{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"awesome-chatgpt-code-review","slug":"chatgpt-code-review","name":"ChatGPT Code Review","type":"repo","url":"https://github.com/kxxt/chatgpt-action","page_url":"https://unfragile.ai/chatgpt-code-review","categories":["code-review-security"],"tags":[],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"awesome-chatgpt-code-review__cap_0","uri":"capability://code.generation.editing.github.pull.request.code.review.automation.via.chatgpt","name":"github pull request code review automation via chatgpt","description":"Automatically triggers ChatGPT code review analysis when pull requests are opened or updated, integrating with GitHub Actions to post review comments directly on PR diffs. 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The system converts function definitions into provider-specific formats (OpenAI tools, Anthropic functions), handles parameter validation, and routes calls to registered handler functions.","intents":["Let LLMs call external functions or APIs in a structured, type-safe manner","Validate function arguments before execution to prevent runtime errors","Support multiple function calling formats across different LLM providers","Build agentic workflows where LLMs can interact with external systems"],"best_for":["Developers building LLM agents that need to interact with APIs or databases","Teams requiring strict input validation before executing LLM-requested actions","Applications needing multi-provider function calling support"],"limitations":["Schema definition overhead — requires explicit type annotations for each function parameter","LLM hallucination risk — models may generate function calls with invalid parameters despite schema constraints","No built-in retry logic for failed function calls — application must handle retries","Limited to synchronous function execution — no native async/await support","Schema complexity increases with nested objects and conditional parameters"],"requires":["Function definitions with type annotations (Python type hints or JSON schema)","LLM provider supporting function calling (OpenAI, Anthropic, or compatible)","Handler functions registered in the function registry"],"input_types":["Function definitions with parameter schemas","LLM-generated function call requests","User-provided arguments matching schema types"],"output_types":["Validated function call results","Schema validation error messages","Structured function call metadata"],"categories":["tool-use-integration","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-chatgpt-code-review__cap_4","uri":"capability://memory.knowledge.chat.history.management.with.context.windowing","name":"chat history management with context windowing","description":"Maintains conversation history across multiple turns, automatically managing context window constraints by summarizing or truncating older messages when approaching token limits. The system tracks message roles (user/assistant/system), token counts per message, and implements sliding window or summarization strategies to keep recent context while staying within model limits.","intents":["Maintain multi-turn conversations without losing context","Automatically manage token limits to prevent context overflow errors","Implement conversation summarization to preserve long-term context","Support conversation persistence and resumption across sessions"],"best_for":["Chatbot applications requiring multi-turn conversations","Long-running agents that need to maintain state across multiple interactions","Teams building conversational interfaces with limited token budgets"],"limitations":["Summarization strategies may lose important details from older messages","Token counting overhead — requires per-message tokenization which adds latency","No built-in persistence layer — requires external storage for conversation history","Context window management is reactive (triggered when limit approached) rather than proactive","Summarization quality depends on LLM capability — may produce inaccurate summaries"],"requires":["Token counter implementation matching target LLM model","Conversation storage mechanism (in-memory, database, or file-based)","Configuration for context window size and truncation strategy"],"input_types":["User messages (text)","Assistant responses (text)","System prompts and instructions"],"output_types":["Conversation history with managed context","Truncated or summarized message sequences","Token count metadata per message"],"categories":["memory-knowledge","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-chatgpt-code-review__cap_5","uri":"capability://automation.workflow.github.actions.workflow.orchestration.and.event.triggering","name":"github actions workflow orchestration and event triggering","description":"Integrates with GitHub Actions to trigger automated workflows based on repository events (push, pull request, schedule) and manage workflow execution state. The system uses GitHub's webhook system to detect events, parses event payloads, and invokes configured actions with context-specific parameters extracted from the event metadata.","intents":["Trigger code review automation on pull request events","Run scheduled tasks (nightly reviews, periodic analysis) on cron schedules","Pass repository context (branch, commit, changed files) to downstream actions","Integrate with GitHub's native CI/CD system without external orchestration"],"best_for":["GitHub-native teams wanting to avoid external CI/CD platforms","Projects requiring simple event-driven automation without complex orchestration","Open-source projects leveraging GitHub's free Actions quota"],"limitations":["Limited to GitHub platform — no support for other Git hosting services","Workflow syntax (YAML) is GitHub-specific and not portable to other CI/CD systems","Execution environment constraints (runner specs, timeout limits) may not suit all workloads","No built-in workflow versioning or rollback — changes to workflow files apply immediately","Debugging workflow failures requires examining GitHub Actions logs, which have limited retention"],"requires":["GitHub repository with Actions enabled","YAML workflow file in .github/workflows/ directory","Appropriate GitHub token with permissions for the triggered actions"],"input_types":["GitHub webhook events (push, pull_request, schedule, etc.)","Event payload JSON with repository and change metadata","Workflow input parameters"],"output_types":["Workflow execution status and logs","GitHub check runs and status checks","Comments and annotations on PRs or commits"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-chatgpt-code-review__cap_6","uri":"capability://data.processing.analysis.diff.parsing.and.code.change.extraction","name":"diff parsing and code change extraction","description":"Parses unified diff format (git diff output) to extract changed code sections, identifies modified lines with context, and maps changes to source file locations. 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