{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"sgpt","slug":"sgpt","name":"sgpt","type":"cli","url":"https://github.com/tbckr/sgpt","page_url":"https://unfragile.ai/sgpt","categories":["cli-tools"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"sgpt__cap_0","uri":"capability://code.generation.editing.natural.language.to.shell.command.generation","name":"natural-language-to-shell-command generation","description":"Converts natural language descriptions into executable shell commands by sending user intent to LLM APIs (OpenAI or compatible) and parsing structured command output. The tool maintains shell context awareness, allowing it to generate commands tailored to the user's current environment and shell type (bash, zsh, fish, etc.). Output is presented for user review before execution, with optional one-shot execution mode for trusted workflows.","intents":["I want to generate a complex shell command without remembering the exact syntax","I need to construct a piped command combining multiple utilities","I want to automate repetitive command generation across different shells"],"best_for":["DevOps engineers and system administrators automating CLI workflows","Developers unfamiliar with specific shell utilities or command syntax","Teams standardizing command generation across heterogeneous shell environments"],"limitations":["LLM-generated commands may contain logical errors or unsafe operations — user review is essential","Requires network access to LLM API; offline operation not supported","Context window limitations mean very complex multi-step workflows may lose detail","Shell-specific syntax variations (bash vs zsh vs fish) require explicit specification"],"requires":["OpenAI API key or compatible LLM endpoint URL","Shell environment (bash, zsh, fish, or PowerShell)","Network connectivity to LLM provider","Go 1.16+ (for CLI compilation)"],"input_types":["natural language text description","shell type specification (optional)"],"output_types":["executable shell command string","command explanation text"],"categories":["code-generation-editing","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"sgpt__cap_1","uri":"capability://text.generation.language.interactive.shell.chat.mode.with.conversation.history","name":"interactive shell chat mode with conversation history","description":"Provides a multi-turn conversational interface within the terminal where users can ask follow-up questions and refine LLM responses iteratively. The tool maintains conversation history across turns, allowing context carryover for related queries. Chat mode operates as a REPL-like loop, accepting user input, sending to the LLM with full conversation context, and streaming responses back to the terminal with proper formatting.","intents":["I want to have a back-and-forth conversation with an LLM without leaving my terminal","I need to refine a shell command or code snippet through iterative feedback","I want to maintain context across multiple related questions in a single session"],"best_for":["Terminal-native developers who avoid GUI applications","Teams using pair programming or collaborative debugging in shared terminal sessions","Users building complex solutions requiring iterative refinement"],"limitations":["Terminal rendering may truncate or wrap long responses poorly on narrow terminals","Conversation history is session-local; no persistent storage across CLI invocations without explicit export","No built-in markdown rendering — code blocks and formatting appear as raw text","Token usage accumulates with each turn; long conversations may exceed API rate limits or incur high costs"],"requires":["OpenAI API key or compatible LLM endpoint","Terminal with ANSI color support (recommended)","Interactive shell environment (bash, zsh, fish, or PowerShell)"],"input_types":["natural language text (multi-line supported)","shell commands or code snippets for context"],"output_types":["natural language response text","code snippets","command suggestions"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"sgpt__cap_10","uri":"capability://memory.knowledge.multi.turn.conversation.state.management.with.context.preservation","name":"multi-turn conversation state management with context preservation","description":"Maintains conversation state across multiple turns in chat mode, preserving full message history and context for the LLM. Each turn includes the user's new message plus all previous messages, allowing the LLM to reference earlier parts of the conversation. State is held in memory during the session and can be optionally exported or saved to files for later retrieval.","intents":["I want to ask follow-up questions that reference earlier parts of the conversation","I need to maintain context across multiple related queries without repeating context","I want to save a conversation for later review or sharing"],"best_for":["Users engaged in iterative problem-solving or debugging","Teams collaborating on complex tasks requiring context carryover","Developers building conversational workflows"],"limitations":["Conversation state is session-local; no persistence across CLI invocations without explicit export","Token usage grows with conversation length; long conversations may exceed API limits or incur high costs","No built-in conversation pruning or summarization; context window limitations may force truncation","Exported conversations are plain text; no structured format for programmatic access"],"requires":["Interactive terminal for chat mode","OpenAI API key or compatible LLM endpoint","Sufficient API rate limits for multi-turn conversations"],"input_types":["user message (text)","optional: previous