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The agent receives a coherent view of related code without manually specifying every file, enabling cross-file reasoning and refactoring.","intents":["I want the agent to understand how a function is used across the codebase before refactoring it","I need the agent to see related files (imports, dependencies) without me listing them explicitly","I want the agent to make changes that are consistent across multiple files that reference each other"],"best_for":["developers working with interconnected codebases (monorepos, microservices)","teams refactoring APIs or shared libraries across multiple files","projects where agent needs to understand dependency graphs to make safe changes"],"limitations":["Context aggregation is heuristic-based — may miss relevant files if import patterns are non-standard or dynamic","Large codebases may exceed token limits even with aggregation — requires manual chunking or summarization","No semantic understanding of code — relies on syntactic patterns (imports, file names) rather than actual dependency analysis","Circular dependencies or complex import graphs may cause redundant file inclusion, wasting context"],"requires":["Codebase with standard import/require patterns (ES6, CommonJS, Python imports, etc.)","File system access to read all relevant files","OpenAI API key with sufficient token limits for aggregated context"],"input_types":["file paths","import/require statements","user-specified file patterns (glob, regex)","entry point files"],"output_types":["aggregated code context","dependency graph visualization (optional)","file relevance scores","context window usage metrics"],"categories":["memory-knowledge","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"codex-cli__cap_3","uri":"capability://text.generation.language.natural.language.to.code.instruction.parsing","name":"natural-language-to-code-instruction-parsing","description":"Interprets high-level natural language instructions from the user (e.g., 'refactor this function to use async/await' or 'add error handling to all API calls') and translates them into concrete code modification tasks for the agent. Uses OpenAI's language understanding to disambiguate intent, infer scope, and generate specific modification plans before executing changes.","intents":["I want to describe what I need in plain English and have the agent figure out how to implement it","I need the agent to infer the scope of changes (which files, which functions) from a vague instruction","I want the agent to ask clarifying questions if my instruction is ambiguous before making changes"],"best_for":["non-technical stakeholders or junior developers who can describe intent but not implementation","rapid prototyping where speed matters more than precision","teams using agents as a collaborative tool where natural language is the interface"],"limitations":["Ambiguous instructions may result in incorrect scope or implementation — agent may modify more or fewer files than intended","No built-in confirmation step — agent executes changes based on its interpretation without user approval","Instruction parsing depends on OpenAI's language model quality — edge cases or domain-specific terminology may be misunderstood","No learning or feedback loop — agent doesn't improve interpretation based on past mistakes"],"requires":["OpenAI API key","Clear, descriptive natural language instructions","Codebase context (files, structure) available to agent"],"input_types":["natural language instructions (string)","codebase context","optional: examples of desired changes"],"output_types":["parsed task specification","scope definition (affected files/functions)","clarifying questions (if ambiguous)","execution plan"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"codex-cli__cap_4","uri":"capability://planning.reasoning.iterative.agent.feedback.and.refinement.loop","name":"iterative-agent-feedback-and-refinement-loop","description":"Implements a multi-turn loop where the agent executes changes, observes results (test failures, linter errors, runtime issues), and refines modifications based on feedback. The agent can retry failed operations, adjust code based on error messages, and converge on a working solution without human intervention between iterations.","intents":["I want the agent to fix failing tests by analyzing error output and adjusting code","I need the agent to retry failed operations with different approaches if the first attempt doesn't work","I want the agent to learn from linter/compiler errors and fix style or type issues autonomously"],"best_for":["automated testing and CI/CD workflows where agents fix failures","refactoring tasks where correctness can be validated by tests","teams using agents for iterative code improvement (performance optimization, style fixes)"],"limitations":["Feedback loop can be slow — each iteration requires API call to OpenAI, adding latency","Agent may get stuck in local optima — if initial approach is fundamentally wrong, agent may iterate inefficiently","No built-in timeout or iteration limit — runaway loops can consume API quota and time","Feedback quality depends on test/linter output clarity — cryptic error messages may confuse agent"],"requires":["OpenAI API key with sufficient quota","Executable tests or linters to provide feedback","Codebase with clear success criteria (passing tests, no linter errors)"],"input_types":["initial code modification request","test/linter output","error messages","execution