{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"mods","slug":"mods","name":"Mods","type":"cli","url":"https://github.com/charmbracelet/mods","page_url":"https://unfragile.ai/mods","categories":["cli-tools"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"mods__cap_0","uri":"capability://text.generation.language.unix.pipeline.aware.llm.prompt.injection","name":"unix pipeline-aware llm prompt injection","description":"Mods reads stdin and automatically prefixes it with a user-supplied prompt, enabling seamless piping of CLI output through LLM providers without explicit prompt templating. The system detects TTY vs non-TTY input via isInputTTY() checks to determine whether to use interactive or batch processing modes, allowing the same command to work in both interactive shells and scripted pipelines.","intents":["Pipe command output directly to Claude or GPT-4 for analysis without manual prompt construction","Chain Unix tools with AI analysis in a single pipeline (e.g., `grep error logs.txt | mods 'summarize these errors'`)","Process large batches of text through an LLM in non-interactive mode for automation"],"best_for":["DevOps engineers automating log analysis and system troubleshooting","Data analysts piping structured output through LLMs for insights","Shell script developers integrating AI into existing Unix workflows"],"limitations":["Stdin buffering may cause latency for very large inputs (>10MB) before LLM processing begins","No built-in streaming of stdin to LLM — entire input must be read before request is sent","TTY detection may fail in certain terminal emulators or CI/CD environments with pseudo-terminals"],"requires":["Unix-like shell (bash, zsh, fish, etc.)","Valid API key for at least one LLM provider (OpenAI, Anthropic, Google, Cohere, or Ollama)","Network connectivity for cloud-based providers or local Ollama instance for local models"],"input_types":["plain text from stdin","structured output (JSON, CSV, logs)","binary data (will be read as text, may produce garbled results)"],"output_types":["streamed text response from LLM","formatted terminal output with syntax highlighting"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"mods__cap_1","uri":"capability://tool.use.integration.multi.provider.llm.client.abstraction.with.model.resolution","name":"multi-provider llm client abstraction with model resolution","description":"Mods abstracts five LLM providers (OpenAI, Anthropic, Google, Cohere, Ollama) behind a unified streaming interface. The system resolves model identifiers to provider-specific clients via startCompletionCmd() in mods.go, which determines the correct provider based on Config.API and Config.Model, then initializes the appropriate client with streaming enabled. This allows users to switch providers via a single --api flag without code changes.","intents":["Switch between GPT-4, Claude, and local Ollama models with a single flag (--api openai vs --api anthropic)","Fall back to alternative providers if primary provider is unavailable or rate-limited","Use local Ollama models for privacy-sensitive workloads without changing command syntax"],"best_for":["Teams evaluating multiple LLM providers without rewriting integration code","Organizations with multi-provider contracts seeking to optimize cost per request","Developers building LLM-powered CLI tools who want provider portability"],"limitations":["Provider-specific features (e.g., vision models, function calling) are not abstracted — users must handle provider differences manually","No automatic fallback between providers if one fails; requires manual retry with different --api flag","Streaming response handling assumes text-only output; image/multimodal responses not supported","Model parameter mapping (temperature, top_p, top_k) may not translate 1:1 across providers with different ranges"],"requires":["API keys for desired providers (OPENAI_API_KEY, ANTHROPIC_API_KEY, GOOGLE_API_KEY, COHERE_API_KEY environment variables)","For Ollama: local Ollama instance running on default port 11434 or configured via OLLAMA_HOST","Go runtime for building from source; pre-built binaries available for Linux, macOS, Windows"],"input_types":["model identifier string (e.g., 'gpt-4o', 'claude-3.5-sonnet', 'llama2')","provider name string (openai, anthropic, google, cohere, ollama)"],"output_types":["initialized streaming client object","streamed text tokens from selected provider"],"categories":["tool-use-integration","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"mods__cap_10","uri":"capability://text.generation.language.adaptive.terminal.capability.detection.and.output.formatting","name":"adaptive terminal capability detection and output formatting","description":"Mods detects terminal capabilities (color support, width, Unicode support) at runtime using termenv and lipgloss libraries, then adapts output formatting accordingly. The system checks for 256-color, truecolor, and monochrome support, and adjusts syntax highlighting, markdown rendering, and ANSI codes based on detected capabilities. This ensures output is readable on basic terminals (e.g., SSH sessions, CI/CD logs) while providing rich formatting on capable terminals.","intents":["Display properly formatted output on any terminal, from basic SSH sessions to modern iTerm2","Automatically disable colors in CI/CD pipelines where ANSI codes may cause issues","Render markdown and code with appropriate styling based on terminal