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The agent reads issue descriptions, acceptance criteria, and linked context, then breaks work into discrete steps, executes them (via tool calls), and updates Linear with progress and results. Supports bidirectional updates so Linear remains the source of truth for project state.","intents":["I want my AI agent to pick up Linear issues and execute them without manual task breakdown","I need the agent to update Linear with progress and completion status automatically","I want to decompose a complex Linear issue into subtasks that the agent can parallelize"],"best_for":["engineering teams using Linear for project management who want AI agents to handle routine tasks","solo developers automating their own task execution pipeline","teams with repetitive work patterns (code reviews, documentation, testing) that can be templated"],"limitations":["Agent execution is limited to tasks that can be expressed as tool calls — complex creative work or human judgment calls require manual intervention","Linear API rate limits (300 requests/min) may throttle agent throughput on high-volume task execution","No built-in rollback mechanism if agent execution fails midway through a task — requires manual cleanup"],"requires":["Linear workspace with API access","Linear API key with read/write permissions","OpenClaw agent framework or compatible LLM agent","Tool registry with implementations for code execution, file operations, or API calls"],"input_types":["Linear issue JSON (title, description, acceptance criteria, labels)","linked attachments and comments","custom fields and metadata"],"output_types":["Linear issue updates (status, comments, linked PRs)","subtask creation in Linear","execution logs and results"],"categories":["planning-reasoning","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-jason-cyr--ai-agent-workflow__cap_2","uri":"capability://tool.use.integration.openclaw.agent.orchestration.and.tool.binding","name":"openclaw agent orchestration and tool binding","description":"Provides a runtime environment for executing AI agents with a standardized tool-calling interface. The system binds external tools (code execution, API calls, file operations) to the agent's action space, manages tool invocation with schema validation, and handles execution results. Supports multi-step reasoning where the agent chains tool calls together to accomplish complex workflows, with built-in error handling and retry logic.","intents":["I want my AI agent to call external tools and APIs in a structured, validated way","I need the agent to chain multiple tool calls together to complete a workflow","I want to add new tools to the agent without modifying core agent code"],"best_for":["developers building multi-step AI workflows that require tool orchestration","teams integrating AI agents into existing tool ecosystems (APIs, CLIs, databases)","builders who need a flexible tool registry without vendor lock-in"],"limitations":["Tool execution latency adds 100-500ms per tool call depending on network and tool complexity","Schema validation overhead increases token usage for each tool invocation — may impact cost on high-volume workflows","No built-in distributed execution — all tool calls run sequentially unless explicitly parallelized"],"requires":["OpenClaw framework installed","LLM API access (OpenAI, Anthropic, or local model)","Tool implementations (Python functions, HTTP endpoints, or CLI wrappers)","Python 3.9+ or Node.js 16+"],"input_types":["tool schema definitions (JSON Schema)","tool implementation code","agent prompts and context"],"output_types":["tool execution results","agent reasoning traces","structured action logs"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-jason-cyr--ai-agent-workflow__cap_3","uri":"capability://memory.knowledge.multi.tool.context.aggregation.for.agent.reasoning","name":"multi-tool context aggregation for agent reasoning","description":"Collects and synthesizes context from three separate systems (Obsidian notes, Linear issues, external APIs) into a unified context window that the agent uses for reasoning. The system performs relevance ranking, deduplication, and context prioritization to fit the agent's token budget while preserving critical information. Uses embedding-based retrieval to surface the most relevant knowledge from each source based on the current task.","intents":["I want the agent to have access to relevant context from all my tools without overwhelming its context window","I need the agent to understand relationships between my notes, tasks, and external data when making decisions","I want to control what context the agent sees for privacy or focus reasons"],"best_for":["teams with fragmented tool ecosystems who want unified AI agent context","knowledge workers managing context across multiple sources","builders optimizing agent performance by carefully curating context"],"limitations":["Embedding-based retrieval may miss relevant context if semantic similarity is low — requires tuning of similarity thresholds","Context aggregation adds 200-500ms latency per agent invocation due to retrieval and ranking","No built-in conflict resolution if the same information exists in multiple sources with different versions"],"requires":["Embedding model (OpenAI, local, or custom)","Vector