logs"],"output_types":["refined code","iteration history and logs","final success/failure status","summary of changes made across iterations"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"codex-cli__cap_5","uri":"capability://code.generation.editing.codebase.aware.file.creation.and.structure.inference","name":"codebase-aware-file-creation-and-structure-inference","description":"Enables the agent to create new files that conform to the existing codebase structure, naming conventions, and architectural patterns. The agent analyzes existing files to infer directory organization, module structure, and style conventions, then generates new files that fit seamlessly into the project without manual specification of paths or formatting.","intents":["I want the agent to create a new component that follows the same patterns as existing code","I need the agent to generate files in the correct directory structure without me specifying paths","I want the agent to infer naming conventions and code style from the codebase and apply them to new files"],"best_for":["teams with consistent codebase conventions and architectural patterns","projects where new files should follow established structure (e.g., feature-based organization, layered architecture)","rapid scaffolding of boilerplate code that conforms to project standards"],"limitations":["Inference is heuristic-based — agent may misidentify patterns if codebase is inconsistent or has multiple conventions","No explicit schema or configuration — agent relies on analyzing existing files, which can be slow for large codebases","Agent may create files in unexpected locations if directory structure is non-standard or implicit","No validation that new files actually fit the architecture — relies on agent's understanding of patterns"],"requires":["Existing codebase with clear structure and conventions","Read access to existing files for pattern analysis","Write access to target directories"],"input_types":["description of new file/component to create","existing codebase files (for pattern inference)","optional: explicit path or naming hints"],"output_types":["new files created in inferred locations","files conforming to inferred style and structure","summary of inferred patterns and decisions"],"categories":["code-generation-editing","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"codex-cli__cap_6","uri":"capability://tool.use.integration.openai.model.selection.and.api.integration","name":"openai-model-selection-and-api-integration","description":"Provides seamless integration with OpenAI's API, allowing users to select between available models (GPT-4, GPT-3.5-turbo, etc.) and automatically handles authentication, request formatting, and response parsing. The CLI abstracts away API details while exposing model selection as a configuration option, enabling users to trade off cost vs. reasoning capability.","intents":["I want to use GPT-4 for complex reasoning but GPT-3.5-turbo for simple tasks to save costs","I need to configure which OpenAI model the agent uses without modifying code","I want to ensure my API key is securely passed to the CLI without exposing it in logs"],"best_for":["developers integrating OpenAI models into local workflows","teams managing API costs by selecting appropriate models per task","users who want to experiment with different models without code changes"],"limitations":["Tied to OpenAI's API — no support for other LLM providers (Anthropic, open-source models) without forking","API rate limits and quota management are user's responsibility — CLI doesn't implement backoff or queuing","Model availability depends on OpenAI's current offerings — older models may be deprecated","No local fallback — if OpenAI API is unavailable, CLI cannot function"],"requires":["Valid OpenAI API key (set via environment variable or config file)","Network access to OpenAI API endpoints","Active OpenAI account with API access enabled"],"input_types":["model name (string, e.g., 'gpt-4', 'gpt-3.5-turbo')","API key (via environment or config)","request parameters (temperature, max_tokens, etc.)"],"output_types":["LLM responses","token usage metrics","API error messages"],"categories":["tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"codex-cli__cap_7","uri":"capability://memory.knowledge.agent.state.and.conversation.history.management","name":"agent-state-and-conversation-history-management","description":"Maintains conversation history and agent state across multiple turns, allowing the agent to reference previous instructions, modifications, and results. The CLI stores interaction logs and can resume interrupted sessions or provide context for follow-up instructions without requiring users to repeat information.","intents":["I want to give the agent a task, then follow up with refinements without repeating the original context","I need to see what the agent did in previous steps to understand its reasoning","I want to resume an interrupted session where the agent left off"],"best_for":["long-running refactoring tasks that span multiple sessions","teams collaborating on code changes where history is important","debugging agent behavior by reviewing conversation logs"],"limitations":["State is stored locally — no cloud sync or multi-device access","Conversation history can grow large, consuming disk space and slowing retrieval","No built-in state compression or summarization — old context may become irrelevant but still consumes tokens","State is not encrypted — sensitive code or API keys in history could be exposed if disk is