{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"awesome-consult7","slug":"consult7","name":"consult7","type":"cli","url":"https://github.com/szeider/consult7","page_url":"https://unfragile.ai/consult7","categories":["documentation"],"tags":[],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"awesome-consult7__cap_0","uri":"capability://tool.use.integration.multi.provider.llm.abstraction.with.unified.interface","name":"multi-provider llm abstraction with unified interface","description":"Implements a BaseProvider interface abstraction layer that unifies three distinct LLM providers (OpenRouter, Google AI, OpenAI) into a single consultation API. Each provider implements provider-specific features (OpenRouter's reasoning mode with |thinking suffix, Google's native ThinkingConfig, OpenAI's O-series effort-based thinking) while exposing a common interface through the PROVIDERS registry in providers/__init__.py. This enables seamless switching between providers and models without changing client code.","intents":["Switch between different LLM providers without rewriting consultation logic","Access provider-specific reasoning modes (O-series, Gemini thinking, OpenRouter reasoning) through a unified API","Route analysis requests to the cheapest or fastest available provider at runtime","Support multiple models with different context windows (up to 1M+ tokens) through a single interface"],"best_for":["Teams building LLM-powered analysis tools that need provider flexibility","Developers wanting to leverage reasoning models without provider lock-in","Organizations comparing LLM provider costs and performance"],"limitations":["Provider-specific features (thinking modes, reasoning) require explicit configuration per provider","Token counting accuracy varies by provider; estimates may differ from actual usage","No automatic fallback if primary provider fails — requires explicit error handling"],"requires":["API keys for at least one of: OpenRouter, Google AI, or OpenAI","Python 3.9+","MCP client implementation to invoke tools"],"input_types":["provider name (string)","model identifier (string)","file collection (list of absolute paths)","analysis query (text)"],"output_types":["LLM response text","thinking/reasoning tokens (if enabled)","token usage metadata"],"categories":["tool-use-integration","provider-abstraction"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-consult7__cap_1","uri":"capability://data.processing.analysis.intelligent.file.collection.with.pattern.expansion.and.safety.limits","name":"intelligent file collection with pattern expansion and safety limits","description":"Processes file collections through file_processor.py with automatic wildcard pattern expansion (e.g., *.py, *_test.js), enforces safety limits (1MB per file, 4MB total collection), and auto-ignores sensitive paths (__pycache__, .env, secrets.py, .DS_Store, .git, node_modules). Requires absolute file paths starting with / for security. This enables safe, scalable analysis of large codebases without manual file enumeration or accidental inclusion of secrets.","intents":["Analyze entire directories of code without manually listing every file","Prevent accidental inclusion of secrets, cache directories, or build artifacts in analysis","Ensure file collections fit within model context windows by enforcing size limits","Support flexible file selection patterns (e.g., all Python files, all test files)"],"best_for":["Developers analyzing large codebases (100+ files) with a single query","Teams with strict security requirements around secret exposure","CI/CD pipelines that need deterministic file collection without manual configuration"],"limitations":["1MB per-file limit excludes large generated files, binaries, or data files","4MB total collection limit constrains analysis to ~1M token context windows; larger codebases require multiple queries","Absolute path requirement (/path/to/file) makes relative path usage impossible","Auto-ignore list is fixed; no per-query customization of ignored patterns"],"requires":["Absolute file paths (must start with /)","File system access to read specified paths","Python 3.9+"],"input_types":["file path patterns (strings with wildcards)","absolute directory paths"],"output_types":["formatted file collection (text with file contents)","collection metadata (file count, total size, token estimate)"],"categories":["data-processing-analysis","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-consult7__cap_2","uri":"capability://data.processing.analysis.context.aware.token.management.with.model.specific.limits","name":"context-aware token management with model-specific limits","description":"Implements token_utils.py for pre-request token counting via estimate_tokens(), validates content fits within model context limits, manages thinking token budgets for reasoning modes, and dynamically retrieves context length from model metadata. This enables safe analysis of large files without exceeding model limits or wasting thinking tokens on models that don't support reasoning.","intents":["Estimate token usage before sending requests to avoid exceeding context limits","Allocate thinking tokens intelligently for reasoning models (O-series, Gemini thinking)","Validate that file collections fit within a model's context window before analysis","Dynamically adjust analysis strategy based on available context for different models"],"best_for":["Cost-conscious teams optimizing token usage across multiple models","Developers building adaptive analysis pipelines that adjust to model