{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github-github--spec-kit","slug":"github--spec-kit","name":"spec-kit","type":"framework","url":"https://github.github.com/spec-kit/","page_url":"https://unfragile.ai/github--spec-kit","categories":["app-builders"],"tags":["ai","copilot","development","engineering","prd","spec","spec-driven"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github-github--spec-kit__cap_0","uri":"capability://planning.reasoning.spec.driven.development.workflow.orchestration.with.resumable.execution","name":"spec-driven development workflow orchestration with resumable execution","description":"Implements a five-phase specification-to-code pipeline (Constitution → Specify → Plan → Tasks → Implement) where each phase generates executable artifacts that feed into the next. Uses a resumable workflow engine (v0.7.0+) that persists execution state, allowing developers to pause/resume multi-step AI-assisted development without losing context. The specify CLI orchestrates phase transitions via slash commands (/speckit.specify, /speckit.plan, /speckit.tasks, /speckit.implement) that generate structured markdown documents in .specify/memory/ and specs/ directories, making specifications machine-readable and directly consumable by AI agents.","intents":["I want to convert natural language requirements into executable specifications that AI agents can implement without ambiguity","I need to pause and resume long-running code generation workflows without losing context or regenerating earlier phases","I want to maintain a complete audit trail of how requirements evolved into implementation through intermediate artifacts","I need to coordinate multiple AI agents across different development phases with clear handoff points"],"best_for":["Teams implementing Spec-Driven Development methodology for AI-assisted development","Solo developers building features with AI coding agents (Copilot, Claude, Devin, etc.)","Organizations wanting to standardize AI-assisted development workflows across projects"],"limitations":["Resumable workflow state is stored locally in .specify/ directory — no built-in distributed state management for team collaboration","Phase transitions are sequential; no parallel execution of workflow steps","Requires explicit agent integration via INTEGRATION_REGISTRY — not all AI agents have native support","Template resolution follows a fixed precedence order (project → preset → default) with no custom override hooks"],"requires":["Python 3.9+","Git repository initialized in project root","At least one supported AI agent configured (30+ agents in INTEGRATION_REGISTRY)","Write access to .specify/ and specs/ directories"],"input_types":["Natural language feature descriptions","Markdown specification documents","YAML workflow definitions","JSON preset configurations"],"output_types":["Markdown specification documents (.specify/memory/constitution.md, specs/NNN-feature/spec.md)","Structured task lists (specs/NNN-feature/tasks.md)","Implementation code (generated by agent in final phase)","Workflow execution logs and state checkpoints"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-github--spec-kit__cap_1","uri":"capability://tool.use.integration.multi.agent.integration.registry.with.standardized.context.management","name":"multi-agent integration registry with standardized context management","description":"Maintains an INTEGRATION_REGISTRY that abstracts 30+ AI coding agents (GitHub Copilot, Claude, Devin, etc.) behind a unified interface. Each agent has a standardized directory structure (.specify/agents/{agent_name}/) where context files, prompts, and agent-specific configuration are stored. The system provides Agent Context Management that automatically updates agent-specific context based on project state, allowing the same specification to be executed by different agents without manual context switching. Supports native function-calling APIs for OpenAI, Anthropic, and other providers.","intents":["I want to switch between different AI agents (Copilot, Claude, Devin) without rewriting prompts or losing context","I need to maintain agent-specific configuration and context files that persist across workflow phases","I want to add support for a new AI agent without modifying core Spec Kit code","I need to ensure all agents receive the same project context and specification artifacts"],"best_for":["Teams evaluating multiple AI agents and wanting to switch between them without workflow disruption","Developers building custom AI agent integrations for proprietary or specialized agents","Organizations with heterogeneous AI agent deployments (some teams use Copilot, others use Claude)"],"limitations":["Agent context updates are pull-based (triggered by specify CLI commands) rather than event-driven — no real-time context sync","No built-in conflict resolution if multiple agents modify the same files simultaneously","Agent-specific capabilities (e.g., tool calling, streaming) require custom integration code","Context size limits vary by agent; no automatic context pruning or summarization for large projects"],"requires":["API credentials for target AI agent (OpenAI API key, Anthropic API key, etc.)","Agent-specific SDK or API client installed","Agent entry in INTEGRATION_REGISTRY (or custom agent definition)","Write access