capabilities"],"best_for":["Developers working across heterogeneous terminal environments (local, SSH, CI/CD)","Teams with users on older terminals who still need readable output","CI/CD pipelines that need to handle ANSI codes gracefully"],"limitations":["Terminal capability detection may be inaccurate in nested SSH sessions or terminal multiplexers","Color palette detection assumes standard ANSI colors; custom color schemes may produce incorrect results","Unicode support detection may fail on Windows or terminals with limited Unicode support","No override mechanism to force specific color mode; detection is automatic","Terminal width detection may be incorrect in resized windows; output may wrap unexpectedly"],"requires":["termenv library for capability detection (included in mods)","Terminal with ANSI escape sequence support (most modern terminals)"],"input_types":["terminal environment variables (TERM, COLORTERM, etc.)","LLM response text"],"output_types":["formatted output with colors/styles appropriate for terminal","monochrome output for terminals without color support"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"mods__cap_11","uri":"capability://automation.workflow.non.interactive.batch.processing.with.piped.output","name":"non-interactive batch processing with piped output","description":"Mods detects non-TTY input/output (via isInputTTY() and isOutputTTY() checks) and automatically switches to batch processing mode, disabling interactive UI elements and streaming directly to stdout. This allows mods to be used in shell scripts, CI/CD pipelines, and data processing workflows where interactive features would interfere with output capture. The system preserves all LLM functionality while adapting presentation for non-interactive contexts.","intents":["Use mods in shell scripts without interactive prompts or UI elements interfering","Pipe mods output to other commands (e.g., `mods 'summarize' < input.txt | grep important`)","Integrate mods into CI/CD pipelines for automated analysis"],"best_for":["Shell script developers integrating LLM analysis into automation","CI/CD pipeline engineers adding AI-powered analysis to build processes","Data processing workflows that need LLM analysis as a pipeline stage"],"limitations":["Batch mode disables all interactive features (streaming UI, syntax highlighting); output is plain text","No progress indication in batch mode; users don't know if request is processing or stalled","Error handling is less user-friendly; errors are printed to stderr without interactive recovery options","Streaming is still enabled (tokens arrive as they're generated) but rendered as plain text without formatting","No way to force interactive mode when input/output are not TTY (e.g., in tmux)"],"requires":["Non-TTY input and/or output (e.g., piped input, redirected output)","API key for selected provider","Network connectivity to LLM provider"],"input_types":["piped stdin (from file, command output, etc.)","command-line arguments"],"output_types":["plain text response to stdout","error messages to stderr"],"categories":["automation-workflow","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"mods__cap_12","uri":"capability://automation.workflow.terminal.capability.detection.and.adaptive.styling","name":"terminal capability detection and adaptive styling","description":"Detects terminal capabilities (color support, width, height) at startup and adapts rendering accordingly. The system checks for TTY support, terminal color depth, and dimensions, then applies appropriate ANSI styling (colors, bold, underline) based on detected capabilities. If the terminal does not support colors, mods falls back to plain text rendering. Terminal width is used to wrap long lines appropriately.","intents":["I want mods to work correctly in terminals with limited color support","I need output to wrap properly regardless of terminal width","I want to use mods in environments where color codes may cause issues (e.g., log aggregation)"],"best_for":["Users with diverse terminal environments (SSH, containers, CI/CD)","Teams using log aggregation systems that may not handle ANSI codes well","Developers working in minimal terminal environments (e.g., embedded systems)"],"limitations":["Terminal capability detection is static at startup; terminal resizing mid-stream may cause rendering issues","Color support detection may be inaccurate in some terminal emulators","No option to force color output or disable colors; detection is automatic","Styling is limited to ANSI codes; no support for true color (24-bit) or other advanced features"],"requires":["Terminal with TTY support for capability detection","ANSI escape code support for colored output (optional; falls back to plain text)"],"input_types":["terminal capabilities (detected via isatty, TERM env var, etc.)"],"output_types":["rendered output with appropriate styling based on terminal capabilities"],"categories":["automation-workflow","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"mods__cap_13","uri":"capability://memory.knowledge.cache.system.for.repeated.requests.and.response.reuse","name":"cache system for repeated requests and response reuse","description":"Mods implements an internal cache system (referenced in DeepWiki as Cache System) that stores responses to identical requests, enabling response reuse without re-querying the LLM. The cache key is derived from the combined prompt, model, and sampling parameters. When a