capabilities","Teams using reasoning models that require explicit thinking token budgets"],"limitations":["Token estimation is approximate; actual usage may differ by 5-15% due to tokenizer differences","Thinking token budget allocation is heuristic-based; no guarantee of optimal reasoning allocation","Context length metadata must be manually maintained for new models; no automatic discovery","No support for dynamic context window reduction if content exceeds limits — requires explicit query reformulation"],"requires":["Model identifier that maps to known context length in model metadata","Provider API key for token counting (if using provider-native tokenizers)","Python 3.9+"],"input_types":["text content (code, documents)","model identifier (string)","thinking mode flag (boolean)"],"output_types":["token count estimate (integer)","context utilization percentage (float)","thinking token budget recommendation (integer)"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-consult7__cap_3","uri":"capability://tool.use.integration.mcp.server.protocol.implementation.with.tool.based.consultation.interface","name":"mcp server protocol implementation with tool-based consultation interface","description":"Implements a Model Context Protocol (MCP) server (src/consult7/server.py) that exposes consultation capabilities as MCP tools, enabling AI agents (like Claude) to invoke file analysis through standardized tool calls. The server handles MCP protocol marshalling, tool registration, and request routing to the consultation engine. This allows Claude and other MCP-compatible agents to analyze codebases as a native capability without custom integrations.","intents":["Enable Claude and other MCP-compatible agents to analyze large codebases natively","Expose file analysis as a standard MCP tool that agents can invoke autonomously","Bridge limited-context agents (200K tokens) with large-context models (1M+ tokens) for comprehensive analysis","Allow agents to request analysis without leaving the MCP protocol context"],"best_for":["Teams using Claude with MCP for autonomous codebase analysis","Developers building agent systems that need external analysis capabilities","Organizations wanting to extend Claude's capabilities with custom analysis tools"],"limitations":["MCP protocol overhead adds latency to each consultation request","Tool invocation is asynchronous; no streaming responses within MCP protocol","Requires MCP-compatible client (Claude, custom agents); not usable with standard REST APIs","Tool definitions must be manually maintained in sync with consultation engine changes"],"requires":["MCP client implementation (Claude, custom agent, or MCP SDK)","Python 3.9+","MCP server running and accessible to client"],"input_types":["MCP tool call with file paths and analysis query"],"output_types":["MCP tool result with analysis response","error messages (if validation fails)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-consult7__cap_4","uri":"capability://planning.reasoning.reasoning.mode.consultation.with.provider.specific.thinking.support","name":"reasoning-mode consultation with provider-specific thinking support","description":"Enables analysis queries to leverage provider-specific reasoning modes: OpenRouter's |thinking suffix for extended reasoning, Google's native ThinkingConfig for Gemini models, and OpenAI's effort-based thinking for O-series models. The consultation engine routes reasoning requests to the appropriate provider and manages thinking token allocation. This allows complex codebase analysis to benefit from extended reasoning without manual prompt engineering.","intents":["Perform deep architectural analysis of codebases using extended reasoning","Leverage O-series reasoning models for complex code understanding tasks","Use Gemini thinking mode for structured analysis of large document collections","Allocate reasoning tokens intelligently based on query complexity"],"best_for":["Teams analyzing complex architectures that benefit from extended reasoning","Developers using O-series or Gemini reasoning models for code understanding","Organizations willing to pay for reasoning token usage for critical analysis"],"limitations":["Reasoning modes significantly increase latency (5-30s+ depending on thinking budget)","Thinking token costs are 5-10x higher than standard tokens; budget carefully","Not all providers support reasoning equally; OpenRouter reasoning is less mature than native O-series","Thinking output is opaque; no visibility into reasoning process, only final answer"],"requires":["API key for provider supporting reasoning (OpenAI O-series, Google Gemini, or OpenRouter)","Sufficient API quota for reasoning token usage","Python 3.9+"],"input_types":["analysis query (text)","file collection (code/documents)","reasoning mode flag (boolean)","thinking budget (optional integer for token allocation)"],"output_types":["analysis response (text)","thinking tokens used (integer, if reasoning enabled)","reasoning metadata (provider-specific)"],"categories":["planning-reasoning","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-consult7__cap_5","uri":"capability://data.processing.analysis.large.context.codebase.analysis.with.1m.token.models","name":"large-context codebase analysis with 1m+ token models","description":"Enables analysis of large codebases by leveraging models with 1M+ token context windows (Google Gemini 2.5 Pro, OpenAI GPT-5 400K, OpenRouter Claude Sonnet 4). The consultation engine formats file collections into a single context window and routes to