to .specify/agents/{agent_name}/ directory"],"input_types":["Agent configuration (JSON/YAML)","Project specifications (Markdown)","Context files (code snippets, architecture docs)","Agent-specific prompts (text templates)"],"output_types":["Agent-specific context files (.specify/agents/{agent_name}/context.md)","Agent configuration updates","Implementation code from selected agent","Agent execution logs and telemetry"],"categories":["tool-use-integration","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-github--spec-kit__cap_10","uri":"capability://tool.use.integration.community.contributed.preset.and.extension.catalogs.with.discovery","name":"community-contributed preset and extension catalogs with discovery","description":"Maintains community-contributed catalogs (presets/catalog.community.json, extensions/catalog.community.json) that allow teams to discover and reuse presets and extensions created by other organizations. The catalog system provides metadata for each preset/extension (name, description, author, version, compatibility), enabling teams to search and filter by use case. Teams can publish their own presets and extensions to the community catalog via a standardized submission process. The specify preset and specify extension commands allow teams to browse, install, and manage presets/extensions from the catalog. Catalogs are versioned and support dependency resolution for extensions that depend on other extensions.","intents":["I want to discover presets and extensions created by other teams for common development patterns","I need to publish my team's custom presets and extensions for other teams to reuse","I want to manage preset and extension versions and dependencies across my organization","I need to find presets/extensions that match my project's language, framework, or domain"],"best_for":["Teams wanting to leverage community-contributed presets and extensions","Organizations building internal developer platforms and wanting to share components","Open-source contributors creating reusable presets and extensions for the community"],"limitations":["Catalog is centralized (GitHub-hosted); no support for private or organization-specific catalogs","No built-in security scanning for community-contributed presets/extensions; teams must review code before use","Dependency resolution is basic; no support for version constraints or semantic versioning","No usage analytics or community ratings to help teams evaluate preset/extension quality"],"requires":["Internet access to fetch community catalogs","Git credentials for publishing presets/extensions (optional)","Preset/extension definition (JSON) and implementation code"],"input_types":["Catalog metadata (JSON)","Preset/extension definitions","Search queries (name, language, framework, domain)"],"output_types":["Catalog listings with metadata","Installed presets/extensions","Dependency resolution reports","Publication confirmations"],"categories":["tool-use-integration","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-github--spec-kit__cap_11","uri":"capability://memory.knowledge.agent.context.injection.and.dynamic.prompt.generation","name":"agent context injection and dynamic prompt generation","description":"Implements Agent Context Management that automatically injects project context (constitution, specifications, task lists, code snippets) into prompts sent to AI agents. The system maintains a context budget (respecting agent token limits) and uses intelligent summarization to fit relevant context within available tokens. Context injection is phase-aware: specification generation includes constitution and project structure; implementation includes specification, tasks, and relevant code examples. The system supports context caching (where available) to reduce token usage across multiple agent calls. Custom context processors can be defined via extensions to inject domain-specific context (e.g., API schemas, database migrations).","intents":["I want AI agents to have access to project context without manually copying/pasting information","I need to optimize token usage by intelligently selecting relevant context for each agent call","I want to inject domain-specific context (API schemas, database migrations) automatically","I need to ensure all agents receive consistent context across different development phases"],"best_for":["Teams using AI agents for code generation who want to reduce manual context management","Projects with large codebases where full context cannot fit in agent token limits","Organizations with custom context requirements (domain-specific schemas, internal tools)"],"limitations":["Context selection is heuristic-based; no guarantee that selected context is optimal for agent task","Summarization may lose important details; no mechanism to request additional context if agent fails","Context caching is agent-specific; not all agents support caching (e.g., older API versions)","No built-in mechanism to detect stale context; manual updates required if project context changes"],"requires":["Project context (constitution, specifications, code)","AI agent with context injection support","Context budget configuration (token limits)","Optional: custom context processors (via extensions)"],"input_types":["Project context (constitution, specs, code)","Agent