database or in-memory index for retrieval","Connectors for each source system (Obsidian, Linear, external APIs)","Context window budget definition (e.g., max 4k tokens for context)"],"input_types":["Obsidian markdown notes","Linear issue data","External API responses","agent query or task description"],"output_types":["ranked context list","unified context string for agent prompt","context metadata (source, relevance score)"],"categories":["memory-knowledge","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-jason-cyr--ai-agent-workflow__cap_4","uri":"capability://memory.knowledge.persistent.agent.state.and.memory.management","name":"persistent agent state and memory management","description":"Maintains long-term memory of agent interactions, decisions, and learned patterns across multiple sessions. The system stores conversation history, task execution logs, and inferred preferences in a structured format, allowing the agent to reference past work and improve its behavior over time. Implements memory decay (older memories become less salient) and consolidation (frequent patterns are summarized) to manage memory growth.","intents":["I want my AI agent to remember what it did in previous sessions and learn from past mistakes","I need the agent to maintain context about my preferences and working style over time","I want to audit what the agent has done and why it made certain decisions"],"best_for":["teams deploying long-running AI agents that need continuity across sessions","solo developers building personal AI assistants that improve over time","organizations requiring audit trails of agent decisions for compliance"],"limitations":["Memory storage grows linearly with agent usage — requires periodic consolidation or archival to prevent bloat","Memory retrieval adds latency to each agent invocation — may require caching or indexing for performance","No built-in privacy controls — sensitive information in memory may need encryption or access controls"],"requires":["Persistent storage backend (database, file system, or vector store)","Memory schema definition (what to store, how to index)","Consolidation logic or scheduled cleanup jobs","Encryption if storing sensitive information"],"input_types":["agent interactions (prompts, responses, tool calls)","task execution logs","user feedback and corrections"],"output_types":["memory embeddings","consolidated memory summaries","audit logs"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-jason-cyr--ai-agent-workflow__cap_5","uri":"capability://automation.workflow.workflow.template.library.and.customization","name":"workflow template library and customization","description":"Provides pre-built workflow templates that connect Obsidian, Linear, and OpenClaw for common patterns (daily standup generation, issue triage, documentation updates). Templates are parameterized and extensible, allowing users to customize trigger conditions, tool bindings, and output formats without writing code. The system supports template composition, allowing complex workflows to be built by chaining simpler templates.","intents":["I want to set up a workflow without writing code — just customize a template for my use case","I need to create a custom workflow that combines multiple tools in a specific sequence","I want to share my workflow template with my team so they can reuse it"],"best_for":["non-technical users who want to automate workflows without coding","teams standardizing on workflow patterns across projects","builders creating reusable automation templates for their organizations"],"limitations":["Template flexibility is limited to parameterization — complex conditional logic requires code","Template composition may create hard-to-debug workflows if dependencies between templates are unclear","No built-in version control for templates — requires external Git or similar for tracking changes"],"requires":["Template definition format (YAML, JSON, or custom DSL)","Template engine that can parse and execute templates","Parameter validation and type checking","Documentation for available template variables and functions"],"input_types":["template definition files","parameter values","trigger events"],"output_types":["executed workflow results","workflow execution logs","customized template instances"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-jason-cyr--ai-agent-workflow__cap_6","uri":"capability://automation.workflow.trigger.based.workflow.execution.and.scheduling","name":"trigger-based workflow execution and scheduling","description":"Executes workflows in response to events (Linear issue created, Obsidian note updated, scheduled time) or manual triggers. The system maintains a trigger registry that maps events to workflow handlers, manages execution queues, and handles retries on failure. Supports both real-time event-driven execution and scheduled batch execution, with configurable concurrency limits to prevent resource exhaustion.","intents":["I want workflows to automatically run when specific events happen (e.g., new Linear issue)","I need to schedule workflows to run at specific times (e.g., daily standup generation)","I want to manually trigger a workflow and see the results in real-time"],"best_for":["teams automating routine workflows triggered by tool events","solo developers who want hands-off