request matches a cached entry, the cached response is returned immediately without API calls, reducing latency and costs.","intents":["Avoid re-querying the LLM for identical prompts and parameters","Reduce API costs by reusing cached responses","Speed up repeated analysis of the same content","Enable offline operation for cached requests"],"best_for":["Users running repeated analyses on similar content","Teams managing LLM costs and wanting to minimize API calls","Developers testing and debugging with repeated prompts"],"limitations":["Cache is in-memory only; not persisted across invocations","Cache key is based on prompt and parameters; semantic similarity is not detected","No cache invalidation mechanism; stale responses may be returned if LLM behavior changes","Cache size is unbounded; large numbers of unique requests may cause memory issues"],"requires":["identical prompt, model, and sampling parameters for cache hit"],"input_types":["prompt string","model identifier","sampling parameters"],"output_types":["cached response (if available)","fresh LLM response (if cache miss)"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"mods__cap_2","uri":"capability://text.generation.language.real.time.streaming.response.rendering.with.terminal.styling","name":"real-time streaming response rendering with terminal styling","description":"Mods streams LLM responses token-by-token to the terminal using Bubble Tea (charmbracelet's TUI framework) and applies syntax highlighting, markdown formatting, and ANSI color codes based on detected terminal capabilities. The system uses Terminal Capabilities detection (via lipgloss and termenv) to determine color support (256-color, truecolor, or monochrome) and adapts output formatting accordingly, enabling rich formatting on capable terminals while remaining readable on basic ones.","intents":["Display code blocks with syntax highlighting as they stream from the LLM","Render markdown-formatted responses with proper indentation and emphasis in the terminal","Show real-time token generation without buffering entire response, providing immediate feedback"],"best_for":["Interactive terminal users who want immediate visual feedback during LLM generation","Developers reviewing code suggestions who benefit from syntax highlighting","Users on slow connections who want to see partial results as they arrive"],"limitations":["Syntax highlighting only works for common languages; obscure language detection may fail or produce incorrect highlighting","Terminal capability detection may be inaccurate in nested SSH sessions or certain terminal multiplexers (tmux, screen)","Streaming rendering adds ~50-100ms latency per token due to TUI event loop overhead compared to raw stdout writes","ANSI escape sequences may not render correctly in terminals with non-standard color palettes or Windows Terminal with certain configurations"],"requires":["Terminal with ANSI escape sequence support (most modern terminals: iTerm2, Terminal.app, GNOME Terminal, Windows Terminal)","Bubble Tea framework (included in mods binary)","TTY output (isOutputTTY() must return true; piped output disables styling)"],"input_types":["streamed text tokens from LLM provider","markdown-formatted text","code blocks with language hints"],"output_types":["ANSI-formatted terminal output with colors and styles","rendered markdown with proper formatting","syntax-highlighted code blocks"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"mods__cap_3","uri":"capability://memory.knowledge.sqlite.backed.conversation.history.with.message.persistence","name":"sqlite-backed conversation history with message persistence","description":"Mods stores conversation history in a local SQLite database (located at ~/.local/share/mods/mods.db by default) with schema supporting messages, roles (user/assistant), timestamps, and metadata. The Conversation Management system (db.go) allows users to continue previous conversations via the --continue flag, which loads prior message context and appends new user input, enabling multi-turn interactions while maintaining full history for audit and replay purposes.","intents":["Continue a previous conversation without re-explaining context (e.g., `mods --continue 'what about the second approach?'`)","Review conversation history for debugging or documentation purposes","Build context-aware workflows where each mods invocation builds on prior exchanges"],"best_for":["Developers iterating on code reviews or debugging sessions across multiple terminal invocations","Researchers maintaining conversation threads for exploratory analysis","Teams needing audit trails of AI-assisted decisions"],"limitations":["No built-in conversation search or filtering — must manually browse database or use external tools","Conversation context is not automatically pruned; very long conversations may exceed token limits of LLM providers","SQLite database is not encrypted at rest — sensitive conversations are stored in plaintext","No multi-user access control — all conversations stored in single user's home directory","Conversation IDs are auto-generated UUIDs; no human-readable naming or tagging system"],"requires":["SQLite3 (included in most Unix systems; Windows requires separate installation or WSL)","Write access to ~/.local/share/mods/ directory (XDG_DATA_HOME override supported)","Sufficient disk space for conversation storage (typical conversation ~10-50KB)"],"input_types":["conversation ID (UUID