conversation history (for import)"],"output_types":["LLM response with full conversation context","exported conversation transcript (optional)"],"categories":["memory-knowledge","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"sgpt__cap_2","uri":"capability://code.generation.editing.code.generation.from.natural.language.specifications","name":"code generation from natural language specifications","description":"Generates code snippets in multiple programming languages (Python, JavaScript, Go, Rust, etc.) from natural language descriptions. The tool sends language-specific prompts to the LLM and returns formatted code blocks suitable for copy-paste or piping to files. Code generation respects language context when available (e.g., if invoked from a Python project, defaults to Python output).","intents":["I need to generate a quick utility function without writing boilerplate","I want to see example code for a library or API I'm unfamiliar with","I need to scaffold a new module or class structure"],"best_for":["Developers prototyping quickly or learning new languages","Teams generating repetitive boilerplate code","Polyglot developers switching between multiple languages frequently"],"limitations":["Generated code may not follow project-specific conventions or style guides","No syntax validation — generated code requires manual review and testing","Complex algorithms or domain-specific logic may require significant refinement","No dependency resolution — generated code may reference libraries not in the project"],"requires":["OpenAI API key or compatible LLM endpoint","Target programming language specification (or auto-detection from context)"],"input_types":["natural language description of desired code","optional: existing code context for style matching"],"output_types":["code snippet string","formatted code block with syntax highlighting"],"categories":["code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"sgpt__cap_3","uri":"capability://automation.workflow.shell.integration.with.command.substitution.and.piping","name":"shell integration with command substitution and piping","description":"Integrates sgpt output directly into shell pipelines and command substitution contexts, allowing LLM-generated content to feed into other commands or be stored in variables. The tool outputs plain text suitable for shell consumption, enabling patterns like `$(sgpt 'generate a JSON config')` or `sgpt 'list files' | grep pattern`. Integration respects shell quoting and escaping conventions to prevent injection vulnerabilities.","intents":["I want to use LLM output as input to another command in a pipeline","I need to store generated content in a shell variable for further processing","I want to compose LLM-generated commands with traditional Unix tools"],"best_for":["Shell script developers building automation workflows","DevOps engineers composing complex command chains","Users leveraging Unix philosophy of small, composable tools"],"limitations":["Output formatting must be plain text; binary or structured output requires post-processing","Command substitution blocks until LLM response completes; long-running requests may timeout in shell contexts","Escaping and quoting edge cases may require manual handling for special characters","No built-in error handling for failed API calls within pipelines"],"requires":["POSIX-compliant shell (bash, zsh, fish, etc.)","OpenAI API key or compatible LLM endpoint","Proper shell quoting and escaping knowledge"],"input_types":["natural language text","piped input from previous commands (optional)"],"output_types":["plain text suitable for shell consumption","newline-delimited output for iteration"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"sgpt__cap_4","uri":"capability://tool.use.integration.multi.provider.llm.api.abstraction","name":"multi-provider llm api abstraction","description":"Abstracts LLM API interactions to support OpenAI and compatible endpoints (e.g., Azure OpenAI, local Ollama instances, or other OpenAI-compatible APIs). Configuration is managed via environment variables or config files, allowing users to switch providers without code changes. The tool handles API authentication, request formatting, and response parsing transparently across providers.","intents":["I want to use sgpt with my organization's private LLM deployment","I need to switch between OpenAI and a local Ollama instance for cost or privacy reasons","I want to use Azure OpenAI or another OpenAI-compatible provider"],"best_for":["Organizations with private LLM deployments or compliance requirements","Developers experimenting with multiple LLM providers","Teams seeking cost optimization through local or alternative providers"],"limitations":["Only OpenAI-compatible APIs are supported; proprietary APIs (Anthropic, Google, etc.) require separate implementation","Provider-specific features (e.g., vision capabilities, function calling) may not be uniformly supported","Configuration management is manual; no built-in provider discovery or auto-switching","Response format assumptions based on OpenAI API may not hold for all compatible endpoints"],"requires":["OpenAI API key or compatible endpoint URL","Environment variable configuration (OPENAI_API_KEY, OPENAI_API_BASE, etc.)","Compatible LLM provider (OpenAI, Azure OpenAI, Ollama, or similar)"],"input_types":["API endpoint URL (optional, defaults to OpenAI)","API key or authentication credentials"],"output_types":["LLM response text","error messages from provider"],"categories":["tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"sgpt__cap_5","uri":"capability://planning.reasoning.context.aware.prompt.engineering.with.system.instructions","name":"context-aware