{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"awesome-adrenaline-debugger-that-fixes-errors-and-explains-them-with-gpt-3","slug":"adrenaline-debugger-that-fixes-errors-and-explains-them-with-gpt-3","name":"Adrenaline: Debugger that fixes errors and explains them with GPT-3","type":"repo","url":"https://github.com/shobrook/adrenaline/","page_url":"https://unfragile.ai/adrenaline-debugger-that-fixes-errors-and-explains-them-with-gpt-3","categories":["code-review-security"],"tags":[],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"awesome-adrenaline-debugger-that-fixes-errors-and-explains-them-with-gpt-3__cap_0","uri":"capability://code.generation.editing.error.detection.and.diagnosis.from.stack.traces","name":"error-detection-and-diagnosis-from-stack-traces","description":"Parses runtime error stack traces and exception messages to identify root causes, then queries GPT-3 to generate contextual explanations of what went wrong. The system extracts file paths, line numbers, and error types from structured stack trace output, maps them to source code context, and uses that context window to prompt GPT-3 for diagnosis rather than sending raw traces.","intents":["I want to understand why my code crashed without manually reading the full stack trace","I need a plain-English explanation of what this exception means in my specific codebase context","I want to quickly triage whether this is a logic error, dependency issue, or environment problem"],"best_for":["solo developers debugging locally without IDE integration","teams using command-line workflows who want AI-assisted error interpretation","developers learning new languages/frameworks and unfamiliar with error messages"],"limitations":["Requires complete, properly formatted stack traces — truncated or obfuscated traces reduce accuracy","GPT-3 responses are non-deterministic; same error may receive slightly different explanations on repeated runs","No persistent error history — cannot learn from patterns across multiple errors in same codebase","Limited to errors that produce stack traces; silent failures or hangs are not detectable"],"requires":["OpenAI API key with GPT-3 access","Python 3.6+","Access to source code files referenced in stack trace"],"input_types":["text (stack trace output)","code (source files for context)"],"output_types":["text (natural language explanation)","structured data (error classification, severity)"],"categories":["code-generation-editing","debugging"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-adrenaline-debugger-that-fixes-errors-and-explains-them-with-gpt-3__cap_1","uri":"capability://code.generation.editing.ai.powered.error.fix.suggestion.generation","name":"ai-powered-error-fix-suggestion-generation","description":"Takes diagnosed errors and generates candidate code fixes by prompting GPT-3 with the error context, stack trace, and surrounding source code. The system constructs a multi-turn prompt that includes the error diagnosis, relevant code snippets (extracted via AST or line-range queries), and asks GPT-3 to propose specific code changes with explanations. Outputs are formatted as diffs or inline code suggestions.","intents":["I want the AI to suggest how to fix this error, not just explain it","I need multiple fix options ranked by likelihood of correctness","I want to see the proposed code change before applying it automatically"],"best_for":["developers who want AI-assisted fixes but maintain manual review control","teams building internal debugging tools that need fix suggestions","learning environments where students see both explanation and solution"],"limitations":["Generated fixes are not validated against test suites — may introduce new bugs or break existing functionality","GPT-3 may suggest fixes that work for the immediate error but violate project conventions or architecture","No awareness of project-specific patterns, custom error handlers, or domain-specific error recovery strategies","Requires sufficient context in source files; fixes degrade in quality for highly abstracted or dynamically-generated code"],"requires":["OpenAI API key with GPT-3 access","Python 3.6+","Source code files with sufficient context around error location"],"input_types":["text (error diagnosis and stack trace)","code (source files and surrounding context)"],"output_types":["text (natural language explanation of fix)","code (proposed code changes as diffs or snippets)"],"categories":["code-generation-editing","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-adrenaline-debugger-that-fixes-errors-and-explains-them-with-gpt-3__cap_2","uri":"capability://text.generation.language.multi.domain.technical.question.answering.with.internet.search","name":"multi-domain-technical-question-answering-with-internet-search","description":"Accepts free-form technical questions across programming concepts, GitHub repositories, documentation, and code snippets, then performs targeted internet searches to ground answers in authoritative sources. The system uses semantic understanding to decompose questions, search for relevant documentation/repositories, and synthesize GPT-3 responses that cite sources. Supports questions about algorithms, design patterns, API behavior, and implementation details.","intents":["I want to ask a technical question and get an