{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github-github--awesome-copilot","slug":"github--awesome-copilot","name":"awesome-copilot","type":"repo","url":"https://awesome-copilot.github.com/","page_url":"https://unfragile.ai/github--awesome-copilot","categories":["code-editors"],"tags":["agent-skills","agents","ai","awesome","custom-agents","github-copilot","hacktoberfest","prompt-engineering"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github-github--awesome-copilot__cap_0","uri":"capability://tool.use.integration.custom.agent.definition.and.mcp.server.integration","name":"custom agent definition and mcp server integration","description":"Enables creation of domain-specific agents through a markdown-based agent definition format (.agent.md) that integrates with GitHub Copilot via MCP (Model Context Protocol) servers. Agents are installed and activated through a registry system that maps agent metadata (name, description, capabilities) to executable MCP server bindings, allowing Copilot to invoke specialized behavior for specific technologies (e.g., Terraform, ARM migration). The architecture supports both built-in agents and external plugin-based agents through a plugin manifest system.","intents":["Create a specialized agent for Terraform infrastructure-as-code workflows that Copilot can invoke","Build a custom agent for ARM architecture migration that understands domain-specific patterns","Integrate an external MCP server as a Copilot agent without modifying core Copilot","Distribute custom agents across an organization's development teams via a marketplace"],"best_for":["Enterprise teams standardizing on specific technology stacks (Terraform, Kubernetes, etc.)","Organizations building internal developer platforms with Copilot integration","Open-source projects creating community-contributed specialized agents"],"limitations":["Agent activation requires manual installation or organization-level policy configuration","MCP server integration adds latency for agent discovery and initialization (~500ms per agent activation)","No built-in versioning or rollback mechanism for agent updates — relies on Git-based version control","Agent context is limited by Copilot's token window; large codebases may exceed context limits"],"requires":["GitHub Copilot IDE extension (VS Code, JetBrains, etc.)","MCP server implementation (Node.js, Python, or other supported runtime)","Agent definition file in .agent.md format with valid frontmatter metadata","Access to awesome-copilot repository or plugin marketplace for distribution"],"input_types":["Markdown agent definition files (.agent.md)","MCP server configuration and bindings","Plugin manifest JSON (plugin.json)"],"output_types":["Executable agent registered in Copilot's agent registry","MCP server endpoints callable from Copilot chat and code completion","Agent metadata published to marketplace discovery system"],"categories":["tool-use-integration","agent-architecture"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-github--awesome-copilot__cap_1","uri":"capability://code.generation.editing.skill.based.capability.composition.with.asset.bundling","name":"skill-based capability composition with asset bundling","description":"Provides a modular skill system where discrete capabilities (e.g., 'sponsor finder', 'fabric lakehouse integration') are packaged as reusable units with SKILL.md format, including embedded prompts, examples, and asset bundles (code snippets, configuration templates). Skills are discoverable through a skills registry and can be composed into agents or used standalone within Copilot. The SKILL.md format enforces structured metadata (name, description, use cases, examples) and supports asset bundling for context-aware code generation.","intents":["Create a reusable 'sponsor finder' skill that Copilot can invoke to identify GitHub sponsors in a codebase","Package a Fabric Lakehouse integration skill with pre-built SQL templates and configuration examples","Compose multiple skills into a single agent for complex workflows (e.g., infrastructure + security audit)","Share specialized skills across teams without duplicating prompt engineering effort"],"best_for":["Teams building libraries of reusable Copilot capabilities","Organizations standardizing on specific tools (Fabric, Databricks, etc.) with Copilot","Open-source projects creating skill marketplaces for domain-specific tasks"],"limitations":["Skills are stateless — no built-in persistence or state management between invocations","Asset bundling increases skill package size; large asset sets may exceed Copilot's context window","No automatic skill versioning or dependency resolution — conflicts must be resolved manually","Skill discovery relies on metadata accuracy; poorly documented skills are hard to find in marketplace"],"requires":["SKILL.md file with valid frontmatter (name, description, use cases, examples)","Asset files (code snippets, templates, configuration) in skill directory","Metadata extraction pipeline to index skills in discovery system","GitHub Copilot IDE extension to invoke skills"],"input_types":["SKILL.md markdown files with YAML frontmatter","Asset files (Python, JavaScript, SQL, YAML, JSON)","Example code