or auto-generated)","new user message to append to conversation","--continue flag to load prior context"],"output_types":["loaded conversation history (prior messages + new response)","updated SQLite database with new message entries"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"mods__cap_4","uri":"capability://automation.workflow.configuration.cascade.with.environment.variable.and.file.based.overrides","name":"configuration cascade with environment variable and file-based overrides","description":"Mods implements a four-tier configuration precedence system (embedded defaults < config file < environment variables < CLI flags) managed by ensureConfig() in config.go. Users can set defaults in ~/.config/mods/mods.yml (XDG-compliant), override via MODS_* environment variables, and further override with CLI flags (--model, --api, --temperature, etc.). This allows flexible configuration for different use cases: global defaults in config file, per-session overrides via environment, and per-invocation overrides via flags.","intents":["Set global defaults (e.g., preferred model, API provider) in config file to avoid repeating flags","Override defaults for specific environments (e.g., MODS_API=anthropic in CI/CD pipelines)","Fine-tune parameters per invocation (e.g., `mods --temperature 0.1 'be precise'` for deterministic output)"],"best_for":["Teams with standardized LLM preferences who want to enforce defaults across users","Developers switching between multiple API keys or providers for different projects","CI/CD pipelines needing environment-specific configuration without hardcoding"],"limitations":["No schema validation for config file — invalid YAML will fail silently or produce cryptic errors","Environment variable names are not discoverable; users must consult documentation to know available MODS_* variables","Config file is not auto-generated; users must manually create ~/.config/mods/mods.yml or copy from template","No built-in config validation or dry-run mode to test configuration before use","Precedence order is not explicitly logged; users may be confused why a flag doesn't override an environment variable"],"requires":["XDG_CONFIG_HOME environment variable (defaults to ~/.config if unset)","YAML parser support in Go (gopkg.in/yaml.v3 included in mods)","Write access to config directory for initial setup"],"input_types":["YAML configuration file (mods.yml)","environment variables (MODS_*, OPENAI_API_KEY, etc.)","CLI flags (--model, --api, --temperature, etc.)"],"output_types":["merged Config struct with resolved values","effective configuration used for LLM request"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"mods__cap_5","uri":"capability://text.generation.language.streaming.llm.response.with.provider.agnostic.token.buffering","name":"streaming llm response with provider-agnostic token buffering","description":"Mods abstracts provider-specific streaming APIs (OpenAI's Server-Sent Events, Anthropic's streaming protocol, etc.) into a unified token stream via the Message Stream Context system. The streaming architecture in stream.go buffers tokens from the provider's streaming response, handles provider-specific error codes and disconnections, and yields individual tokens to the UI layer for rendering. This decouples provider implementation details from the rendering pipeline, allowing the same UI code to work with any provider.","intents":["Display LLM responses token-by-token without waiting for full completion","Handle provider-specific streaming protocols transparently without UI code changes","Gracefully handle network interruptions or provider errors during streaming"],"best_for":["Users on slow or unreliable connections who want to see partial results immediately","Developers building provider-agnostic LLM CLI tools","Scenarios where response latency is critical (e.g., real-time code review)"],"limitations":["Streaming adds complexity to error handling — provider errors may occur mid-stream, after partial output has been rendered","Token buffering introduces ~100-200ms latency per token due to event loop processing","No built-in retry logic for interrupted streams — connection loss requires manual re-invocation","Provider rate limits are not pre-checked; streaming may start then fail partway through","Streaming response cannot be easily saved to file without post-processing (ANSI codes included)"],"requires":["Network connectivity to LLM provider","Provider API key with streaming support enabled","TTY output for interactive rendering (non-TTY output falls back to buffered mode)"],"input_types":["provider-specific streaming response (SSE, chunked HTTP, etc.)","user prompt and conversation context"],"output_types":["individual tokens streamed to UI renderer","complete response text after streaming completes"],"categories":["text-generation-language","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"mods__cap_6","uri":"capability://text.generation.language.temperature.and.sampling.parameter.configuration.with.provider.specific.mapping","name":"temperature and sampling parameter configuration with provider-specific mapping","description":"Mods exposes LLM sampling parameters (temperature, top_p, top_k, max_tokens) via CLI flags and config file, then maps these to provider-specific APIs during client initialization. The configuration system stores normalized parameter values in the Config struct (temperature, topP, topK, maxTokens fields), and the