answer grounded in current documentation, not just training data","I need to understand how a specific GitHub repository works without reading all the code","I want explanations of programming concepts with real-world examples from documentation"],"best_for":["developers learning new frameworks or languages who want curated explanations","teams onboarding new members who need quick technical context","non-specialists who need technical information explained accessibly"],"limitations":["Internet search results are only as current as search engine indexes — may miss very recent changes or niche documentation","GPT-3 synthesis can conflate information from multiple sources or introduce inaccuracies when sources disagree","No persistent knowledge base — repeated questions trigger new searches rather than leveraging cached answers","Search quality depends on question phrasing; ambiguous questions may retrieve irrelevant documentation"],"requires":["OpenAI API key with GPT-3 access","Internet search API access (Bing, Google, or similar)","Web access to retrieve and parse documentation pages"],"input_types":["text (natural language question)"],"output_types":["text (natural language answer with source citations)","structured data (source URLs, confidence scores)"],"categories":["text-generation-language","search-retrieval","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-adrenaline-debugger-that-fixes-errors-and-explains-them-with-gpt-3__cap_3","uri":"capability://code.generation.editing.code.snippet.analysis.and.explanation","name":"code-snippet-analysis-and-explanation","description":"Accepts user-provided code snippets (functions, classes, or full files) and generates detailed explanations of what the code does, how it works, and potential issues. The system parses the code to identify language, extracts key structures (functions, classes, control flow), and prompts GPT-3 with the code and metadata to generate line-by-line or block-level explanations. Can identify bugs, suggest optimizations, and explain algorithmic complexity.","intents":["I want to understand what this code does without spending time reading it line-by-line","I need to find bugs or inefficiencies in code I didn't write","I want to learn how a specific algorithm or pattern is implemented in this code"],"best_for":["code reviewers who need to understand unfamiliar code quickly","developers learning from open-source code","teams documenting legacy code without original documentation"],"limitations":["Explanations are only as good as GPT-3's understanding of the language and patterns used","Context-dependent code (relying on external state, configuration, or implicit contracts) may be misexplained","No execution or type-checking — cannot identify runtime errors or type mismatches","Large code snippets (>500 lines) may exceed token limits or lose coherence in explanations"],"requires":["OpenAI API key with GPT-3 access","Python 3.6+","Code snippet in text format (supports most programming languages)"],"input_types":["code (source code snippet or file)"],"output_types":["text (natural language explanation)","structured data (identified functions, classes, complexity analysis)"],"categories":["code-generation-editing","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-adrenaline-debugger-that-fixes-errors-and-explains-them-with-gpt-3__cap_4","uri":"capability://search.retrieval.github.repository.analysis.and.architecture.explanation","name":"github-repository-analysis-and-architecture-explanation","description":"Analyzes public GitHub repositories by fetching repository metadata, README files, and key source files, then generates explanations of repository architecture, function behavior, and implementation details. The system constructs a knowledge graph of the repository structure (identifying entry points, main modules, dependencies) and uses GPT-3 to synthesize explanations of how components interact and what the repository does.","intents":["I want to understand what this GitHub repository does and how it's structured without cloning and reading all the code","I need to know how a specific function in a repository works and what it depends on","I want to evaluate whether this repository is suitable for my use case based on its architecture"],"best_for":["developers evaluating open-source libraries before integrating them","teams onboarding new developers who need to understand project structure","researchers analyzing code patterns across multiple repositories"],"limitations":["Analysis is limited to public repositories accessible via GitHub API","Large repositories (>10K files) may timeout or exceed API rate limits during analysis","Architecture explanations are inferred from code structure; may miss design decisions documented only in issues or PRs","No access to commit history or evolution — cannot explain how architecture changed over time"],"requires":["OpenAI API key with GPT-3 access","GitHub API token (for higher rate limits and private repo access if needed)","Python 3.6+","Internet access to GitHub API"],"input_types":["text (GitHub repository URL or owner/repo identifier)"],"output_types":["text (natural language explanation of repository architecture and purpose)","structured data (identified modules, dependencies, entry