and configuration templates"],"output_types":["Indexed skill metadata in marketplace","Copilot-invokable skill capabilities","Generated code using skill templates and examples"],"categories":["code-generation-editing","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-github--awesome-copilot__cap_10","uri":"capability://text.generation.language.documentation.generation.and.learning.hub.with.cookbook.examples","name":"documentation generation and learning hub with cookbook examples","description":"Provides automated documentation generation from content metadata and a learning hub with cookbook examples demonstrating how to use agents, skills, and workflows. The documentation pipeline generates API documentation, usage guides, and examples from content files, while the learning hub curates best practices and real-world examples. The system supports multiple documentation formats (Markdown, HTML) and integrates with a website (Astro-based) for publishing.","intents":["Generate API documentation for custom agents automatically from agent definitions","Create a learning hub with cookbook examples showing how to compose agents and skills","Publish usage guides and best practices for common Copilot customization patterns","Maintain up-to-date documentation that stays in sync with content changes"],"best_for":["Open-source projects documenting community-contributed content","Organizations building internal documentation for custom agents and skills","Teams creating learning resources for Copilot customization"],"limitations":["Documentation generation is template-based; complex documentation requires manual writing","Learning hub examples must be manually curated; no automatic example generation","Documentation pipeline adds latency to content publishing; changes take time to appear","Website hosting and maintenance required for documentation distribution"],"requires":["Content files with metadata for documentation generation","Documentation templates (Markdown, HTML)","Astro-based website for publishing documentation","Build pipeline to generate documentation from content"],"input_types":["Content files (.agent.md, SKILL.md, .instructions.md, etc.) with metadata","Cookbook examples (code, configuration, usage patterns)","Documentation templates"],"output_types":["Generated API documentation","Usage guides and best practices","Learning hub with cookbook examples","Published website with searchable documentation"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-github--awesome-copilot__cap_11","uri":"capability://data.processing.analysis.contributor.recognition.system.with.attribution.and.metrics","name":"contributor recognition system with attribution and metrics","description":"Provides automated contributor recognition and attribution by extracting Git history, tracking contributions across content types, and generating contributor reports. The system maintains a contributor database (.all-contributorsrc) with attribution metadata and generates contributor recognition in documentation and marketplace. Metrics track contribution volume, content quality, and community impact.","intents":["Automatically recognize contributors in README and documentation based on Git history","Track contribution metrics (number of agents, skills, instructions created) per contributor","Generate contributor reports showing community engagement and impact","Maintain a contributor database for recognition and community management"],"best_for":["Open-source projects recognizing community contributors","Organizations tracking internal contributor metrics and engagement","Communities building contributor recognition programs"],"limitations":["Attribution is based on Git history; contributors without Git commits are not recognized","Metrics are quantitative (volume); qualitative impact (usefulness, adoption) is not measured","Contributor database must be manually maintained for non-Git contributors","No built-in incentive system; recognition is informational only"],"requires":["Git repository with contributor history",".all-contributorsrc file for contributor database","Build scripts to extract Git history and generate reports","Documentation templates for contributor recognition"],"input_types":["Git history (commits, authors, dates)",".all-contributorsrc contributor database","Content files for contribution tracking"],"output_types":["Contributor reports with metrics","Attribution in documentation and marketplace","Contributor database with metadata"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-github--awesome-copilot__cap_12","uri":"capability://search.retrieval.website.and.discovery.platform.with.full.text.search.and.filtering","name":"website and discovery platform with full-text search and filtering","description":"Provides a modern, searchable website (Astro-based) for discovering and exploring agents, skills, instructions, workflows, and plugins. The website includes full-text search powered by Pagefind, filtering by category/language/technology, and a responsive UI for browsing content. The platform integrates with the marketplace discovery system and learning hub to provide a unified discovery experience.","intents":["Search for 'Terraform' agents and skills