compromised"],"requires":["Local filesystem with write permissions","Sufficient disk space for conversation logs","Optional: configuration to specify state storage location"],"input_types":["conversation messages","code modifications","command execution results","agent reasoning traces"],"output_types":["conversation history (text or structured format)","state snapshots","session logs"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"codex-cli__cap_8","uri":"capability://tool.use.integration.environment.variable.and.configuration.management","name":"environment variable and configuration management","description":"Manages API keys, model selection, and other configuration through environment variables and optional config files. The CLI reads OPENAI_API_KEY, model name, and other settings from the environment or a local config file, allowing users to customize behavior without modifying code. This enables easy switching between models, API keys, and other settings across different projects or environments.","intents":["I want to use different OpenAI models for different tasks without changing code","I need to manage API keys securely across multiple projects","I want to configure the agent's behavior (timeouts, token limits) per project"],"best_for":["developers managing multiple projects with different configurations","teams with security policies around API key management","developers who want to experiment with different models"],"limitations":["No built-in secret management; API keys are stored as plain environment variables","Config file format is not standardized; may vary across versions","No validation of configuration values; invalid settings may cause cryptic errors","No per-command configuration overrides; settings are global to the session"],"requires":["Environment variable support (OPENAI_API_KEY, etc.)","Optional config file (format depends on implementation)"],"input_types":["environment variables (text)","config file (YAML, JSON, or other format)","command-line arguments (text)"],"output_types":["resolved configuration (structured)","validation errors (text)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"codex-cli__headline","uri":"capability://tool.use.integration.ai.powered.cli.coding.assistant","name":"ai-powered cli coding assistant","description":"OpenAI's Codex CLI is a lightweight coding agent that operates directly in the terminal, allowing users to read, modify files, and execute commands within their codebase efficiently. It features sandboxed execution and supports multi-file editing, making it an ideal tool for developers looking to enhance their coding workflow.","intents":["best AI coding CLI","AI CLI for terminal coding","Codex CLI for code modification","lightweight coding assistant for terminal","CLI tool for multi-file editing"],"best_for":["developers needing terminal-based coding assistance"],"limitations":[],"requires":[],"input_types":[],"output_types":[],"categories":["tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":77,"verified":false,"data_access_risk":"high","permissions":["OpenAI API key (GPT-4 or GPT-3.5-turbo compatible)","Node.js 16+ or Python 3.8+","Read/write filesystem permissions in target directory","Network access to OpenAI API endpoints","Shell environment (bash, zsh, or PowerShell) available in PATH","Permissions to execute commands in the target directory","OpenAI API key for agent reasoning","No restrictive SELinux or AppArmor policies blocking command execution","Codebase with standard import/require patterns (ES6, CommonJS, Python imports, etc.)","File system access to read all relevant files"],"failure_modes":["Sandboxing is filesystem-level only — no process-level isolation, so agent can still execute arbitrary shell commands within allowed scope","No built-in version control integration — changes are applied directly to files without automatic git commits or rollback","Context window limits mean large codebases may require chunking or summarization before agent can reason about full structure","Agent reasoning is sequential — no parallel multi-file analysis, so large refactors can be slow","Sandboxing is directory-scoped, not process-scoped — agent can still access environment variables and system binaries outside the project","No timeout enforcement by default — long-running commands can block the agent indefinitely","Output parsing is agent-dependent — if agent misinterprets command output, it may make incorrect decisions","Platform-specific: bash/zsh on Unix, PowerShell on Windows — scripts must be written for target OS","Context aggregation is heuristic-based — may miss relevant files if import patterns are non-standard or dynamic","Large codebases may exceed token limits even with aggregation — requires manual chunking or summarization","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7,"quality":0.8500000000000001,"ecosystem":0.39999999999999997,"match_graph":0.25,"freshness":0.75,"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":"2026-06-17T09:51:04.690Z","last_scraped_at":null,"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=codex-cli","compare_url":"https://unfragile.ai/compare?artifact=codex-cli"}},"signature":"o2/dSFop62w7bsfKCy2kBj/XpPfxUp0oYh3ArBiqxAovHFcRW4KBDkVzUM6L3d1BsZwVOJo5G1xYeiz+k+JPCg==","signedAt":"2026-06-21T08:23:58.576Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/codex-cli","artifact":"https://unfragile.ai/codex-cli","verify":"https://unfragile.ai/api/v1/verify?slug=codex-cli","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"}}