task/prompt","Context budget (token limits)","Custom context processors"],"output_types":["Injected prompt with context","Context selection report (what was included/excluded)","Token usage metrics","Cache hit/miss statistics"],"categories":["memory-knowledge","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-github--spec-kit__cap_12","uri":"capability://code.generation.editing.specification.to.code.generation.with.ai.agent.orchestration","name":"specification-to-code generation with ai agent orchestration","description":"Implements the /speckit.implement slash command that orchestrates AI agents to generate working implementation code from specifications and task lists. The implementation phase passes the specification, tasks, constitution, and relevant code examples to the selected AI agent, which generates code that satisfies the specification requirements. The system supports multiple implementation strategies: single-agent implementation (one agent generates all code), multi-agent implementation (different agents handle different components), and incremental implementation (agents implement tasks sequentially). Implementation artifacts are validated against specification requirements, and failures trigger re-generation with additional context or agent switching.","intents":["I want to generate working implementation code directly from specifications without manual coding","I need to implement complex features by coordinating multiple AI agents on different components","I want to validate generated code against specification requirements automatically","I need to handle implementation failures by re-generating with additional context or switching agents"],"best_for":["Teams using AI agents for code generation who want end-to-end automation","Projects with well-defined specifications and clear acceptance criteria","Organizations wanting to reduce manual coding effort for routine features"],"limitations":["Generated code quality depends heavily on specification clarity and AI agent capability","No built-in code review or security scanning; generated code requires manual review before merging","Multi-agent implementation requires careful coordination; no automatic conflict resolution if agents modify same files","Implementation validation is specification-based; no guarantee that generated code is production-ready"],"requires":["Specification document (specs/NNN-feature/spec.md)","Task list (specs/NNN-feature/tasks.md)","AI agent configured in INTEGRATION_REGISTRY","Write access to project code directory"],"input_types":["Specification document (Markdown)","Task list (Markdown)","Constitution (project context)","Relevant code examples (for style/pattern matching)"],"output_types":["Generated implementation code","Implementation validation report","Code coverage metrics","Test cases (if generated)"],"categories":["code-generation-editing","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-github--spec-kit__cap_2","uri":"capability://text.generation.language.template.based.specification.and.task.generation.with.preset.system","name":"template-based specification and task generation with preset system","description":"Implements a three-tier template resolution system (project-level → preset → default templates) that generates specifications and task lists from natural language inputs. The Preset System provides reusable template catalogs (presets/catalog.community.json) that define document templates, command templates, and workflow step types. When a developer runs /speckit.specify or /speckit.tasks, the system resolves the appropriate template, interpolates variables from project context, and generates structured markdown documents. Templates support Jinja2-style variable substitution and conditional sections, enabling flexible specification generation across different project types and domains.","intents":["I want to generate consistent specification documents without manually writing boilerplate sections","I need to create reusable templates for common feature types (CRUD operations, API endpoints, etc.)","I want to customize specification templates for my team's specific requirements without forking Spec Kit","I need to generate task breakdowns automatically from specifications using domain-specific templates"],"best_for":["Teams with standardized development processes wanting to enforce specification consistency","Organizations building domain-specific development workflows (e.g., data pipelines, microservices)","Developers creating reusable preset catalogs for their organization or open-source community"],"limitations":["Template resolution follows fixed precedence (project → preset → default) with no custom override hooks for edge cases","Jinja2 template syntax has limited expressiveness — complex conditional logic requires custom template extensions","No built-in template validation; invalid templates fail at generation time rather than during preset definition","Preset catalogs are static JSON files; no dynamic template generation or runtime template composition"],"requires":["Preset definition (JSON) in presets/ directory or from community catalog","Jinja2 template engine (included with Spec Kit)","Project context variables (project name, language, framework, etc.)","Write access to specs/ directory"],"input_types":["Natural language feature