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The agent indexes file paths and content, tracks conversation history, and routes queries to the appropriate files based on semantic understanding of developer intent.","intents":["Ask questions about how a specific function works across multiple files","Get explanations of code patterns and architectural decisions in context","Understand dependencies and relationships between modules without manual exploration","Discuss refactoring options while the agent references actual code"],"best_for":["Solo developers onboarding into unfamiliar codebases","Teams conducting code reviews with AI assistance","Developers learning legacy systems through interactive dialogue"],"limitations":["Context window limits prevent analyzing very large codebases in single conversation","Requires explicit file/directory specification — no automatic intelligent file selection","Conversation history grows linearly; no automatic summarization or context pruning","File changes during conversation are not automatically detected or refreshed"],"requires":["Node.js 14+ or Python 3.8+","API key for OpenAI, Anthropic, or compatible LLM provider","Local file system access with read permissions","Network connectivity for LLM API calls"],"input_types":["natural language queries","file paths","directory paths","code snippets for context"],"output_types":["natural language explanations","code suggestions","architectural diagrams (text-based)","refactoring recommendations"],"categories":["planning-reasoning","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-gpt-runner__cap_1","uri":"capability://code.generation.editing.batch.file.processing.with.llm.transformation","name":"batch file processing with llm transformation","description":"Processes multiple files in sequence through an LLM pipeline, applying consistent transformations, analyses, or generations across a codebase. The agent reads each file, sends it to the LLM with a specified prompt template, and writes results back to the filesystem or collects them for review, enabling bulk code refactoring, documentation generation, or linting-style operations at scale.","intents":["Generate docstrings or comments for all functions in a codebase","Apply consistent code style transformations across multiple files","Extract metadata or patterns from many files into a summary report","Batch-translate code comments from one language to another"],"best_for":["Teams automating documentation generation across large codebases","Developers performing bulk refactoring operations","Projects needing consistent code quality improvements across files"],"limitations":["No built-in deduplication — processes each file independently, missing cross-file optimization opportunities","Requires manual specification of which files to process; no intelligent filtering","No rollback mechanism if transformations produce errors across multiple files","API costs scale linearly with file count; no batching optimization for similar files"],"requires":["Node.js 14+ or Python 3.8+","LLM API key with sufficient quota for batch operations","Write permissions to target directories","Sufficient disk space for output files"],"input_types":["file paths (glob patterns or explicit lists)","prompt templates","configuration files"],"output_types":["transformed code files","summary reports","structured data (JSON, CSV)"],"categories":["code-generation-editing","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-gpt-runner__cap_2","uri":"capability://memory.knowledge.conversation.history.persistence.and.resumption","name":"conversation history persistence and resumption","description":"Automatically saves multi-turn conversations with file context to disk, allowing developers to pause analysis and resume later without losing dialogue history or re-establishing context. The agent serializes conversation state (messages, file references, LLM responses) to a structured format and reconstructs the full context when a session is reopened, maintaining semantic continuity across sessions.","intents":["Resume a code analysis session the next day without re-explaining the codebase","Share conversation history with team members for collaborative code review","Archive conversations as documentation of architectural decisions","Iterate on code understanding over multiple sessions without context loss"],"best_for":["Teams conducting long-running code audits or migrations","Developers working on complex systems requiring multiple analysis sessions","Organizations needing audit trails of AI-assisted code decisions"],"limitations":["Serialized state can become large for long conversations; no automatic compression","No built-in versioning — overwriting conversations loses previous versions","File changes since last session are not automatically detected; manual refresh required","Conversation files are plain text/JSON; no encryption for sensitive code discussions"],"requires":["Local filesystem with write permissions","Sufficient disk space for conversation logs","JSON or YAML parser support in the runtime"],"input_types":["conversation session identifiers","file paths for context"],"output_types":["conversation history files (JSON/YAML)","serialized context state"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-gpt-runner__cap_3","uri":"capability://tool.use.integration.multi.provider.llm.abstraction.with.unified.interface","name":"multi-provider llm abstraction with unified interface","description":"Provides a unified API for interacting with multiple LLM providers (OpenAI, Anthropic, local models via Ollama, etc.) without changing application code. The agent abstracts provider-specific API differences (authentication, request/response formats, parameter names) behind a common interface, allowing developers to swap providers or use multiple providers in parallel by changing configuration.","intents":["Switch from OpenAI to Anthropic without rewriting conversation logic","Use local Ollama models for privacy-sensitive code analysis","Compare responses from multiple LLM providers for the same query","Fallback to a secondary provider if the primary provider is unavailable"],"best_for":["Teams evaluating multiple LLM providers for cost/quality tradeoffs","Organizations with privacy requirements preferring local models","Developers building LLM applications that need provider flexibility"],"limitations":["Abstraction doesn't normalize model