prompt engineering with system instructions","description":"Constructs LLM prompts with system instructions and context that tailor responses to specific use cases (shell commands, code generation, explanations, etc.). The tool embeds domain-specific prompting strategies that guide the LLM toward generating safe, executable, and relevant output. System prompts are customizable via configuration, allowing users to inject project-specific guidelines or constraints.","intents":["I want generated shell commands to follow my team's security policies","I need code generation to match my project's style and conventions","I want the LLM to prioritize safety and correctness over brevity"],"best_for":["Teams enforcing coding standards or security policies","Projects with domain-specific requirements (e.g., infrastructure-as-code, compliance)","Users seeking consistent output quality across multiple invocations"],"limitations":["System prompt customization requires manual configuration; no UI for prompt tuning","Prompt engineering effectiveness varies with LLM model and version","Complex constraints may conflict with each other or reduce LLM output quality","No built-in A/B testing or evaluation framework for prompt effectiveness"],"requires":["Configuration file or environment variables for custom system prompts","Understanding of prompt engineering principles"],"input_types":["custom system prompt text","user query"],"output_types":["LLM response tailored to system instructions","command or code output"],"categories":["planning-reasoning","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"sgpt__cap_6","uri":"capability://text.generation.language.streaming.response.output.with.real.time.terminal.rendering","name":"streaming response output with real-time terminal rendering","description":"Streams LLM responses token-by-token to the terminal as they arrive, rather than buffering the entire response before display. This provides real-time feedback and reduces perceived latency for long responses. The tool handles terminal rendering, line wrapping, and ANSI color codes to present streamed output cleanly. Streaming is compatible with piping and command substitution, though buffering may occur in those contexts.","intents":["I want to see LLM responses appear in real-time rather than waiting for completion","I need to cancel a long-running response without waiting for it to finish","I want to monitor token generation for debugging or cost tracking"],"best_for":["Interactive terminal users who value responsiveness","Developers debugging LLM behavior or monitoring token usage","Users on high-latency connections where streaming reduces perceived wait time"],"limitations":["Streaming output cannot be easily captured or post-processed mid-stream","Terminal rendering artifacts may occur with very fast token generation","Piping streamed output may buffer entire response, negating latency benefits","No built-in token counting or cost estimation during streaming"],"requires":["Terminal with streaming I/O support (most modern terminals)","Network connection to LLM provider with streaming API support"],"input_types":["user query"],"output_types":["streamed text output to terminal","ANSI-formatted response"],"categories":["text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"sgpt__cap_7","uri":"capability://memory.knowledge.shell.history.integration.and.command.caching","name":"shell history integration and command caching","description":"Integrates with shell history mechanisms (bash history, zsh history, etc.) to track generated commands and enable recall of previous LLM outputs. The tool can optionally cache frequently-used command patterns to reduce API calls and latency. Caching is transparent to the user and respects cache invalidation policies based on time or explicit user action.","intents":["I want to recall a previously generated command without regenerating it","I want to reduce API costs by caching common command patterns","I want to track which commands were LLM-generated vs manually typed"],"best_for":["Power users running repetitive command patterns","Teams optimizing LLM API costs","Users seeking audit trails of LLM-generated commands"],"limitations":["Cache invalidation is manual or time-based; no semantic understanding of when cached commands become stale","Shell history integration is shell-specific; not all shells are equally well-supported","Cache storage is local; no synchronization across machines or sessions","Privacy consideration: cached commands may contain sensitive data (API keys, credentials)"],"requires":["Shell with history support (bash, zsh, fish, etc.)","Local filesystem access for cache storage","Optional: explicit cache configuration"],"input_types":["user query","cache key (implicit or explicit)"],"output_types":["cached command string","newly generated command"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"sgpt__cap_8","uri":"capability://safety.moderation.error.handling.and.safety.guardrails.for.shell.command.execution","name":"error handling and safety guardrails for shell command execution","description":"Implements safety mechanisms to prevent execution of potentially dangerous shell commands, including command review before execution, optional dry-run mode, and warnings for risky patterns (e.g., `rm -rf`, `sudo` commands). The tool provides a confirmation prompt by default, allowing users to review and edit commands before execution. Advanced users can disable guardrails via configuration for trusted workflows.","intents":["I want to review generated commands before executing them to catch errors","I need to prevent accidental