on the awesome-copilot website","Filter content by programming language (Python, JavaScript) and technology (Kubernetes, AWS)","Browse trending agents and skills to discover new capabilities","Access learning hub and cookbook examples directly from the website"],"best_for":["Teams discovering community-contributed Copilot customizations","Organizations hosting internal discovery platforms","Developers exploring available capabilities before building custom content"],"limitations":["Website hosting and maintenance required; no built-in CDN or caching","Full-text search (Pagefind) adds client-side latency; large content sets may be slow","Filtering is based on metadata; complex queries require manual filtering","Website is static; real-time updates require rebuild and redeploy"],"requires":["Astro-based website framework","Pagefind full-text search integration","Marketplace metadata indexes","Website hosting (GitHub Pages, Vercel, etc.)"],"input_types":["Marketplace metadata indexes","Content files for indexing","Search queries (text, filters)"],"output_types":["Rendered website with search and filtering","Search results with ranking","Content pages with metadata and examples"],"categories":["search-retrieval","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-github--awesome-copilot__cap_13","uri":"capability://automation.workflow.content.contribution.workflow.with.quality.review.and.merge.automation","name":"content contribution workflow with quality review and merge automation","description":"Provides a structured contribution workflow for submitting new agents, skills, instructions, and workflows through pull requests with automated quality checks, community review, and merge automation. The workflow includes contribution guidelines, templates for each content type, automated validation, and a review process that ensures quality before merging. Merge automation handles contributor recognition, documentation updates, and marketplace indexing.","intents":["Submit a new Terraform agent to awesome-copilot with automated validation and community review","Create a new skill with templates and guidelines to ensure quality and consistency","Review and merge community contributions with automated quality checks and contributor recognition","Maintain contribution guidelines and templates as content types evolve"],"best_for":["Open-source projects managing community contributions at scale","Organizations maintaining internal content repositories with quality standards","Teams automating contribution review and merge processes"],"limitations":["Contribution workflow adds friction; complex submissions may require multiple review rounds","Automated validation catches structural issues but not subjective quality (usefulness, clarity)","Merge automation requires careful configuration; errors can corrupt marketplace indexes","Contributor experience depends on review speed; slow reviews discourage contributions"],"requires":["Contribution guidelines and templates for each content type","Pull request templates with checklists","Automated validation workflows (GitHub Actions)","Review process and merge automation scripts"],"input_types":["Pull requests with new content","Content files (.agent.md, SKILL.md, .instructions.md, etc.)","Contributor information and attribution"],"output_types":["Validated content with quality checks","Community review feedback","Merged content with contributor recognition","Updated marketplace indexes and documentation"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-github--awesome-copilot__cap_2","uri":"capability://code.generation.editing.custom.instruction.injection.with.language.specific.context.management","name":"custom instruction injection with language-specific context management","description":"Allows injection of custom instructions into Copilot's behavior through .instructions.md files with YAML frontmatter, supporting language-specific instructions (Python, JavaScript, Go, etc.) and context management strategies. Instructions are applied globally or scoped to specific file types/projects, enabling teams to enforce coding standards, architectural patterns (OOP design patterns), and domain-specific conventions without modifying Copilot's core behavior. The instruction system integrates with Copilot's prompt context management to prioritize instructions based on file type and project configuration.","intents":["Enforce OOP design patterns (Factory, Singleton, Observer) across a Python codebase via Copilot","Apply language-specific coding standards (PEP 8 for Python, ESLint rules for JavaScript) to all Copilot suggestions","Inject architectural guidelines (microservices patterns, API design conventions) into code generation","Distribute organization-wide coding standards to developers without manual documentation"],"best_for":["Enterprise teams enforcing architectural standards and design patterns","Organizations with language-specific coding conventions (style guides, linting rules)","Teams migrating codebases to new architectural patterns (monolith to microservices)"],"limitations":["Instructions are applied at Copilot invocation time; no runtime enforcement or validation","Conflicting instructions (e.g., two design pattern