Mods
CLI ToolFreePipe CLI output through AI models.
Capabilities14 decomposed
unix pipeline-aware llm prompt injection
Medium confidenceMods 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.
Implements dual-mode input handling (TTY vs non-TTY) via isInputTTY()/isOutputTTY() checks in main.go, allowing the same binary to function as both an interactive REPL and a batch pipeline component without mode flags — most LLM CLIs require explicit flags to switch modes
Simpler than shell wrapper scripts around API calls because it natively understands Unix conventions; more composable than web-based LLM interfaces because it respects stdin/stdout/stderr semantics
multi-provider llm client abstraction with model resolution
Medium confidenceMods 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.
Implements provider resolution in startCompletionCmd() (mods.go 276-454) with a unified streaming interface that abstracts provider-specific client initialization, allowing Config.API to determine provider at runtime rather than compile-time — most LLM CLIs hardcode a single provider
More flexible than LangChain's provider abstraction because it's CLI-first and doesn't require Python/JavaScript; lighter weight than Ollama's web UI because it's a single binary with no server overhead
adaptive terminal capability detection and output formatting
Medium confidenceMods 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.
Implements runtime terminal capability detection via termenv/lipgloss with adaptive output formatting based on detected color support (256-color, truecolor, monochrome) — most LLM CLIs either hardcode ANSI codes or provide no color support
More robust than hardcoded ANSI codes because it adapts to terminal capabilities; more user-friendly than --no-color flags because detection is automatic
non-interactive batch processing with piped output
Medium confidenceMods 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.
Implements automatic TTY detection via isInputTTY()/isOutputTTY() to switch between interactive and batch modes without explicit flags, disabling UI elements in non-TTY contexts — most LLM CLIs require explicit flags to disable interactive features
More seamless than flag-based mode switching because detection is automatic; more compatible with Unix pipelines because it respects TTY conventions
terminal capability detection and adaptive styling
Medium confidenceDetects 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.
Automatically detects terminal capabilities and adapts styling without user configuration, ensuring mods works correctly across diverse terminal environments
More user-friendly than requiring manual color configuration and more robust than assuming all terminals support colors; automatic detection eliminates configuration burden
cache system for repeated requests and response reuse
Medium confidenceMods 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.
Implements in-memory response caching based on prompt and parameter hash, enabling response reuse for identical requests without API calls. The cache is transparent to users and requires no configuration.
Reduces API costs and latency for repeated requests without user configuration; most LLM CLIs don't implement caching, requiring users to manually manage response reuse.
real-time streaming response rendering with terminal styling
Medium confidenceMods 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.
Uses Bubble Tea's event-driven model combined with termenv for terminal capability detection to render streaming responses with adaptive styling — most LLM CLIs either buffer entire responses before rendering or use basic printf-style output without capability detection
More responsive than web-based LLM interfaces because rendering happens locally without network round-trips; more sophisticated than curl-based API calls because it handles terminal capabilities and markdown formatting automatically
sqlite-backed conversation history with message persistence
Medium confidenceMods 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.
Implements conversation persistence via SQLite with automatic schema management in db.go, storing full message history with timestamps and roles, enabling --continue flag to load prior context without re-sending entire conversation to LLM — most LLM CLIs either discard history after each invocation or require manual context management
More durable than in-memory conversation buffers because data survives process restarts; more lightweight than full chat applications because it uses embedded SQLite rather than external databases
configuration cascade with environment variable and file-based overrides
Medium confidenceMods 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.
Implements explicit four-tier precedence cascade (embedded template < file < env < flags) in config.go with XDG Base Directory compliance, allowing configuration to be specified at multiple levels with clear override semantics — most LLM CLIs either hardcode defaults or require all configuration via flags
More flexible than single-source configuration because it supports multiple override mechanisms; more discoverable than environment-only configuration because defaults are visible in config file
streaming llm response with provider-agnostic token buffering
Medium confidenceMods 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.
Implements provider-agnostic token streaming via Message Stream Context abstraction in stream.go, buffering provider-specific streaming responses into a unified token channel that decouples provider implementation from rendering — most LLM CLIs either hardcode a single provider's streaming protocol or buffer entire responses before rendering
More responsive than buffered responses because tokens appear immediately; more maintainable than provider-specific streaming code because provider changes don't affect UI layer
temperature and sampling parameter configuration with provider-specific mapping
Medium confidenceMods 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.
Stores normalized sampling parameters in Config struct (temperature, topP, topK, maxTokens) and maps them to provider-specific APIs during client initialization, allowing single parameter specification to work across providers despite different ranges and semantics — most LLM CLIs either hardcode parameters or require provider-specific syntax
More user-friendly than provider-specific parameter syntax because it abstracts differences; more flexible than fixed defaults because it allows per-invocation tuning
system prompt and role-based message formatting
Medium confidenceMods 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.
Implements system prompt support via --system flag and config file integration, prepending system instructions to user input in message array sent to provider — most LLM CLIs either don't support system prompts or require manual message construction
More convenient than manual message construction because system prompt is stored in config; more flexible than hardcoded system prompts because it can be overridden per invocation
multi-cloud provider (mcp) tool integration for external function calling
Medium confidenceMods 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.
Implements MCP protocol integration for external tool calling, allowing LLMs to invoke configured tools and use results in responses — most LLM CLIs don't support tool calling or require provider-specific function calling syntax
More flexible than hardcoded tool support because it uses standard MCP protocol; more powerful than simple command piping because LLM can conditionally invoke tools based on context
conversation title auto-generation and management
Medium confidenceMods 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.
Implements conversation titling via --title flag with storage in SQLite, enabling human-readable conversation references — most LLM CLIs either don't support conversation persistence or use opaque identifiers
More user-friendly than UUID-based conversation references because titles are memorable; simpler than full conversation search because it relies on exact title matching
Capabilities are decomposed by AI analysis. Each maps to specific user intents and improves with match feedback.
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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
- ✓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
- ✓Developers working across heterogeneous terminal environments (local, SSH, CI/CD)
- ✓Teams with users on older terminals who still need readable output
Known 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
- ⚠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
Requirements
Input / Output
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About
AI on the command line by Charm. Mods works by reading standard input and prefixing it with a prompt, letting you pipe any CLI output through GPT-4, Claude, or local models for analysis.
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