description","Preset configuration (JSON)","Template definitions (Jinja2 markdown)","Project context variables (YAML/JSON)"],"output_types":["Generated specification documents (Markdown)","Generated task lists (Markdown with structured sections)","Template-rendered command definitions","Workflow step definitions"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-github--spec-kit__cap_3","uri":"capability://tool.use.integration.extension.system.for.custom.slash.commands.and.workflow.steps","name":"extension system for custom slash commands and workflow steps","description":"Provides an Extension Architecture that allows developers to define custom slash commands (e.g., /speckit.custom-command) and workflow step types without modifying core Spec Kit code. Extensions are registered via extensions/catalog.community.json and loaded dynamically at runtime. Each extension can define custom command handlers, template processors, and workflow step implementations. The system supports extension composition, allowing extensions to depend on and build upon other extensions. Extension development follows a standardized interface with hooks for pre/post-processing, context injection, and output formatting.","intents":["I want to add custom slash commands specific to my team's development process","I need to create custom workflow step types that integrate with our internal tools (Jira, Slack, etc.)","I want to extend Spec Kit's template system with domain-specific processors (e.g., API schema generation)","I need to build a custom extension that other teams can reuse via the community catalog"],"best_for":["Teams with custom development workflows wanting to extend Spec Kit without forking","Organizations building internal developer platforms on top of Spec Kit","Open-source contributors creating reusable extensions for the community"],"limitations":["Extensions are loaded at CLI startup; no hot-reloading during development","No built-in extension dependency resolution — circular dependencies can cause initialization failures","Extension API is Python-only; no support for extensions written in other languages","Limited extension sandboxing; malicious extensions have access to full project context and file system"],"requires":["Python 3.9+","Extension definition (JSON) in extensions/catalog.community.json","Extension implementation module (Python)","Spec Kit extension API knowledge (documented in DEVELOPMENT.md)"],"input_types":["Extension configuration (JSON)","Extension implementation code (Python)","Custom command definitions","Workflow step type definitions"],"output_types":["Custom slash command handlers","Custom workflow step implementations","Extended template processors","Extension metadata and documentation"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-github--spec-kit__cap_4","uri":"capability://text.generation.language.ai.assisted.specification.generation.with.natural.language.to.structured.output","name":"ai-assisted specification generation with natural language to structured output","description":"Transforms natural language feature descriptions into machine-readable specifications through the /speckit.specify slash command. The system uses AI agents to analyze feature requirements, extract key components (inputs, outputs, constraints, acceptance criteria), and generate structured Markdown documents in specs/NNN-feature/spec.md. The specification format is designed to be both human-readable and machine-parseable, with sections for API contracts, data models, error handling, and edge cases. The generated specifications serve as the primary input for downstream phases (planning, task generation, implementation), ensuring AI agents have precise, unambiguous requirements.","intents":["I want to convert a vague feature request into a precise, structured specification without manual writing","I need to ensure AI agents understand all edge cases and constraints before implementation begins","I want to generate API contracts and data models automatically from feature descriptions","I need specifications that are both human-readable and machine-parseable for downstream automation"],"best_for":["Teams using AI agents for code generation who want to reduce ambiguity in requirements","Developers building features with complex requirements (APIs, data models, edge cases)","Organizations wanting to standardize specification format across projects"],"limitations":["Specification quality depends heavily on input description clarity — vague inputs produce vague specs","No built-in validation of generated specifications; invalid specs are only caught during planning/implementation phases","Specifications are generated once and not automatically updated if requirements change","No conflict detection if multiple developers generate specs for overlapping features"],"requires":["Natural language feature description (text input)","AI agent configured in INTEGRATION_REGISTRY","Project context (language, framework, architecture)","Write access to specs/ directory"],"input_types":["Natural language feature description (text)","Project context (code, architecture docs)","Existing specifications (for reference)"],"output_types":["Structured specification document (Markdown)","API contract definitions","Data model schemas","Acceptance criteria and edge