provider client initialization code translates these to provider-specific request formats (e.g., OpenAI's temperature vs Anthropic's temperature with different ranges). This allows users to specify sampling parameters once and have them apply across providers.","intents":["Control response creativity/determinism via temperature flag (e.g., `mods --temperature 0.1` for precise output)","Limit response length via max-tokens to control costs or fit within context windows","Fine-tune diversity via top_p and top_k for specific use cases (e.g., creative writing vs code generation)"],"best_for":["Developers optimizing LLM behavior for specific tasks (deterministic code generation vs creative brainstorming)","Cost-conscious teams limiting token usage via max_tokens","Researchers experimenting with different sampling strategies"],"limitations":["Parameter ranges differ across providers (OpenAI temperature 0-2 vs Anthropic 0-1); mods does not validate or normalize ranges","Some providers don't support all parameters (e.g., Ollama may not support top_k); invalid parameters are silently ignored","No guidance on which parameters to use for specific tasks; users must consult provider documentation","Parameter values are not validated before sending to provider; invalid values may cause API errors","No A/B testing or comparison mode to evaluate parameter impact"],"requires":["Understanding of LLM sampling parameters (temperature, top_p, top_k)","Provider documentation for parameter ranges and semantics","API key for selected provider"],"input_types":["temperature (float, typically 0-2)","top_p (float, typically 0-1)","top_k (int, typically 1-100)","max_tokens (int, typically 1-4096)"],"output_types":["provider-specific request with mapped parameters","LLM response generated with specified parameters"],"categories":["text-generation-language","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"mods__cap_7","uri":"capability://text.generation.language.system.prompt.and.role.based.message.formatting","name":"system prompt and role-based message formatting","description":"Mods supports system prompts (via --system flag or config file) that are prepended to user input before sending to the LLM. The message formatting system constructs a message array with role-based structure (system, user, assistant) that is sent to the provider's API. This allows users to define custom system instructions (e.g., 'You are a code reviewer') that shape the LLM's behavior for the entire conversation without modifying the user's input.","intents":["Define custom system instructions for specialized tasks (e.g., 'You are a security auditor, review this code for vulnerabilities')","Maintain consistent behavior across multiple invocations by storing system prompt in config file","Combine system prompt with piped input for context-aware analysis"],"best_for":["Teams with standardized analysis procedures who want consistent LLM behavior","Developers building specialized tools (code reviewers, security auditors, etc.)","Users who want to shape LLM behavior without modifying their input"],"limitations":["System prompt is not validated; overly long or conflicting prompts may produce unexpected results","System prompt is sent with every request, consuming tokens and increasing latency","No built-in system prompt library or templates; users must write custom prompts","System prompt effectiveness varies by model; same prompt may produce different results on different LLMs","No way to dynamically adjust system prompt based on input content"],"requires":["Understanding of prompt engineering and LLM behavior","API key for selected provider","Sufficient token budget to include system prompt in every request"],"input_types":["system prompt string (plain text or multiline)","user input (from stdin or CLI argument)"],"output_types":["message array with system, user, and assistant roles","LLM response shaped by system prompt"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"mods__cap_8","uri":"capability://tool.use.integration.multi.cloud.provider.mcp.tool.integration.for.external.function.calling","name":"multi-cloud provider (mcp) tool integration for external function calling","description":"Mods integrates with the Multi-Cloud Provider (MCP) protocol to enable LLMs to call external tools and functions. The MCP Tool Integration system (referenced in DeepWiki architecture) allows configuration of external tools in the mods config file, which are then made available to the LLM as callable functions. This enables workflows where the LLM can invoke external commands (e.g., shell scripts, APIs) and use their results to refine responses.","intents":["Enable LLM to call external tools (e.g., web search, database queries) during response generation","Build agentic workflows where LLM can take actions and observe results","Integrate mods with existing tool ecosystems via MCP protocol"],"best_for":["Developers building agentic LLM workflows that require external tool access","Teams with existing MCP-compatible tools who want to expose them to LLMs","Researchers exploring LLM agent capabilities"],"limitations":["MCP tool configuration is complex and requires understanding of MCP protocol","Not all LLM providers support function calling; tool integration only works with compatible providers","Tool execution happens on user's machine; no sandboxing or resource limits","Tool errors are not gracefully handled; LLM may produce incorrect results