requests and responses","Kubernetes API interactions","configuration parsing details"],"categories":["automation-workflow","kubernetes-infrastructure"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"kubectl-ai__cap_8","uri":"capability://tool.use.integration.kubernetes.plugin.installation.and.discovery","name":"kubernetes-plugin-installation-and-discovery","description":"Integrates with kubectl's plugin system by installing as a kubectl subcommand (kubectl ai), enabling discovery via kubectl plugin list and invocation via kubectl ai [args]. The plugin follows kubectl plugin conventions (executable in PATH, named kubectl-ai), allowing seamless integration with existing kubectl workflows without requiring separate command invocation.","intents":["I want to use kubectl-ai as a native kubectl subcommand (kubectl ai) instead of a separate tool","I need kubectl to discover kubectl-ai automatically without manual PATH configuration","I want to integrate kubectl-ai into my kubectl workflow alongside other plugins","I need to verify that kubectl-ai is installed and discoverable"],"best_for":["Kubernetes operators familiar with kubectl plugin ecosystem","Teams using multiple kubectl plugins (kubectx, krew, etc.)","Organizations standardizing on kubectl as the primary CLI interface","Users wanting minimal CLI surface (single kubectl command instead of separate binary)"],"limitations":["Plugin installation requires manual binary placement in PATH or use of krew package manager","kubectl plugin discovery is slow (scans PATH for all kubectl-* executables); adds ~100-200ms to kubectl startup","Plugin naming convention (kubectl-ai) is rigid; no customization of subcommand name","Plugin system doesn't support plugin dependencies; each plugin must be independently installed","No built-in update mechanism; users must manually update binary or use krew"],"requires":["kubectl installed and in PATH","kubectl-ai binary in PATH (or installed via krew)","Executable permissions on kubectl-ai binary"],"input_types":["kubectl subcommand invocation (kubectl ai [args])"],"output_types":["kubectl plugin integration","kubectl plugin list discovery"],"categories":["tool-use-integration","kubernetes-infrastructure"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"kubectl-ai__cap_9","uri":"capability://automation.workflow.environment.variable.and.flag.based.configuration","name":"environment-variable-and-flag-based-configuration","description":"Supports dual configuration modes: environment variables (OPENAI_API_KEY, OPENAI_ENDPOINT, OPENAI_DEPLOYMENT_NAME, AZURE_OPENAI_MAP, REQUIRE_CONFIRMATION, TEMPERATURE, USE_K8S_API, K8S_OPENAPI_URL, DEBUG) and CLI flags (--openai-api-key, --openai-endpoint, etc.). CLI flags take precedence over environment variables, enabling both persistent configuration (via .bashrc, .zshrc) and per-invocation overrides.","intents":["I want to set my OpenAI API key once in environment variables instead of passing it every invocation","I need to override environment configuration for a single kubectl-ai invocation","I want to configure kubectl-ai in CI/CD pipelines using environment variables","I need to support multiple configurations (dev, staging, prod) via environment switching"],"best_for":["Local development workflows where configuration is set once in shell profile","CI/CD pipelines using environment variable injection (GitHub Actions secrets, GitLab CI variables)","Teams managing multiple environments with different LLM endpoints or API keys","Organizations using configuration management tools (Ansible, Terraform) to set environment variables"],"limitations":["Environment variables are visible in shell history and process listings; not suitable for sensitive data in shared environments","No configuration file support (.kubectl-ai.yaml, ~/.kubectl-ai); configuration is scattered across environment and CLI flags","CLI flag parsing is basic; no support for config file inheritance or profiles","No validation of environment variables at startup; invalid values fail silently at runtime","Azure OpenAI mapping (AZURE_OPENAI_MAP) requires manual JSON string in environment variable; no user-friendly format"],"requires":["Environment variables set in shell profile or CI/CD system","CLI flags passed as arguments","Precedence: CLI flags > environment variables"],"input_types":["environment variables","CLI flags"],"output_types":["configuration applied to kubectl-ai invocation"],"categories":["automation-workflow","kubernetes-infrastructure"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"kubectl-ai__headline","uri":"capability://tool.use.integration.ai.powered.kubernetes.manifest.generator","name":"ai-powered kubernetes manifest generator","description":"kubectl-ai is a plugin that uses AI to generate Kubernetes manifests from natural language descriptions, simplifying resource management for developers and enabling faster prototyping and learning.","intents":["best AI tool for Kubernetes","Kubernetes manifest generator for developers","AI plugin for Kubernetes resource creation","natural language to Kubernetes YAML generator","Kubernetes automation tools","Kubernetes tools for rapid prototyping"],"best_for":["developers looking to streamline Kubernetes management","teams working in air-gapped environments"],"limitations":["requires access to AI models","may need confirmation before applying changes"],"requires":["kubectl installed","AI model access"],"input_types":["natural language descriptions"],"output_types":["YAML manifests"],"categories":["tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":55,"verified":false,"data_access_risk":"high","permissions":["kubectl installed and configured with cluster access","OpenAI API key OR Azure OpenAI credentials OR compatible local LLM endpoint (Ollama, vLLM, etc.)","Go runtime (for building from source) or pre-compiled binary","Network access to LLM endpoint (unless using local models)","OPENAI_API_KEY environment variable (for OpenAI/Azure) OR running local LLM on accessible endpoint","OPENAI_ENDPOINT environment variable (optional, defaults to OpenAI's public endpoint)","OPENAI_DEPLOYMENT_NAME or AZURE_OPENAI_MAP for Azure OpenAI","Network connectivity to chosen endpoint","OpenAI API key (OPENAI_API_KEY) for public OpenAI","Azure OpenAI API key and endpoint for Azure deployments"],"failure_modes":["LLM hallucinations can produce syntactically valid but semantically incorrect YAML (e.g., wrong API versions, unsupported field combinations)","Requires internet connectivity to OpenAI/Azure endpoints unless using local models, adding latency (typically 2-5 seconds per manifest)","No built-in validation against actual Kubernetes cluster capabilities or installed CRDs","Temperature hardcoded to 0 by default, limiting creative generation for complex scenarios","Cannot generate manifests for custom resources (CRDs) without explicit schema injection","Local LLM quality varies significantly; smaller models (7B-13B parameters) produce lower-quality manifests than GPT-3.5/4","Azure OpenAI requires explicit deployment name mapping via AZURE_OPENAI_MAP, adding configuration complexity","No built-in provider auto-detection; users must explicitly set OPENAI_ENDPOINT for non-OpenAI providers","Endpoint compatibility is best-effort; non-OpenAI-compatible APIs may fail silently or produce parsing errors","OpenAI API calls incur per-token costs; no built-in cost tracking or budgeting","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":0.3,"quality":0.2,"ecosystem":0.15,"match_graph":0.3,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-06-17T09:51:04.692Z","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=kubectl-ai","compare_url":"https://unfragile.ai/compare?artifact=kubectl-ai"}},"signature":"BV9rZn40m9NTDRU6rP2//vETE9c8+DwYvOtNt3UbxaiSuCnluQmuhMVmfqeMF049IIBeX+cU6RPf4oaf6nTZAg==","signedAt":"2026-06-20T20:01:34.350Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/kubectl-ai","artifact":"https://unfragile.ai/kubectl-ai","verify":"https://unfragile.ai/api/v1/verify?slug=kubectl-ai","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"}}