cases"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-github--spec-kit__cap_5","uri":"capability://planning.reasoning.automated.task.decomposition.and.planning.from.specifications","name":"automated task decomposition and planning from specifications","description":"Implements the /speckit.plan and /speckit.tasks commands that decompose specifications into actionable development tasks. The planning phase analyzes the specification document and generates a structured plan (specs/NNN-feature/plan.md) that breaks down the feature into implementation phases, identifies dependencies, and estimates complexity. The tasks phase further decomposes the plan into granular tasks (specs/NNN-feature/tasks.md) with clear acceptance criteria, estimated effort, and prerequisite tasks. The decomposition uses AI agents to understand specification semantics and generate realistic task breakdowns that account for testing, documentation, and integration work.","intents":["I want to break down a complex specification into manageable tasks without manual estimation","I need to identify task dependencies and critical path for feature implementation","I want to generate realistic effort estimates for each task based on specification complexity","I need tasks with clear acceptance criteria that AI agents can use to validate implementation"],"best_for":["Teams using AI agents for implementation who need structured task lists","Project managers wanting to estimate effort from specifications","Developers implementing features incrementally with clear task boundaries"],"limitations":["Task decomposition quality depends on specification clarity — ambiguous specs produce ambiguous task lists","Effort estimates are AI-generated and may not reflect actual team velocity or complexity","No built-in task prioritization or scheduling; task order is determined by decomposition algorithm","Task dependencies are identified but not validated against actual implementation constraints"],"requires":["Specification document (specs/NNN-feature/spec.md)","AI agent configured in INTEGRATION_REGISTRY","Project context (team size, velocity, tech stack)","Write access to specs/ directory"],"input_types":["Specification document (Markdown)","Project context (team metrics, tech stack)","Historical task data (for estimation calibration)"],"output_types":["Plan document with phases and dependencies (Markdown)","Task list with acceptance criteria and estimates (Markdown)","Task dependency graph","Effort and complexity estimates"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-github--spec-kit__cap_6","uri":"capability://automation.workflow.project.initialization.with.language.and.framework.detection","name":"project initialization with language and framework detection","description":"Implements the specify init command that bootstraps a new Spec Kit project by detecting project language, framework, and architecture. The initialization process creates the .specify/ directory structure, initializes configuration files, and sets up the INTEGRATION_REGISTRY for the detected tech stack. The system analyzes existing code (if present) to infer project structure, dependencies, and conventions, then generates a constitution document (.specify/memory/constitution.md) that captures project context. Initialization supports multiple languages (Python, JavaScript, Go, Rust, etc.) and frameworks (Django, FastAPI, React, etc.) with language-specific templates and presets.","intents":["I want to set up Spec Kit in a new project without manual configuration","I need Spec Kit to understand my project's language, framework, and architecture automatically","I want to generate a constitution document that captures project context for AI agents","I need to initialize Spec Kit in an existing project and preserve current structure"],"best_for":["Teams starting new projects with Spec Kit","Developers adding Spec Kit to existing projects","Organizations wanting standardized project initialization across multiple projects"],"limitations":["Language/framework detection relies on file patterns and package managers; polyglot projects may be misclassified","Constitution generation is based on static code analysis; dynamic or runtime configuration is not captured","Initialization creates default presets and extensions; customization requires manual editing","No rollback mechanism if initialization fails partway through"],"requires":["Git repository initialized in project root","Python 3.9+ (for Spec Kit itself)","Write access to project root directory","Optional: existing project code (for context inference)"],"input_types":["Project root directory","Existing code (for language/framework detection)","Optional: initialization flags (language, framework override)"],"output_types":[".specify/ directory structure","Configuration files (YAML/JSON)","Constitution document (.specify/memory/constitution.md)","Preset and extension catalogs"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-github--spec-kit__cap_7","uri":"capability://memory.knowledge.constitution.based.project.context.management.for.ai.agents","name":"constitution-based project context management for ai agents","description":"Implements the /speckit.constitution slash command that generates a constitution document (.specify/memory/constitution.md) capturing project-wide