appropriate high-context models. This allows comprehensive codebase analysis in a single query without chunking or multiple round-trips.","intents":["Analyze entire large codebases (10K+ lines) in a single query without chunking","Understand cross-file dependencies and architecture in one pass","Generate comprehensive documentation from full codebase context","Perform whole-codebase refactoring analysis without losing context"],"best_for":["Teams analyzing large monorepos or complex architectures","Developers generating comprehensive codebase documentation","Organizations with budgets for high-context model usage"],"limitations":["4MB total file collection limit constrains analysis to ~1M tokens; very large codebases still require chunking","High-context models are expensive; 1M token queries cost 10-100x more than standard models","Latency increases with context size; 1M token queries may take 30-60s","Not all models support 1M+ context equally; some have reduced performance at maximum context"],"requires":["API key for high-context model provider (Google, OpenAI, or OpenRouter)","Sufficient API quota and budget for large token usage","Python 3.9+"],"input_types":["file collection (code files, up to 4MB total)","analysis query (text)"],"output_types":["comprehensive analysis response (text)","token usage metadata (input/output tokens)"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-consult7__cap_6","uri":"capability://text.generation.language.documentation.generation.from.code.and.document.collections","name":"documentation generation from code and document collections","description":"Analyzes code and document collections to generate comprehensive documentation by leveraging large-context models. The consultation engine accepts file collections and documentation queries, formats them into a single context, and returns structured documentation output. This enables automated documentation generation that understands full codebase context rather than isolated files.","intents":["Generate comprehensive API documentation from source code","Create architecture documentation from codebase analysis","Produce migration guides by analyzing old and new code versions","Generate README and getting-started guides from codebase structure"],"best_for":["Teams maintaining large codebases with outdated documentation","Open-source projects needing comprehensive documentation at scale","Organizations automating documentation generation in CI/CD pipelines"],"limitations":["Generated documentation requires human review; LLM-generated content may contain inaccuracies","Documentation quality depends on code quality and clarity; poorly documented code produces poor documentation","No built-in version control or change tracking for documentation; requires external management","Documentation generation is expensive at scale; 1M token queries cost significantly"],"requires":["API key for high-context model provider","Code or document files to analyze (up to 4MB total)","Python 3.9+"],"input_types":["code files (Python, JavaScript, Go, etc.)","existing documentation (Markdown, RST)","documentation query or template (text)"],"output_types":["generated documentation (Markdown, text)","structured documentation sections (if query specifies format)"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-consult7__cap_7","uri":"capability://safety.moderation.secure.file.collection.with.secret.prevention.and.path.validation","name":"secure file collection with secret prevention and path validation","description":"Enforces security constraints on file collection through absolute path requirements (/path/to/file), auto-ignores sensitive paths (.env, secrets.py, .git, node_modules), and validates file access permissions before inclusion. This prevents accidental exposure of API keys, credentials, or private configuration in analysis requests sent to external LLM providers.","intents":["Analyze codebases without accidentally exposing secrets or credentials","Prevent inclusion of .env files, private keys, or configuration in analysis","Ensure file collections are safe for external LLM provider analysis","Validate file access permissions before sending to external services"],"best_for":["Teams analyzing production codebases with strict security requirements","Organizations with compliance requirements (SOC 2, ISO 27001) around secret exposure","CI/CD pipelines that need deterministic, secure file collection"],"limitations":["Auto-ignore list is fixed; no per-query customization of ignored patterns","Absolute path requirement prevents relative path usage, complicating portable scripts","No encryption of file contents in transit; relies on HTTPS for transport security","No audit logging of which files were analyzed; requires external logging for compliance"],"requires":["Absolute file paths (must start with /)","File system access to validate paths","Python 3.9+"],"input_types":["absolute file paths (strings starting with /)","file path patterns (wildcards)"],"output_types":["validated file collection (text with contents)","security validation metadata (ignored files, excluded patterns)"],"categories":["safety-moderation","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-consult7__cap_8","uri":"capability://data.processing.analysis.provider.cost.and.performance.comparison.for.analysis.queries","name":"provider cost and performance comparison for analysis queries","description":"Enables comparison of different providers and models for the same analysis query by routing requests to multiple providers and returning