capabilities — advanced features (vision, function calling) vary by provider","No automatic prompt optimization for different model architectures","Latency differences between providers not abstracted; slower providers block conversation flow","Cost tracking and billing integration not built-in; requires manual accounting"],"requires":["API keys for at least one LLM provider","Network connectivity for cloud providers or local Ollama instance","Configuration file specifying provider credentials"],"input_types":["provider configuration (API keys, endpoints)","prompts and messages"],"output_types":["LLM responses","provider metadata (model name, tokens used)"],"categories":["tool-use-integration","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-gpt-runner__cap_4","uri":"capability://text.generation.language.streaming.response.output.with.real.time.feedback","name":"streaming response output with real-time feedback","description":"Streams LLM responses token-by-token to the user interface or console as they are generated, rather than waiting for the complete response. The agent pipes the LLM's streaming output directly to the output stream, providing immediate feedback and reducing perceived latency for long-running analyses or code generation tasks.","intents":["See LLM responses appear in real-time instead of waiting for full completion","Cancel long-running queries early if the initial tokens indicate wrong direction","Monitor progress of batch file processing with live output","Reduce perceived latency for interactive code analysis sessions"],"best_for":["Interactive CLI tools where user experience depends on responsiveness","Long-running batch operations where progress visibility matters","Developers debugging LLM behavior by observing token generation"],"limitations":["Streaming prevents post-processing the full response before display","No built-in buffering for structured output (JSON, code) — partial tokens may be invalid","Terminal rendering of streaming output can be garbled with concurrent operations","Cancellation mid-stream may leave incomplete output in files or logs"],"requires":["LLM provider with streaming API support (OpenAI, Anthropic, Ollama)","Output stream that supports real-time writes (console, file, WebSocket)"],"input_types":["prompts and messages"],"output_types":["streamed text tokens","real-time console output"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-gpt-runner__cap_5","uri":"capability://text.generation.language.prompt.template.system.with.variable.substitution","name":"prompt template system with variable substitution","description":"Provides a templating engine for defining reusable prompts with placeholders for dynamic values (file paths, code snippets, user queries). The agent substitutes variables at runtime before sending prompts to the LLM, enabling consistent prompt engineering across multiple queries and batch operations without hardcoding values.","intents":["Define a docstring generation template once and apply it to many functions","Create parameterized prompts that adapt to different file types or languages","Reuse prompt templates across team members without duplicating logic","A/B test different prompt phrasings by swapping template files"],"best_for":["Teams standardizing prompt engineering practices","Projects requiring consistent LLM behavior across batch operations","Developers iterating on prompt quality without code changes"],"limitations":["No built-in prompt optimization or validation — templates can produce poor results","Variable substitution is simple string replacement; no conditional logic or loops","No version control for templates; changes affect all future uses","Template syntax not standardized — may conflict with LLM prompt syntax"],"requires":["Template files in supported format (Handlebars, Jinja2, or custom syntax)","Variable definitions provided at runtime"],"input_types":["template files","variable values (strings, file paths, code snippets)"],"output_types":["substituted prompts","rendered templates"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-gpt-runner__cap_6","uri":"capability://search.retrieval.file.content.indexing.and.semantic.search","name":"file content indexing and semantic search","description":"Builds an in-memory or persistent index of file contents, enabling semantic search queries to find relevant files or code snippets without reading the entire filesystem. The agent may use keyword matching, embeddings, or AST-based indexing to quickly locate files matching developer queries, reducing the context needed for each LLM call.","intents":["Find all files related to a specific feature or module without manual exploration","Locate functions with similar logic across the codebase for refactoring","Identify files that need updates when a dependency changes","Search for code patterns or anti-patterns across many files"],"best_for":["Developers working with large codebases where manual file exploration is slow","Teams conducting codebase-wide refactoring or migration projects","Projects needing to understand cross-file dependencies"],"limitations":["Indexing adds startup latency; large codebases may take seconds to index","Semantic search quality depends on embedding model; keyword search may miss relevant files","Index must be rebuilt when files change; no incremental indexing","No support for binary files or non-text content"],"requires":["Sufficient memory for in-memory index (scales with codebase size)","Optional: embedding model API or local embedding service for semantic search","File system access with read permissions"],"input_types":["search queries (natural language or keywords)","file paths or patterns to index"],"output_types":["ranked list of relevant files","file snippets matching search criteria","search result metadata (relevance scores)"],"categories":["search-retrieval","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-gpt-runner__cap_7","uri":"capability://safety.moderation.error.handling.and.recovery.with.user.guidance","name":"error handling and recovery with user guidance","description":"Detects LLM errors, API failures, and malformed outputs, then provides actionable guidance to users on how to resolve issues. The agent may suggest retrying with different parameters, checking API credentials, or reformulating queries, and can automatically retry transient failures with exponential backoff.","intents":["Understand why an LLM query