context, conventions, and constraints. The constitution serves as a persistent knowledge base that AI agents reference during all development phases. It includes project metadata (name, language, framework), architectural patterns, coding standards, directory structure, key dependencies, and project-specific constraints. The constitution is generated once during initialization and can be updated via the /speckit.constitution command. All downstream phases (specification, planning, implementation) include the constitution in agent context, ensuring consistency across AI-assisted development.","intents":["I want to capture project conventions and constraints in a machine-readable format for AI agents","I need AI agents to understand project architecture and coding standards without explicit prompting","I want to maintain a single source of truth for project context across all development phases","I need to update project context when architecture or conventions change"],"best_for":["Teams using AI agents for code generation who want to enforce project conventions","Projects with complex architecture or strict coding standards","Organizations wanting to standardize project context across multiple projects"],"limitations":["Constitution is generated once and requires manual updates if project structure changes","No built-in validation of constitution content; invalid sections are only caught when agents reference them","Constitution size is limited by agent context windows; large projects may need to summarize or split context","No versioning or change tracking for constitution updates"],"requires":["Project initialized with specify init","AI agent configured in INTEGRATION_REGISTRY","Write access to .specify/memory/ directory"],"input_types":["Project code and structure","Project metadata (name, language, framework)","Architectural documentation","Coding standards and conventions"],"output_types":["Constitution document (.specify/memory/constitution.md)","Project metadata (JSON)","Architectural patterns and constraints","Coding standards and conventions"],"categories":["memory-knowledge","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-github--spec-kit__cap_8","uri":"capability://automation.workflow.workflow.definition.and.execution.with.step.based.orchestration","name":"workflow definition and execution with step-based orchestration","description":"Implements a Workflow System that allows teams to define custom development workflows as YAML files with typed step definitions. Each workflow step has a type (e.g., 'specify', 'plan', 'implement', 'test', 'deploy') and can execute custom commands, invoke AI agents, or trigger external tools. Steps support conditional execution, error handling, and output passing between steps. The workflow engine maintains execution state in .specify/workflows/, enabling resumable execution and rollback. Workflows can be composed from reusable step templates and published to the community catalog for sharing. The system supports both sequential and parallel step execution (where applicable).","intents":["I want to define a custom development workflow that combines Spec Kit phases with our internal tools","I need to automate testing, deployment, and validation as part of the specification-to-code pipeline","I want to create reusable workflows that other teams can adopt with minimal customization","I need to handle errors and edge cases in multi-step workflows with clear recovery paths"],"best_for":["Teams with custom development processes wanting to automate them end-to-end","Organizations building internal developer platforms on top of Spec Kit","Open-source contributors creating reusable workflows for common development patterns"],"limitations":["Workflow execution is sequential by default; parallel step execution requires explicit configuration and careful dependency management","Error handling is step-level; no built-in transaction semantics or distributed rollback","Workflow state is stored locally; no support for distributed workflow execution across multiple machines","Conditional logic in workflows is limited to simple boolean expressions; complex branching requires custom step implementations"],"requires":["Workflow definition (YAML)","Step type implementations (custom commands or extensions)","Write access to .specify/workflows/ directory","All tools/agents referenced in workflow steps must be configured"],"input_types":["Workflow definition (YAML)","Step type definitions","Project context and specifications","External tool configurations"],"output_types":["Workflow execution logs","Step output artifacts","Workflow state checkpoints","Final implementation and deployment artifacts"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-github--spec-kit__cap_9","uri":"capability://safety.moderation.specification.validation.and.consistency.checking.across.phases","name":"specification validation and consistency checking across phases","description":"Implements the specify check and specify version commands that validate specifications for completeness, consistency, and compatibility with downstream phases. The validation system checks that specifications include required sections (API contracts, data models, acceptance criteria), validates that task lists align with specification scope, and ensures implementation artifacts match