workflow-creator-skill

name	workflow_creator_skill
description	Create, optimize, and evaluate reusable AgentEvolver HTML Workflows. Use when a repeated successful multi-agent orchestration should be retained, when an evolvable Workflow has recurring orchestration-level defects, or when a live version needs evidence for keep, improve, or rollback decisions.
version	1.2.0
type	["orchestrator","worker"]
license	Apache-2.0
category	meta
requirements	["cpu"]
metadata	{}

Workflow Creator

One methodology for the complete Workflow lifecycle. A Workflow is a reusable orchestration program, not an Agent subtype and not a substitute for a Skill's domain instructions.

Orchestration

MetaAgent uses this lifecycle:

1. Prefer an existing active Workflow when applicability and contracts match.
2. Generate only after a useful orchestration pattern has repeated successfully, or when the user explicitly requests a reusable Workflow.
3. Register generated/optimized HTML as status="active"; like Tool and Skill, it is live immediately.
4. Dispatch workflow_evaluate_agent on at least three representative cases.
5. Record every outcome through evolution_tool.record_workflow_evaluation.
6. Keep a healthy version; otherwise optimize, rollback, or unload it.

Do not evolve from a single transient failure. Fix Agent/Tool/Skill quality at its owning layer; change Workflow only for topology, routing, conditions, fan-out, verification, retry, aggregation, budget, or termination defects.

Creating a Workflow

• Inspect active Workflows first and avoid semantic duplicates.
• Generalize concrete task details into typed <inputs>.
• Write one complete HTML document to extension/workflow/{name}.html.
• Use a <workflow> element with name, semantic version, schema-version="1.1.0", status="active", a precise description, and enable-evolving="true".
• Include <applicability> tags and prose explaining when to use and when not to use it.
• Use only supported callable nodes: agent, tool, skill, connector, environment, workflow. Connector and Environment nodes require an action.
• Use only bounded control nodes: parallel, map, branch, loop, verify, reduce, checkpoint. Every loop requires max-rounds; all fan-out needs a concurrency bound.
• Values use restricted ${path} expressions. Never embed JavaScript, Python, handlers, shell commands, or arbitrary templates.
• Declare complex input constraints with sibling <schema for="name"> Draft 2020-12 JSON Schema and keep its type consistent with the <input> attribute.
• Define explicit <outputs> and reference guaranteed top-level step results.
• Set node timeout and retry/backoff policy where an external capability can stall.
• Compile the file before completion and include its absolute path in done_tool.reasoning.

Improving a Workflow

1. Call inspect_workflow first. Stop if missing or enable_evolving=false.
2. Read evaluation evidence and identify a Workflow-owned defect.
3. Make the smallest structural change; preserve public name and input/output compatibility unless the task explicitly authorizes a breaking change.
4. Increment the semantic version and keep status active.
5. Compile and check boundedness, reachability, capability names, and output references.
6. Include the edited absolute HTML path in done_tool.reasoning for registration.

Never tune a Workflow to one benchmark case. Prefer parameterization over copying variants.

Evaluating a Workflow

Evaluation is read-only:

1. Call inspect_workflow; confirm name, version, active status, source, and contract.
2. Check schema safety, bounded termination, path coverage, capability existence, retry and verification policy, input/output clarity, and applicability precision.
3. Run a representative case when safe. Compare with the prior active version or manual orchestration when available.
4. Score outcome quality from 0.0–1.0 and report success, real run id, case id, elapsed time, token cost, and concrete notes.
5. Call evolution_tool with action=record_workflow_evaluation. Recommend keep, optimize, rollback, or unload from concrete evidence.

A version is provisionally healthy after at least 3 evaluations, success rate at least 0.8, and average quality at least 0.7. This summary guides retention; it does not create a second publication state. Representative coverage and the absence of safety or termination defects remain mandatory.