points)"],"categories":["search-retrieval","text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-adrenaline-debugger-that-fixes-errors-and-explains-them-with-gpt-3__cap_5","uri":"capability://text.generation.language.technical.documentation.interpretation.and.clarification","name":"technical-documentation-interpretation-and-clarification","description":"Retrieves and parses technical documentation from websites (API references, language docs, framework guides) and generates clarifications or answers to specific questions about that documentation. The system fetches documentation pages, extracts relevant sections, and uses GPT-3 to explain concepts, provide examples, or answer questions grounded in the documentation text.","intents":["I want to understand what this API parameter does based on the official documentation","I need a clearer explanation of this concept from the documentation with examples","I want to find the right documentation section that answers my question"],"best_for":["developers learning new APIs or frameworks","teams standardizing on specific tools and needing quick reference","non-native English speakers who need documentation explained more clearly"],"limitations":["Documentation retrieval depends on website structure and accessibility — may fail for sites with JavaScript-heavy rendering","Explanations are limited to information in the documentation — cannot infer undocumented behavior","Large documentation pages may exceed token limits, requiring chunking that can lose context","Documentation may be outdated or contain errors that GPT-3 will faithfully reproduce"],"requires":["OpenAI API key with GPT-3 access","Python 3.6+","Internet access to documentation websites","Web scraping or API access to retrieve documentation content"],"input_types":["text (documentation URL or search query)"],"output_types":["text (natural language explanation or answer)","structured data (documentation section references, code examples)"],"categories":["text-generation-language","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-adrenaline-debugger-that-fixes-errors-and-explains-them-with-gpt-3__cap_6","uri":"capability://planning.reasoning.multi.step.reasoning.for.complex.technical.questions","name":"multi-step-reasoning-for-complex-technical-questions","description":"Decomposes complex technical questions into sub-questions, searches for information to answer each sub-question, and synthesizes a comprehensive answer by reasoning across multiple sources. The system uses chain-of-thought prompting with GPT-3 to break down questions like 'how do I implement X pattern in Y framework' into component questions about the pattern, the framework, and integration points, then retrieves information for each and synthesizes a complete answer.","intents":["I have a complex technical question that requires understanding multiple concepts and how they interact","I want to see the reasoning process behind the answer, not just the final result","I need a comprehensive answer that covers multiple aspects of a technical problem"],"best_for":["architects designing systems and needing to understand multiple technology interactions","teams solving novel technical problems that don't have straightforward documentation","educators teaching complex technical concepts and wanting to show reasoning"],"limitations":["Multi-step reasoning increases latency — each sub-question requires a separate search and GPT-3 call","Errors in early reasoning steps can propagate to final answer; no built-in error correction","Reasoning chains are non-deterministic — same question may produce different decompositions on repeated runs","Token limits may be exceeded for very complex questions with many sub-questions"],"requires":["OpenAI API key with GPT-3 access","Internet search API access","Python 3.6+","Sufficient API quota for multiple sequential calls"],"input_types":["text (natural language question)"],"output_types":["text (comprehensive answer with reasoning steps)","structured data (sub-questions, intermediate answers, synthesis logic)"],"categories":["planning-reasoning","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-adrenaline-debugger-that-fixes-errors-and-explains-them-with-gpt-3__cap_7","uri":"capability://text.generation.language.explanatory.diagram.generation.for.technical.concepts","name":"explanatory-diagram-generation-for-technical-concepts","description":"Generates visual diagrams (ASCII art, structured descriptions, or references to diagram tools) to explain technical concepts, architectures, or workflows. The system uses GPT-3 to generate diagram descriptions or ASCII representations of system architectures, data flows, or algorithm visualizations based on technical questions or code analysis.","intents":["I want to visualize how this system architecture works","I need a diagram to explain this algorithm or data structure to my team","I want to see the flow of data through this code or system"],"best_for":["teams communicating complex architectures to stakeholders","educators explaining technical concepts visually","documentation teams creating visual aids for technical content"],"limitations":["ASCII art diagrams are limited in visual sophistication — complex architectures may be hard to represent","GPT-3-generated diagrams may not follow standard notation (UML, flowchart