guidelines) are not automatically resolved","Context management adds ~100-200ms latency per Copilot suggestion due to instruction prioritization","Language-specific instructions require manual maintenance as language versions evolve"],"requires":[".instructions.md file with YAML frontmatter (name, description, languages, patterns)","GitHub Copilot IDE extension with instruction support","Project configuration (.copilot.yml or similar) to scope instructions to file types","Access to awesome-copilot repository or organization's instruction registry"],"input_types":["Markdown instruction files (.instructions.md)","YAML frontmatter with language tags and pattern definitions","Project configuration files specifying instruction scope"],"output_types":["Copilot code suggestions adhering to injected instructions","Indexed instructions in discovery system","Applied instructions in Copilot's context window"],"categories":["code-generation-editing","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-github--awesome-copilot__cap_3","uri":"capability://text.generation.language.prompt.file.system.with.task.specific.template.composition","name":"prompt file system with task-specific template composition","description":"Provides a structured prompt file system (.prompt.md format) with quality standards and task-specific templates that enable composition of reusable prompt fragments for common Copilot tasks (code review, refactoring, documentation generation). Prompts are indexed by task type and can be combined to create complex multi-step workflows. The system enforces prompt quality standards (clarity, specificity, examples) and includes a validation pipeline to ensure prompts meet organizational guidelines before distribution.","intents":["Create a reusable code review prompt that Copilot applies consistently across pull requests","Compose a multi-step refactoring prompt that guides Copilot through legacy code modernization","Build a documentation generation prompt with examples for API documentation, README files, etc.","Standardize prompt quality across teams using validation gates and quality metrics"],"best_for":["Teams standardizing Copilot usage patterns across projects","Organizations building prompt libraries for common development tasks","Projects requiring consistent code review and documentation standards"],"limitations":["Prompt composition adds complexity; poorly composed prompts may produce inconsistent results","No built-in A/B testing or prompt evaluation metrics — quality assessment is manual","Task-specific templates may not generalize to novel use cases outside their design scope","Prompt versioning relies on Git; no built-in rollback or A/B testing infrastructure"],"requires":[".prompt.md files with YAML frontmatter (task type, description, examples, quality metrics)","Validation pipeline to enforce quality standards (clarity, specificity, example coverage)","GitHub Copilot IDE extension to invoke prompts","Access to prompt registry for discovery and composition"],"input_types":["Markdown prompt files (.prompt.md)","YAML frontmatter with task type and quality metadata","Code context and examples for prompt composition"],"output_types":["Composed prompts sent to Copilot","Copilot responses following prompt guidance","Indexed prompts in discovery system with quality metrics"],"categories":["text-generation-language","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-github--awesome-copilot__cap_4","uri":"capability://planning.reasoning.agentic.workflow.orchestration.with.dag.based.task.planning","name":"agentic workflow orchestration with dag-based task planning","description":"Enables definition of multi-step agentic workflows using a declarative workflow definition format that supports DAG (directed acyclic graph) task planning, phase-based execution, and event-driven hooks. Workflows coordinate multiple agents and skills to accomplish complex tasks (e.g., infrastructure provisioning, code migration) with explicit task dependencies, error handling, and security boundaries. The architecture supports the Ralph Loop pattern (Reasoning → Action → Learning → Feedback) for iterative task execution and includes hooks for event-driven automation (pre/post-task, on-error).","intents":["Define a multi-phase infrastructure provisioning workflow that coordinates Terraform agent, security audit agent, and deployment agent","Create a code migration workflow that plans tasks as a DAG, executes them in dependency order, and handles failures gracefully","Implement a Ralph Loop pattern for iterative code improvement where Copilot reasons about changes, executes them, learns from results, and refines","Trigger automated workflows on Git events (push, PR) using hooks and event-driven automation"],"best_for":["Teams automating complex multi-step development tasks (infrastructure, migrations, audits)","Organizations building internal developer platforms with Copilot-driven automation","Projects requiring iterative AI-driven improvement with feedback loops"],"limitations":["DAG execution adds latency for task planning and dependency resolution (~1-2 seconds per workflow)","No built-in distributed execution; workflows run sequentially on a single machine","Error