Plugin orchestration patterns

Plugins and skills only deliver value when they run in the right order, with the right data, and without stepping on each other. Plugin orchestration is the layer that decides which skill runs when, how outputs flow to the next step, and how to recover when something fails. This guide covers plugin orchestration patterns: coordinator roles, dependency resolution, execution models, and where document and PDF workflows (e.g. iReadPDF) sit in the orchestration stack so US professionals get predictable, maintainable automation.

Summary Treat orchestration as a separate concern from skill logic. Use a coordinator that resolves dependencies, runs skills in a valid order (linear, parallel, or DAG), and passes stable output keys between steps. When document pipelines are involved, treat "get document summary" or "get document status" as orchestrated steps that other plugins consume—so iReadPDF and your doc pipeline slot in as one managed step among many.

Table of Contents

• What Plugin Orchestration Is
• The Coordinator Role
• Dependency Resolution
• Execution Models
• Document and PDF Steps in the Pipeline
• Failure and Retry Policies
• Choosing a Pattern for Your Stack
• Conclusion

What Plugin Orchestration Is

Orchestration is the logic that answers: Which plugin runs next? With what inputs? What happens if it fails?

Without orchestration, you end up with scripts that call skills in a fixed order. When you add a new skill or change dependencies, you edit the script by hand and hope nothing breaks. With explicit orchestration, you declare dependencies (e.g. "compose_brief needs calendar_events, task_list, doc_queue") and the coordinator figures out order and data flow. That makes it easier to add document steps (e.g. "get document summary" from iReadPDF) or swap steps without rewriting the whole workflow.

For US professionals running morning briefs, meeting prep, and task suggestion, orchestration is what turns a bag of skills into a coherent, auditable pipeline.

The Coordinator Role

The coordinator is the component that owns orchestration. It does not implement business logic; it invokes skills and passes data.

1. Parse the workflow definition. The workflow might be a list of steps (get_calendar, get_tasks, get_document_status, compose_brief, send) or a DAG (directed acyclic graph) where each node is a skill and edges are data dependencies.
2. Resolve execution order. From dependencies, compute a valid order. If compose_brief needs calendar_events, task_list, and doc_queue, then get_calendar, get_tasks, and get_document_status must run first—and they can run in parallel if they have no dependencies on each other.
3. Invoke skills and pass outputs. For each step, the coordinator calls the skill with the right inputs (from config or from previous steps' outputs), collects the output, and feeds it to the next step(s) by key (e.g. calendar_events, doc_queue).
4. Handle failures. On failure, the coordinator applies policy: retry, skip with fallback, or abort and notify.

When document workflows are in the mix, the coordinator treats "get document status" and "get document summary" like any other step: same dependency resolution, same data passing. If your doc summaries come from iReadPDF or a cached store, that step returns a stable shape (e.g. doc_summaries) so downstream steps stay agnostic to where the data came from.

Dependency Resolution

Dependencies determine order. Each skill declares what it needs; the coordinator builds a dependency graph.

• Input-based dependencies. "compose_brief needs calendar_events, task_list, doc_queue." So the coordinator ensures get_calendar, get_tasks, and get_document_status (or whatever produces those keys) run before compose_brief. No need to hard-code "run get_calendar first"; the graph says so.
• No circular dependencies. If skill A needs B's output and B needs A's output, the graph is invalid. Refactor so one of them depends on a shared upstream (e.g. both read from a "context" step that runs first).
• Optional inputs. Some steps accept optional inputs (e.g. doc_queue). If get_document_status is skipped or fails, the coordinator can pass an empty doc_queue so compose_brief still runs. Document which inputs are required vs optional in your workflow spec.

For document-heavy workflows, list doc_queue and doc_summaries as outputs of dedicated steps. Other skills depend on those keys, not on "the PDF tool." That keeps iReadPDF and your doc pipeline as one replaceable step in the graph.