specification requirements. The version command tracks specification versions and detects breaking changes when specifications are updated. Validation rules can be customized via extensions, allowing teams to enforce domain-specific constraints (e.g., API versioning policies, security requirements).","intents":["I want to validate specifications before passing them to AI agents for implementation","I need to detect breaking changes when specifications are updated mid-development","I want to ensure task lists and implementation artifacts align with specification requirements","I need to enforce domain-specific constraints (API versioning, security policies) across specifications"],"best_for":["Teams wanting to catch specification issues before implementation begins","Organizations with strict compliance or security requirements","Projects with long development cycles where specification drift is a concern"],"limitations":["Validation rules are static; no semantic validation of specification content (e.g., API contract correctness)","Version tracking is manual; no automatic detection of specification changes","Validation errors are reported but not automatically fixed; remediation requires manual editing","No integration with specification review workflows or approval gates"],"requires":["Specification document (specs/NNN-feature/spec.md)","Validation rules (built-in or custom via extensions)","Write access to .specify/ directory (for version tracking)"],"input_types":["Specification document (Markdown)","Task list (Markdown)","Implementation artifacts (code)","Validation rules (JSON/YAML)"],"output_types":["Validation report (text/JSON)","List of issues and warnings","Version compatibility report","Remediation suggestions"],"categories":["safety-moderation","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-github--spec-kit__headline","uri":"capability://tool.use.integration.spec.driven.development.toolkit.for.ai.assisted.software.development","name":"spec-driven development toolkit for ai-assisted software development","description":"Spec Kit is an open-source framework designed to facilitate Spec-Driven Development, enabling developers to create executable specifications that directly generate code through AI agents, streamlining the software development process.","intents":["best Spec-Driven Development toolkit","Spec-Driven Development framework for AI-assisted coding","open-source tools for Spec-Driven Development","how to implement Spec-Driven Development in projects","AI tools for generating code from specifications"],"best_for":[],"limitations":[],"requires":[],"input_types":[],"output_types":[],"categories":["tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":57,"verified":false,"data_access_risk":"high","permissions":["Python 3.9+","Git repository initialized in project root","At least one supported AI agent configured (30+ agents in INTEGRATION_REGISTRY)","Write access to .specify/ and specs/ directories","API credentials for target AI agent (OpenAI API key, Anthropic API key, etc.)","Agent-specific SDK or API client installed","Agent entry in INTEGRATION_REGISTRY (or custom agent definition)","Write access to .specify/agents/{agent_name}/ directory","Internet access to fetch community catalogs","Git credentials for publishing presets/extensions (optional)"],"failure_modes":["Resumable workflow state is stored locally in .specify/ directory — no built-in distributed state management for team collaboration","Phase transitions are sequential; no parallel execution of workflow steps","Requires explicit agent integration via INTEGRATION_REGISTRY — not all AI agents have native support","Template resolution follows a fixed precedence order (project → preset → default) with no custom override hooks","Agent context updates are pull-based (triggered by specify CLI commands) rather than event-driven — no real-time context sync","No built-in conflict resolution if multiple agents modify the same files simultaneously","Agent-specific capabilities (e.g., tool calling, streaming) require custom integration code","Context size limits vary by agent; no automatic context pruning or summarization for large projects","Catalog is centralized (GitHub-hosted); no support for private or organization-specific catalogs","No built-in security scanning for community-contributed presets/extensions; teams must review code before use","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.8855878844600458,"quality":0.35,"ecosystem":0.6000000000000001,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.3,"quality":0.2,"ecosystem":0.15,"match_graph":0.23,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:21.550Z","last_scraped_at":"2026-05-03T13:58:39.623Z","last_commit":"2026-05-01T21:18:19Z"},"community":{"stars":92269,"forks":7990,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=github--spec-kit","compare_url":"https://unfragile.ai/compare?artifact=github--spec-kit"}},"signature":"LHYtiEQYQWE2kEZjKgh1q09/uve6udO1yL8PmKSqVofciwCCCVvHc9cYvPxTKlQrb7Q9gJY8wwSei7/8v/FBDw==","signedAt":"2026-06-20T14:47:56.023Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/github--spec-kit","artifact":"https://unfragile.ai/github--spec-kit","verify":"https://unfragile.ai/api/v1/verify?slug=github--spec-kit","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"}}