failed and how to fix it","Automatically retry failed API calls without manual intervention","Get suggestions for reformulating queries that produce errors","Debug authentication or configuration issues with clear error messages"],"best_for":["Non-technical users who need clear error guidance","Automated workflows that must handle transient failures gracefully","Teams debugging LLM integration issues"],"limitations":["Error recovery is heuristic-based; not all failures can be automatically resolved","Retry logic may mask underlying issues that require manual intervention","User guidance is generic; may not address domain-specific problems","No built-in monitoring or alerting for repeated failures"],"requires":["Error handling middleware in the LLM client","Logging infrastructure for debugging"],"input_types":["error messages from LLM APIs","failed prompts and responses"],"output_types":["user-friendly error messages","recovery suggestions","retry attempts with modified parameters"],"categories":["safety-moderation","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-gpt-runner__cap_8","uri":"capability://code.generation.editing.code.execution.and.validation.with.sandboxing","name":"code execution and validation with sandboxing","description":"Executes code generated or suggested by the LLM in a controlled environment, validating that it runs without errors before presenting it to the user. The agent may use subprocess isolation, Docker containers, or language-specific sandboxes to safely execute code and capture output, preventing malicious or broken code from affecting the developer's system.","intents":["Verify that generated code snippets actually work before using them","Test refactored code to ensure it maintains functionality","Validate code suggestions against actual runtime behavior","Catch syntax errors or import issues before code review"],"best_for":["Teams using LLM-generated code in production pipelines","Developers who want confidence that AI suggestions are executable","Projects with strict code quality requirements"],"limitations":["Sandboxing adds latency; execution may take seconds per code snippet","Sandbox environment may not match production environment exactly","No support for code requiring external services or network access","Execution timeout limits prevent testing long-running code"],"requires":["Language runtime(s) for code execution (Python, Node.js, etc.)","Optional: Docker for stronger isolation","Sufficient system resources for parallel execution"],"input_types":["code snippets","test cases or assertions"],"output_types":["execution results (stdout, stderr)","validation status (pass/fail)","error messages and stack traces"],"categories":["code-generation-editing","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-gpt-runner__cap_9","uri":"capability://automation.workflow.configuration.management.with.environment.specific.settings","name":"configuration management with environment-specific settings","description":"Manages configuration for LLM providers, file paths, prompt templates, and other settings through configuration files or environment variables, allowing developers to adapt the agent's behavior without code changes. The agent loads configuration at startup and applies environment-specific overrides (development, staging, production).","intents":["Use different LLM providers in development vs. production","Configure file paths and exclusions for different projects","Set API rate limits and timeout values per environment","Share configuration across team members via version control"],"best_for":["Teams deploying the agent across multiple environments","Projects with environment-specific requirements","Developers who want to avoid hardcoding configuration"],"limitations":["Configuration validation is minimal; invalid settings may cause runtime errors","No built-in secrets management; sensitive values (API keys) must be handled separately","Configuration format may not support complex nested structures","No hot-reload; configuration changes require agent restart"],"requires":["Configuration file format support (YAML, JSON, TOML, .env)","Environment variable support in the runtime"],"input_types":["configuration files","environment variables"],"output_types":["parsed configuration objects","validated settings"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":26,"verified":false,"data_access_risk":"high","permissions":["Node.js 14+ or Python 3.8+","API key for OpenAI, Anthropic, or compatible LLM provider","Local file system access with read permissions","Network connectivity for LLM API calls","LLM API key with sufficient quota for batch operations","Write permissions to target directories","Sufficient disk space for output files","Local filesystem with write permissions","Sufficient disk space for conversation logs","JSON or YAML parser support in the runtime"],"failure_modes":["Context window limits prevent analyzing very large codebases in single conversation","Requires explicit file/directory specification — no automatic intelligent file selection","Conversation history grows linearly; no automatic summarization or context pruning","File changes during conversation are not automatically detected or refreshed","No built-in deduplication — processes each file independently, missing cross-file optimization opportunities","Requires manual specification of which files to process; no intelligent filtering","No rollback mechanism if transformations produce errors across multiple files","API costs scale linearly with file count; no batching optimization for similar files","Serialized state can become large for long conversations; no automatic compression","No built-in versioning — overwriting conversations loses previous versions","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.3,"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":"2026-06-17T09:51:03.041Z","last_scraped_at":"2026-05-03T14:00:10.321Z","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=gpt-runner","compare_url":"https://unfragile.ai/compare?artifact=gpt-runner"}},"signature":"4NR8aM62cqaCjTvxwX32+C2QxzEZefq4PSLxmQ86SRn1D8TPJVAWW7WSY0XahSSlzDITGsZdKB8R5D2BSJiRBA==","signedAt":"2026-06-19T21:01:19.662Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/gpt-runner","artifact":"https://unfragile.ai/gpt-runner","verify":"https://unfragile.ai/api/v1/verify?slug=gpt-runner","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"}}