handling is declarative but not automatic — failed tasks require manual intervention or explicit retry logic","Ralph Loop pattern requires careful prompt engineering to avoid infinite loops or divergent reasoning"],"requires":["Workflow definition file in YAML or JSON format with task DAG and phase definitions","Hook configuration for event-driven automation (pre/post-task, on-error scripts)","Agent and skill definitions for tasks in the workflow","GitHub Copilot IDE extension and awesome-copilot runtime to execute workflows"],"input_types":["Workflow definition files (YAML/JSON) with task DAG and phase structure","Hook configuration scripts (bash, Python, JavaScript)","Agent and skill definitions referenced in workflow tasks"],"output_types":["Executed workflow with task results and logs","Generated code/infrastructure from workflow tasks","Event-driven automation triggered by hooks"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-github--awesome-copilot__cap_5","uri":"capability://automation.workflow.hook.based.event.driven.automation.with.pre.post.task.execution","name":"hook-based event-driven automation with pre/post-task execution","description":"Provides a hook system for event-driven automation that triggers scripts (bash, Python, JavaScript) on workflow events (pre-task, post-task, on-error, on-success). Hooks enable integration with external systems (CI/CD, monitoring, notifications) and allow teams to extend workflow behavior without modifying core workflow definitions. The architecture supports hook chaining (sequential execution) and conditional execution based on task results.","intents":["Trigger a Slack notification when a workflow task fails, including error logs and remediation steps","Run a security scan as a post-task hook after code generation to validate generated code","Execute a Git commit hook to automatically commit generated code with a specific message format","Chain multiple hooks to implement complex automation (e.g., generate code → scan → commit → deploy)"],"best_for":["Teams integrating Copilot workflows with CI/CD pipelines and monitoring systems","Organizations automating notifications and alerting for AI-driven tasks","Projects requiring security validation or compliance checks on generated code"],"limitations":["Hook execution is synchronous; long-running hooks block workflow progress","No built-in retry logic for failed hooks — failures propagate to workflow","Hook scripts must be stored in repository; no external hook registry or marketplace","Conditional hook execution requires explicit logic in hook scripts; no declarative conditions"],"requires":["Hook configuration in workflow definition (pre-task, post-task, on-error, on-success events)","Hook script files (bash, Python, JavaScript) in repository","External system credentials (Slack API token, Git credentials, etc.) for hook integration","awesome-copilot runtime to execute hooks"],"input_types":["Hook configuration in workflow YAML/JSON","Hook script files (bash, Python, JavaScript)","Task results and context passed to hooks"],"output_types":["Hook execution results (logs, exit codes)","External system updates (Slack messages, Git commits, CI/CD triggers)","Workflow state changes based on hook results"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-github--awesome-copilot__cap_6","uri":"capability://tool.use.integration.plugin.system.with.marketplace.discovery.and.external.plugin.integration","name":"plugin system with marketplace discovery and external plugin integration","description":"Provides a plugin architecture that enables external developers to create and distribute plugins (agents, skills, instructions, workflows) through a centralized marketplace. Plugins are defined via plugin.json manifest files with metadata (name, description, version, dependencies) and can be installed into awesome-copilot via a plugin registry. The system supports plugin versioning, dependency resolution, and external plugin integration without requiring changes to core awesome-copilot code.","intents":["Create a third-party plugin for Kubernetes management that extends awesome-copilot with K8s-specific agents and skills","Publish a plugin to the awesome-copilot marketplace so other teams can discover and install it","Integrate an external plugin that depends on other plugins, with automatic dependency resolution","Build an organization-specific plugin registry for internal plugins not suitable for public distribution"],"best_for":["Third-party developers building Copilot extensions for specific technologies","Organizations creating internal plugin registries for proprietary tools","Open-source projects distributing Copilot customizations via marketplace"],"limitations":["Plugin dependency resolution is manual; no automatic conflict detection or version negotiation","Plugin security is not enforced; plugins can execute arbitrary code with full system access","Plugin marketplace discovery relies on metadata accuracy; poorly documented plugins are hard to find","No built-in plugin sandboxing; malicious plugins can compromise the development environment"],"requires":["plugin.json manifest file with metadata (name, description, version, dependencies, entry points)","Plugin