symbols) consistently","No interactive or animated diagrams — static representations only","Diagram quality depends on GPT-3's ability to understand the concept; abstract or novel concepts may produce poor diagrams"],"requires":["OpenAI API key with GPT-3 access","Python 3.6+","Optional: diagram rendering library (e.g., Graphviz, PlantUML) for more sophisticated output"],"input_types":["text (technical concept, architecture description, or code)"],"output_types":["text (ASCII art diagram or diagram description)","structured data (diagram specification for rendering tools)"],"categories":["text-generation-language","image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-adrenaline-debugger-that-fixes-errors-and-explains-them-with-gpt-3__cap_8","uri":"capability://memory.knowledge.source.attribution.and.citation.tracking","name":"source-attribution-and-citation-tracking","description":"Tracks and attributes information in answers to specific sources (documentation pages, GitHub repositories, Stack Overflow posts, etc.), providing citations and source URLs alongside explanations. The system maintains a mapping of retrieved information to sources and includes source references in generated answers, enabling users to verify information and explore sources independently.","intents":["I want to verify the answer by checking the original source","I need to cite sources in documentation or reports based on this answer","I want to know which sources were used to generate this answer"],"best_for":["teams creating documentation that requires source attribution","researchers needing to trace information to authoritative sources","developers verifying AI-generated answers before using them in production"],"limitations":["Source tracking adds overhead to answer generation — increases latency","Not all information sources are equally authoritative — system may cite low-quality sources alongside official documentation","Source URLs may change or become invalid over time — citations can become stale","Synthesized answers that combine information from multiple sources may not map cleanly to individual citations"],"requires":["OpenAI API key with GPT-3 access","Internet search API access","Python 3.6+","Database or data structure to track source-to-information mappings"],"input_types":["text (question or code for analysis)"],"output_types":["text (answer with inline citations)","structured data (source URLs, confidence scores, source types)"],"categories":["memory-knowledge","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":26,"verified":false,"data_access_risk":"high","permissions":["OpenAI API key with GPT-3 access","Python 3.6+","Access to source code files referenced in stack trace","Source code files with sufficient context around error location","Internet search API access (Bing, Google, or similar)","Web access to retrieve and parse documentation pages","Code snippet in text format (supports most programming languages)","GitHub API token (for higher rate limits and private repo access if needed)","Internet access to GitHub API","Internet access to documentation websites"],"failure_modes":["Requires complete, properly formatted stack traces — truncated or obfuscated traces reduce accuracy","GPT-3 responses are non-deterministic; same error may receive slightly different explanations on repeated runs","No persistent error history — cannot learn from patterns across multiple errors in same codebase","Limited to errors that produce stack traces; silent failures or hangs are not detectable","Generated fixes are not validated against test suites — may introduce new bugs or break existing functionality","GPT-3 may suggest fixes that work for the immediate error but violate project conventions or architecture","No awareness of project-specific patterns, custom error handlers, or domain-specific error recovery strategies","Requires sufficient context in source files; fixes degrade in quality for highly abstracted or dynamically-generated code","Internet search results are only as current as search engine indexes — may miss very recent changes or niche documentation","GPT-3 synthesis can conflate information from multiple sources or introduce inaccuracies when sources disagree","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.43,"ecosystem":0.39999999999999997,"match_graph":0.25,"freshness":0.52,"weights":{"adoption":0.3,"quality":0.2,"ecosystem":0.15,"match_graph":0.3,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-06-17T09:51:02.370Z","last_scraped_at":"2026-05-03T14:00:05.262Z","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=adrenaline-debugger-that-fixes-errors-and-explains-them-with-gpt-3","compare_url":"https://unfragile.ai/compare?artifact=adrenaline-debugger-that-fixes-errors-and-explains-them-with-gpt-3"}},"signature":"QbdfKJWueUXNDzJnfaWSzZUq0D7pOwvSa0zpwNjNSaZaz1vKxBZG7Aua7L5/2p2KeWUWPZk2nPSPk1nmK2RnDg==","signedAt":"2026-06-21T14:28:18.149Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/adrenaline-debugger-that-fixes-errors-and-explains-them-with-gpt-3","artifact":"https://unfragile.ai/adrenaline-debugger-that-fixes-errors-and-explains-them-with-gpt-3","verify":"https://unfragile.ai/api/v1/verify?slug=adrenaline-debugger-that-fixes-errors-and-explains-them-with-gpt-3","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"}}