execution of destructive commands","I want to test commands in dry-run mode before committing to execution"],"best_for":["Developers new to shell scripting or LLM-assisted command generation","Teams enforcing command review policies","Users working in production environments where mistakes are costly"],"limitations":["Safety checks are heuristic-based; sophisticated attacks or edge cases may bypass them","Confirmation prompts add friction for power users; can be disabled but then safety is lost","Dry-run mode may not accurately simulate command behavior (e.g., for commands with side effects)","No integration with external approval workflows or audit systems"],"requires":["Interactive terminal for confirmation prompts","Optional: configuration to customize safety rules"],"input_types":["generated shell command"],"output_types":["confirmation prompt","command execution result","safety warning"],"categories":["safety-moderation","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"sgpt__cap_9","uri":"capability://tool.use.integration.configuration.management.via.environment.variables.and.config.files","name":"configuration management via environment variables and config files","description":"Manages tool configuration through environment variables (e.g., OPENAI_API_KEY, SGPT_MODEL) and optional YAML/TOML config files, allowing users to customize behavior without CLI flags. Configuration is hierarchical: environment variables override config files, which override defaults. This enables both global system-wide configuration and per-project overrides.","intents":["I want to set up sgpt once and use it consistently across all my projects","I need different configurations for different projects (e.g., different LLM models)","I want to manage sensitive credentials securely via environment variables"],"best_for":["Teams standardizing tool configuration across projects","Users managing multiple LLM providers or models","DevOps engineers automating tool setup in CI/CD pipelines"],"limitations":["Configuration discovery is manual; no built-in config validation or schema checking","No GUI for configuration management; requires manual file editing","Environment variable naming conventions must be memorized or documented","No built-in secrets management; credentials stored in plaintext in config files or environment"],"requires":["Environment variable support in shell","Optional: YAML or TOML config file in standard location (~/.sgpt/config.yaml or similar)"],"input_types":["environment variable key-value pairs","config file in YAML or TOML format"],"output_types":["resolved configuration values","error messages for invalid configuration"],"categories":["tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"sgpt__headline","uri":"capability://tool.use.integration.ai.powered.command.line.productivity.tool","name":"ai-powered command-line productivity tool","description":"A command-line interface tool that leverages large language models to generate shell commands, code, and text from natural language, enhancing productivity for developers and tech enthusiasts.","intents":["best AI command-line tool","command-line tool for generating code","AI tool for shell command generation","productivity tools for developers","best CLI for LLM integration"],"best_for":["developers","system administrators"],"limitations":[],"requires":["OpenAI API or compatible"],"input_types":["natural language"],"output_types":["shell commands","code","text"],"categories":["tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":57,"verified":false,"data_access_risk":"high","permissions":["OpenAI API key or compatible LLM endpoint URL","Shell environment (bash, zsh, fish, or PowerShell)","Network connectivity to LLM provider","Go 1.16+ (for CLI compilation)","OpenAI API key or compatible LLM endpoint","Terminal with ANSI color support (recommended)","Interactive shell environment (bash, zsh, fish, or PowerShell)","Interactive terminal for chat mode","Sufficient API rate limits for multi-turn conversations","Target programming language specification (or auto-detection from context)"],"failure_modes":["LLM-generated commands may contain logical errors or unsafe operations — user review is essential","Requires network access to LLM API; offline operation not supported","Context window limitations mean very complex multi-step workflows may lose detail","Shell-specific syntax variations (bash vs zsh vs fish) require explicit specification","Terminal rendering may truncate or wrap long responses poorly on narrow terminals","Conversation history is session-local; no persistent storage across CLI invocations without explicit export","No built-in markdown rendering — code blocks and formatting appear as raw text","Token usage accumulates with each turn; long conversations may exceed API rate limits or incur high costs","Conversation state is session-local; no persistence across CLI invocations without explicit export","Token usage grows with conversation length; long conversations may exceed API limits or incur high costs","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":0.25,"ecosystem":0.1,"match_graph":0.28,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-06-17T09:51:05.296Z","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=sgpt","compare_url":"https://unfragile.ai/compare?artifact=sgpt"}},"signature":"eMyhI1COq+2khDtJYD7ZCuarxgqqUq2Hd9jpabhveJzST6v6I13K+qRXrKWiNet+EKFW7yF3H6akzePieKUVDA==","signedAt":"2026-06-21T16:58:21.803Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/sgpt","artifact":"https://unfragile.ai/sgpt","verify":"https://unfragile.ai/api/v1/verify?slug=sgpt","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"}}