content (agents, skills, instructions, workflows) in plugin directory","Access to awesome-copilot marketplace or organization's plugin registry","awesome-copilot runtime to load and execute plugins"],"input_types":["plugin.json manifest files","Plugin content files (agents, skills, instructions, workflows)","Plugin metadata and documentation"],"output_types":["Installed plugins in awesome-copilot environment","Indexed plugins in marketplace discovery system","Plugin-provided agents, skills, instructions available to Copilot"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-github--awesome-copilot__cap_7","uri":"capability://planning.reasoning.multi.agent.orchestration.with.gem.team.pattern.and.phase.based.execution","name":"multi-agent orchestration with gem team pattern and phase-based execution","description":"Implements advanced multi-agent orchestration using the GEM Team pattern (Group, Expand, Merge) combined with phase-based workflow execution. Multiple specialized agents (e.g., architect, implementer, reviewer) work in coordinated phases, with explicit handoff points and context sharing between agents. The architecture supports task decomposition into agent-specific responsibilities and merges results from parallel agent execution, enabling complex collaborative workflows.","intents":["Orchestrate a code review workflow where architect agent designs changes, implementer agent generates code, and reviewer agent validates quality","Decompose a large refactoring task into parallel agent work (one agent per module) and merge results","Implement a GEM Team pattern where agents group related tasks, expand into detailed plans, and merge results","Coordinate agents with explicit phase transitions and context sharing between phases"],"best_for":["Large teams automating complex collaborative workflows with multiple specialized agents","Organizations decomposing large tasks into parallel agent work","Projects requiring explicit handoff points and context sharing between agents"],"limitations":["Phase-based execution adds latency for context sharing and handoff (~500ms-1s per phase transition)","Merging results from parallel agents requires explicit merge logic; conflicts are not automatically resolved","GEM Team pattern requires careful task decomposition; poorly decomposed tasks may not parallelize effectively","Context sharing between agents is limited by Copilot's token window; large contexts may exceed limits"],"requires":["Workflow definition with GEM Team pattern and phase structure","Multiple agent definitions for specialized roles (architect, implementer, reviewer, etc.)","Task decomposition strategy for parallel agent execution","Merge logic for combining results from parallel agents"],"input_types":["Workflow definition with GEM Team pattern","Task descriptions for decomposition","Agent definitions for specialized roles"],"output_types":["Executed workflow with phase results","Merged results from parallel agent execution","Context shared between agents at phase transitions"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-github--awesome-copilot__cap_8","uri":"capability://search.retrieval.marketplace.discovery.and.search.system.with.metadata.indexing","name":"marketplace discovery and search system with metadata indexing","description":"Provides a searchable marketplace for discovering agents, skills, instructions, workflows, and plugins through a metadata-driven indexing system. The marketplace includes full-text search, filtering by category/language/technology, and ranking by popularity/recency. Metadata is extracted from content files (YAML frontmatter, README files) and indexed by a build pipeline, enabling fast discovery without requiring manual curation.","intents":["Search for Terraform agents in the marketplace to find community-contributed infrastructure automation","Filter skills by language (Python, JavaScript) and technology (Fabric, Databricks) to find relevant capabilities","Discover design pattern instructions for OOP patterns commonly used in your tech stack","Browse trending plugins and agents to stay updated on community contributions"],"best_for":["Teams discovering community-contributed Copilot customizations","Organizations building internal marketplaces for proprietary agents/skills","Developers exploring available capabilities before building custom agents"],"limitations":["Metadata accuracy depends on contributor quality; poorly documented content is hard to discover","Search ranking is based on popularity/recency; niche but high-quality content may be buried","No built-in content quality rating system; users cannot distinguish high-quality from low-quality contributions","Marketplace indexing is batch-based; new contributions take time to appear in search results"],"requires":["Content files with valid YAML frontmatter (name, description, tags, categories)","Build pipeline to extract metadata and index content","Search infrastructure (full-text search engine, filtering, ranking)","Website/UI to display marketplace and search results"],"input_types":["Content files (.agent.md, SKILL.md, .instructions.md, etc.) with YAML frontmatter","README files with descriptions and examples","Search queries (text, filters, categories)"],"output_types":["Indexed metadata in search system","Search results with ranking and filtering","Marketplace UI displaying discoverable content"],"categories":["search-retrieval","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-github--awesome-copilot__cap_9","uri":"capability://automation.workflow.build.pipeline.with.validation.workflows.and.quality.gates","name":"build pipeline with validation workflows and quality gates","description":"Implements a comprehensive build pipeline that validates content quality, extracts metadata, generates marketplace indexes, and enforces quality gates before publishing. The pipeline includes frontmatter validation, documentation generation, marketplace generation, and contributor recognition. Quality gates ensure content meets standards (metadata completeness, example coverage, clarity) before distribution, preventing low-quality contributions from reaching users.","intents":["Validate that all agent definitions include required metadata (name, description, examples) before merging","Automatically generate marketplace indexes from content metadata without manual curation","Enforce quality standards (clarity, specificity, example coverage) on prompts and instructions","Generate contributor recognition and attribution automatically from Git history"],"best_for":["Open-source projects maintaining quality standards across community contributions","Organizations enforcing consistent metadata and documentation standards","Teams automating marketplace generation and content indexing"],"limitations":["Quality gate enforcement is rule-based; subjective quality issues (clarity, usefulness) are not detected","Build pipeline adds latency to contribution workflow; contributors must wait for validation","Validation rules must be maintained as content types evolve; outdated rules may reject valid content","No built-in feedback mechanism for contributors to understand why their content failed validation"],"requires":["GitHub Actions workflows for build pipeline automation","Validation rules for metadata completeness and quality standards","Metadata extraction scripts (frontmatter parsing, README analysis)","Marketplace generation scripts to create indexes and discovery data"],"input_types":["Content files (.agent.md, SKILL.md, .instructions.md, etc.)","YAML frontmatter with metadata","Git history for contributor attribution"],"output_types":["Validation results (pass/fail with error messages)","Extracted metadata indexes","Generated marketplace data","Contributor recognition and attribution"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":54,"verified":false,"data_access_risk":"high","permissions":["GitHub Copilot IDE extension (VS Code, JetBrains, etc.)","MCP server implementation (Node.js, Python, or other supported runtime)","Agent definition file in .agent.md format with valid frontmatter metadata","Access to awesome-copilot repository or plugin marketplace for distribution","SKILL.md file with valid frontmatter (name, description, use cases, examples)","Asset files (code snippets, templates, configuration) in skill directory","Metadata extraction pipeline to index skills in discovery system","GitHub Copilot IDE extension to invoke skills","Content files with metadata for documentation generation","Documentation templates (Markdown, HTML)"],"failure_modes":["Agent activation requires manual installation or organization-level policy configuration","MCP server integration adds latency for agent discovery and initialization (~500ms per agent activation)","No built-in versioning or rollback mechanism for agent updates — relies on Git-based version control","Agent context is limited by Copilot's token window; large codebases may exceed context limits","Skills are stateless — no built-in persistence or state management between invocations","Asset bundling increases skill package size; large asset sets may exceed Copilot's context window","No automatic skill versioning or dependency resolution — conflicts must be resolved manually","Skill discovery relies on metadata accuracy; poorly documented skills are hard to find in marketplace","Documentation generation is template-based; complex documentation requires manual writing","Learning hub examples must be manually curated; no automatic example generation","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7871283104074772,"quality":0.5,"ecosystem":0.6000000000000001,"match_graph":0.25,"freshness":0.75,"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":"2026-05-24T12:16:21.550Z","last_scraped_at":"2026-05-03T13:59:50.672Z","last_commit":"2026-05-02T19:44:45Z"},"community":{"stars":31994,"forks":3872,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=github--awesome-copilot","compare_url":"https://unfragile.ai/compare?artifact=github--awesome-copilot"}},"signature":"IdTFaQ/qxrWeCgzwBiTGcOr+0lAosj8A7oDzmU5fuiPObODa+KOSpE+5dbFosnffLMGoEkfdl3xMS+XwM708Aw==","signedAt":"2026-06-20T01:01:43.413Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/github--awesome-copilot","artifact":"https://unfragile.ai/github--awesome-copilot","verify":"https://unfragile.ai/api/v1/verify?slug=github--awesome-copilot","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"}}