cost/performance metadata (token usage, latency, pricing). The provider abstraction layer tracks token counts and execution time per provider, allowing users to make informed decisions about provider selection based on cost-benefit trade-offs.","intents":["Compare costs across OpenRouter, Google AI, and OpenAI for the same analysis","Benchmark latency and quality differences between providers","Select the most cost-effective provider for recurring analysis queries","Optimize provider selection based on model capabilities and pricing"],"best_for":["Cost-conscious teams analyzing large codebases regularly","Organizations evaluating LLM providers for long-term use","Teams with flexible provider requirements wanting to optimize spend"],"limitations":["Comparison requires multiple API calls; adds latency and cost for benchmarking","Pricing data must be manually maintained; no automatic price discovery","Quality comparison is subjective; latency and token usage don't capture output quality","Provider availability varies; some models may be unavailable or rate-limited"],"requires":["API keys for multiple providers (OpenRouter, Google AI, OpenAI)","Sufficient API quota for comparison queries","Python 3.9+"],"input_types":["analysis query (text)","file collection (code/documents)","list of providers to compare (strings)"],"output_types":["comparison results (structured data with cost/latency/tokens per provider)","provider recommendation (based on cost or performance)"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-consult7__cap_9","uri":"capability://planning.reasoning.adaptive.analysis.strategy.based.on.model.capabilities","name":"adaptive analysis strategy based on model capabilities","description":"Dynamically adjusts analysis approach based on model capabilities: uses reasoning modes for O-series and Gemini models, allocates thinking tokens intelligently, and selects appropriate context window sizes. The consultation engine inspects model metadata and provider features to determine optimal analysis strategy without user configuration.","intents":["Automatically leverage reasoning models when available for complex analysis","Adjust analysis depth based on model capabilities (reasoning vs. standard)","Optimize token allocation for different model types without manual tuning","Enable graceful degradation if high-context models are unavailable"],"best_for":["Teams wanting to leverage advanced model features without manual configuration","Developers building adaptive analysis pipelines","Organizations with heterogeneous model deployments"],"limitations":["Adaptive strategy selection is heuristic-based; may not match user preferences","No user override mechanism; adaptive decisions are opaque and non-configurable","Model metadata must be manually maintained; no automatic capability discovery","Reasoning mode selection increases latency and cost; no cost-aware adaptation"],"requires":["Model metadata with capability information (reasoning support, context window)","API keys for multiple providers","Python 3.9+"],"input_types":["analysis query (text)","file collection (code/documents)","model identifier (optional; auto-selected if not provided)"],"output_types":["analysis response (text)","strategy metadata (which model/reasoning mode was used)"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":30,"verified":false,"data_access_risk":"high","permissions":["API keys for at least one of: OpenRouter, Google AI, or OpenAI","Python 3.9+","MCP client implementation to invoke tools","Absolute file paths (must start with /)","File system access to read specified paths","Model identifier that maps to known context length in model metadata","Provider API key for token counting (if using provider-native tokenizers)","MCP client implementation (Claude, custom agent, or MCP SDK)","MCP server running and accessible to client","API key for provider supporting reasoning (OpenAI O-series, Google Gemini, or OpenRouter)"],"failure_modes":["Provider-specific features (thinking modes, reasoning) require explicit configuration per provider","Token counting accuracy varies by provider; estimates may differ from actual usage","No automatic fallback if primary provider fails — requires explicit error handling","1MB per-file limit excludes large generated files, binaries, or data files","4MB total collection limit constrains analysis to ~1M token context windows; larger codebases require multiple queries","Absolute path requirement (/path/to/file) makes relative path usage impossible","Auto-ignore list is fixed; no per-query customization of ignored patterns","Token estimation is approximate; actual usage may differ by 5-15% due to tokenizer differences","Thinking token budget allocation is heuristic-based; no guarantee of optimal reasoning allocation","Context length metadata must be manually maintained for new models; no automatic discovery","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.45,"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":"2026-06-17T09:51:03.036Z","last_scraped_at":"2026-05-03T14:00:15.503Z","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=consult7","compare_url":"https://unfragile.ai/compare?artifact=consult7"}},"signature":"+ouz7y963Y9Sp+u3aiMzz1IJX251DdetI1yDJrSsq4zYdYxgSNhagl125thz8RWVtIBGv3JLvCPdQylRK7+KCw==","signedAt":"2026-07-08T14:35:08.874Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/consult7","artifact":"https://unfragile.ai/consult7","verify":"https://unfragile.ai/api/v1/verify?slug=consult7","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"}}