Execution Models

Common execution models for orchestrated plugins:

| Model | Description | When to use | |-------|-------------|-------------| | Linear | Steps run in a fixed sequence. Output of step N is input to step N+1. | Simple pipelines (fetch → transform → send). Easy to reason about. | | Parallel | Independent steps run at the same time; results are merged before dependent steps. | When get_calendar, get_tasks, and get_document_status are independent—run all three, then compose_brief. | | DAG | Directed acyclic graph. Steps run when their dependencies are satisfied; multiple steps can run in parallel when possible. | Complex workflows with branching and merging. | | Conditional | Some steps run only when a condition holds (e.g. include_docs true). | Same workflow with or without document steps; coordinator skips get_document_status when not needed. |

Many US-professional workflows are linear or "parallel fetch then linear compose." Document steps (get document status, get document summaries) often sit in the "fetch" phase so that compose_brief and similar skills receive a single, consistent doc format from one place—e.g. iReadPDF output or a cache that your pipeline fills.

Document and PDF Steps in the Pipeline

Document and PDF handling should be explicit steps in the orchestration, not hidden inside other skills.

1. Define a step that returns doc status. A skill (or adapter to your doc store) returns doc_queue: list of documents with status (to_summarize, summarized, to_sign, etc.). The coordinator runs this step and passes doc_queue to any skill that needs it. Summaries may come from iReadPDF runs that wrote to memory; this step just reads that state.
2. Define a step that returns doc summaries when needed. For meeting prep or detailed briefs, a step that returns doc_summaries (title, summary, key_points) for given doc IDs. That step may call your PDF pipeline or read from cache; the rest of the workflow only sees the structured output.
3. One schema for doc data. All steps that produce or consume doc_queue or doc_summaries use the same schema. Then the coordinator and every plugin (including those that don't touch PDFs) stay in sync. When you change how iReadPDF writes summaries, you update that one step's contract, not every consumer.

Orchestration stays clean when document workflows are first-class steps with clear inputs and outputs, rather than ad-hoc PDF logic scattered across skills.

Failure and Retry Policies

The coordinator must decide what to do when a step fails.

• Retry. For transient failures (network, rate limit), retry with backoff. Set a max retry count and timeout so the workflow doesn't hang.
• Skip with fallback. For optional steps (e.g. get_document_status), on failure pass a fallback value (e.g. doc_queue: []) and continue. Log the failure for later inspection.
• Abort. For critical steps (e.g. compose_brief, send), abort the workflow and notify. Optionally store partial results (e.g. what was fetched) for debugging.
• Document policies per step. In the workflow definition, mark each step as "required" or "optional" and attach retry/fallback behavior. Then the coordinator behaves consistently and operators know what to expect.

For document steps, distinguish "no documents" (success with empty list) from "failed to fetch" (error). iReadPDF or your pipeline may have its own retries; the coordinator only needs to know whether the step succeeded and what it returned.

Choosing a Pattern for Your Stack

Start simple; add structure when you need it.

1. Small set of skills, fixed order. Use a linear orchestrator: a list of steps and a small engine that runs them in order, passing outputs by key. Add document steps (get_document_status, get_document_summaries) as entries in that list.
2. Many independent fetches. Use parallel execution for the fetch phase, then a single compose step. Document steps run in parallel with calendar and tasks when they don't depend on each other.
3. Complex workflows with branching. Model the workflow as a DAG. Each node is a skill; edges are data dependencies. Use a coordinator that can execute DAGs (e.g. topological sort, then run nodes when dependencies are ready). Document and PDF steps are nodes like any other.
4. Same workflow with or without docs. Use conditional steps: "if include_docs, run get_document_status and optionally get_document_summaries; else pass empty." The rest of the chain is unchanged; iReadPDF and your doc pipeline are only invoked when the workflow asks for doc data.

Whatever pattern you pick, document it and keep skill contracts (inputs/outputs) stable so the coordinator and your plugins stay decoupled.

Conclusion

Plugin orchestration patterns give you a clear way to run multiple skills in the right order, with the right data, and with defined behavior on failure. A coordinator that resolves dependencies and supports linear, parallel, or DAG execution keeps workflows maintainable as you add or change skills. When document and PDF workflows are first-class steps—returning doc_queue and doc_summaries in a standard format—tools like iReadPDF slot in as one orchestrated step and the rest of your automation stays document-agnostic. For US professionals, that's how you scale from a few scripts to a robust, auditable automation stack.

Ready to orchestrate your document workflow as a first-class step? Use iReadPDF for consistent PDF summarization and extraction, then plug that output into your orchestrated pipeline so every workflow gets the same doc context without duplication.