automation","organizations with scheduled reporting or maintenance tasks"],"limitations":["Event delivery latency depends on polling interval or webhook configuration — real-time execution may have 30-60 second delays","Concurrent workflow execution is limited by resource constraints — high-volume triggers may queue or fail","No built-in deduplication — duplicate events may trigger duplicate workflow executions"],"requires":["Event source integration (webhooks, polling, or API subscriptions)","Workflow execution engine","Job queue or scheduler (e.g., Bull, Celery, cron)","Error handling and retry logic"],"input_types":["event payloads (Linear webhook, Obsidian change event)","trigger configuration (conditions, filters)","workflow definitions"],"output_types":["workflow execution results","execution logs and metrics","error reports"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-jason-cyr--ai-agent-workflow__cap_7","uri":"capability://planning.reasoning.agent.decision.logging.and.explainability","name":"agent decision logging and explainability","description":"Captures detailed logs of agent reasoning, tool calls, and decisions, making the agent's behavior transparent and auditable. The system records the agent's thought process (chain-of-thought), tool invocations with inputs/outputs, and decision rationale. Logs are structured and queryable, allowing users to understand why the agent made a specific decision and to identify patterns or errors in agent behavior.","intents":["I want to understand why the agent made a specific decision or took an action","I need to audit the agent's behavior for compliance or debugging purposes","I want to identify patterns in agent errors so I can improve its prompts or tools"],"best_for":["teams deploying AI agents in production who need observability","organizations with compliance requirements for decision auditing","builders debugging agent behavior and improving performance"],"limitations":["Detailed logging increases storage requirements — may require archival or compression for long-term retention","Log analysis requires specialized tools or queries — raw logs are difficult to interpret manually","Sensitive information in logs (API keys, personal data) requires careful handling and encryption"],"requires":["Structured logging framework (e.g., JSON logging)","Log storage backend (file system, database, or log aggregation service)","Log querying and analysis tools","Encryption for sensitive data"],"input_types":["agent prompts and responses","tool invocations and results","decision points and reasoning"],"output_types":["structured execution logs","decision traces","audit reports"],"categories":["planning-reasoning","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":32,"verified":false,"data_access_risk":"high","permissions":["Obsidian 1.0+","Access to Obsidian vault directory or API","OpenClaw or compatible AI agent framework","Node.js 16+ for sync daemon","Linear workspace with API access","Linear API key with read/write permissions","OpenClaw agent framework or compatible LLM agent","Tool registry with implementations for code execution, file operations, or API calls","OpenClaw framework installed","LLM API access (OpenAI, Anthropic, or local model)"],"failure_modes":["Sync latency depends on polling interval or webhook configuration — real-time updates may have 30-60 second delays","Large vaults (10k+ notes) may require optimization to avoid memory bloat in agent context window","Obsidian plugin ecosystem compatibility not fully documented — custom plugins may interfere with sync","Agent execution is limited to tasks that can be expressed as tool calls — complex creative work or human judgment calls require manual intervention","Linear API rate limits (300 requests/min) may throttle agent throughput on high-volume task execution","No built-in rollback mechanism if agent execution fails midway through a task — requires manual cleanup","Tool execution latency adds 100-500ms per tool call depending on network and tool complexity","Schema validation overhead increases token usage for each tool invocation — may impact cost on high-volume workflows","No built-in distributed execution — all tool calls run sequentially unless explicitly parallelized","Embedding-based retrieval may miss relevant context if semantic similarity is low — requires tuning of similarity thresholds","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.09211650352323648,"quality":0.41,"ecosystem":0.6000000000000001,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.2,"quality":0.25,"ecosystem":0.1,"match_graph":0.4,"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":"2026-04-22T08:04:46.151Z","last_commit":"2026-03-30T12:43:53Z"},"community":{"stars":62,"forks":10,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=jason-cyr--ai-agent-workflow","compare_url":"https://unfragile.ai/compare?artifact=jason-cyr--ai-agent-workflow"}},"signature":"vkpNNnSOVAwWXY57iZ62LS6r60F8JXpLpk55YQppbvpr+9KujmC1YLfzTMAynxipsQT9z6f3GuDb9sQT9rk3Ag==","signedAt":"2026-06-19T23:51:25.109Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/jason-cyr--ai-agent-workflow","artifact":"https://unfragile.ai/jason-cyr--ai-agent-workflow","verify":"https://unfragile.ai/api/v1/verify?slug=jason-cyr--ai-agent-workflow","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"}}