if tool fails","No built-in tool library; users must configure each tool individually"],"requires":["MCP-compatible LLM provider (OpenAI, Anthropic, etc.)","MCP server implementation for external tools","Configuration of MCP tools in mods.yml"],"input_types":["MCP tool configuration (tool name, description, parameters)","user prompt that may trigger tool calls"],"output_types":["LLM response with tool calls embedded","tool execution results integrated into response"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"mods__cap_9","uri":"capability://memory.knowledge.conversation.title.auto.generation.and.management","name":"conversation title auto-generation and management","description":"Mods can automatically generate conversation titles via the --title flag, which creates a human-readable label for stored conversations. The title is stored in the SQLite database alongside conversation messages, enabling users to organize and identify conversations without relying on auto-generated UUIDs. This allows conversations to be referenced by meaningful names rather than opaque identifiers.","intents":["Organize conversations with human-readable titles for easy identification","Reference conversations by name instead of UUID (e.g., `mods --continue 'code-review-session'`)","Build conversation management workflows with meaningful conversation labels"],"best_for":["Users maintaining multiple concurrent conversations who need to track context","Teams sharing conversation history where meaningful titles aid understanding","Developers building conversation management UIs"],"limitations":["Title generation is manual via --title flag; no automatic title generation from conversation content","No title search or filtering; users must remember exact titles to reference conversations","Titles are not unique; multiple conversations can have the same title, causing ambiguity","No title editing after conversation creation; must delete and recreate to change title","Title length is not validated; very long titles may cause display issues"],"requires":["SQLite database for conversation storage","User-provided title string via --title flag"],"input_types":["conversation title string (user-provided)"],"output_types":["stored conversation with title in database","conversation retrievable by title"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"mods__headline","uri":"capability://tool.use.integration.ai.command.line.interface.for.llm.integration","name":"ai command-line interface for llm integration","description":"Mods is a command-line tool that seamlessly integrates Large Language Models into terminal workflows, allowing users to analyze CLI output with AI models like GPT-4 and Claude.","intents":["best AI CLI tool","AI integration for command line","command line tool for LLMs","CLI tool to analyze output with AI","best tool for AI in terminal workflows"],"best_for":[],"limitations":[],"requires":[],"input_types":[],"output_types":[],"categories":["tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":68,"verified":false,"data_access_risk":"high","permissions":["Unix-like shell (bash, zsh, fish, etc.)","Valid API key for at least one LLM provider (OpenAI, Anthropic, Google, Cohere, or Ollama)","Network connectivity for cloud-based providers or local Ollama instance for local models","API keys for desired providers (OPENAI_API_KEY, ANTHROPIC_API_KEY, GOOGLE_API_KEY, COHERE_API_KEY environment variables)","For Ollama: local Ollama instance running on default port 11434 or configured via OLLAMA_HOST","Go runtime for building from source; pre-built binaries available for Linux, macOS, Windows","termenv library for capability detection (included in mods)","Terminal with ANSI escape sequence support (most modern terminals)","Non-TTY input and/or output (e.g., piped input, redirected output)","API key for selected provider"],"failure_modes":["Stdin buffering may cause latency for very large inputs (>10MB) before LLM processing begins","No built-in streaming of stdin to LLM — entire input must be read before request is sent","TTY detection may fail in certain terminal emulators or CI/CD environments with pseudo-terminals","Provider-specific features (e.g., vision models, function calling) are not abstracted — users must handle provider differences manually","No automatic fallback between providers if one fails; requires manual retry with different --api flag","Streaming response handling assumes text-only output; image/multimodal responses not supported","Model parameter mapping (temperature, top_p, top_k) may not translate 1:1 across providers with different ranges","Terminal capability detection may be inaccurate in nested SSH sessions or terminal multiplexers","Color palette detection assumes standard ANSI colors; custom color schemes may produce incorrect results","Unicode support detection may fail on Windows or terminals with limited Unicode support","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":"2026-06-17T09:51:04.693Z","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=mods","compare_url":"https://unfragile.ai/compare?artifact=mods"}},"signature":"R2Te2vmf4kKCf9jZjd+6a5sU5OD/LTAHsX5DZWzeJiL5NOPqfXH+EdqvBXusMAYBp6L4k/B9jjQg9jm2AyzbCA==","signedAt":"2026-06-22T07:49:48.793Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/mods","artifact":"https://unfragile.ai/mods","verify":"https://unfragile.ai/api/v1/verify?slug=mods","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"}}