compile-workflow

name	compile-workflow
description	Update workflow implementation from its embedded description. Use this after modifying workflow or node descriptions.

Compile Workflow

Updates the run() method of a workflow in src/crayon/workflows/*.ts based on its embedded description field and the description fields in referenced nodes/agents.

Announce at start: "I'm using the compile-workflow skill to update the workflow implementation from its description."

---

Pre-Flight Checks

1. Verify workflow files exist:

   • src/crayon/workflows/ must exist with at least one .ts file
   • If no .ts files found, tell user to run /crayon:create-workflow first

2. If no workflow name provided:

   • List all workflows in src/crayon/workflows/
   • Ask user to select which one to compile

3. If workflow name provided:

   • Verify src/crayon/workflows/<name>.ts exists
   • If not, list available workflows and ask user to choose

---

Description Parsing

Workflow Description

Read the description field from the Workflow.create() call in src/crayon/workflows/<name>.ts. The description contains flow-level information:

• Summary — first line/paragraph
• ## Tasks — ordered list of tasks with:
  • **Node:** references (name + type)
  • **Condition:** / **If true:** / **If false:** for decisions
  • **Loop:** for iteration
  • **Return:** for terminal tasks

Node/Agent Descriptions

For each task's **Node:** reference, read the description field from the node/agent file to get:

• What the node does — first paragraph
• **Input Description:** — plain language inputs
• **Output Description:** — plain language outputs

Task Formats

Standard task:

### N. Task Name
**Node:** `node-name` (agent|node)

Node file contains:

<Description>

**Input Description:** what it needs

**Output Description:** what it produces

Decision task (no Node):

### N. Decision Name
**Condition:** `expression`
**If true:** continue to task M
**If false:** return:
  - field1: value
  - field2: value

Terminal task (ends with Return):

**Return:**
  - field1: value
  - field2: value

---

Node Resolution

For each task's **Node:** reference, determine what it is and where it lives.

Node Types

Type	Location	Import Pattern
(builtin)	Built-in nodes from crayon	import { webRead } from "runcrayon"
(node)	User-defined in src/crayon/nodes/	import { nodeName } from "../nodes/<name>"
(agent)	src/crayon/agents/<name>.ts	import { agentName } from "../agents/<name>"

Note: Agent imports reference the executable file (src/crayon/agents/<name>.ts), not the spec file (src/crayon/agents/<name>.md). The executable contains the runtime code that loads the spec.

IMPORTANT: Never use .js extensions in import paths. Use extensionless imports (e.g., "../nodes/check-website" not "../nodes/check-website.js"). Turbopack cannot resolve .js → .ts in production builds.

Resolution Steps

1. Parse node reference: Extract name and type from **Node:** \name` (type)` in the workflow description

2. For builtin nodes:

   • Check if it's a built-in node (web_read, etc.)
   • Import from "runcrayon"

3. For user-defined nodes:

   • Look for src/crayon/nodes/<name>.ts
   • Read its description field and inputSchema/outputSchema for type info
   • If missing: create it using the stub templates from /crayon:create-workflow

4. For agents:

   • Look for src/crayon/agents/<name>.ts
   • Read its description field and inputSchema/outputSchema for type info
   • If missing: create it using the stub templates from /crayon:create-workflow

Updating Agent Tools from Description

When an agent's description contains a **Tools needed:** section (added by /crayon:refine-node), update the agent's tools record and imports to match. Use the tool type to determine the import pattern:

Tool Type	Import Pattern	tools record entry
(builtin)	import { webRead } from "runcrayon"	web_read: webRead
(provider)	import { createOpenAI } from "@ai-sdk/openai"; const openai = createOpenAI();	web_search: openai.tools.webSearch()
(user node in src/crayon/nodes/<file>.ts)	import { name } from "../nodes/<file>"	enrich_company: enrichCompany

---

Handling Ambiguities

When task logic is unclear, make your best guess and generate the code. The user can correct after. Don't block on questions.

Common Ambiguities and Defaults

Pattern	Problem	Default
"if good fit"	Undefined criteria	Use score >= 80 and add a comment noting the threshold is a guess
"check if valid"	Undefined validation	Check for presence of required fields
Untyped output	Can't generate schema	Infer from node description and context
Missing condition	Decision has no Condition:	Infer from surrounding task context, add a TODO comment if truly unknowable

After generating code, tell the user what you assumed so they can correct anything.

---

Code Generation

Rewrite the run() method in the existing src/crayon/workflows/<name>.ts file. Also update imports and schemas as needed. Preserve the description field as-is.

Generated run() Structure

async run(ctx, inputs: <Name>Input): Promise<<Name>Output> {
  // Task 1: <Task Name>
  // <task description as comment>
  const <output_var> = await ctx.run(<nodeRef>, { <inputs> });

  // Task 2: <Decision or next task>
  if (<condition>) {
    // ...
  }

  return { <output fields> };
},

Side-Effect Node Data Flow

When wiring ctx.run() calls for side-effect nodes (nodes whose description includes **Side Effect:**):

1. Every target input MUST trace to an explicit source — Side-effect node inputs like recipientEmail, slackChannel, recordId must come from:

   • The workflow's own input fields (e.g., inputs.recipientEmail)
   • An upstream node's output field (e.g., enrichResult.email)
   • A deliberate constant with clear rationale

   NEVER fabricate or randomly choose a target value. If the data flow for a target field is unclear, add a // TODO: wire this to an explicit source comment and flag it to the user.

2. Side-effect outputs should be captured — Always capture the return value of side-effect nodes, even if it's not used by downstream nodes. This ensures the action details (what was sent, to whom, what was updated) appear in the workflow trace.

   // GOOD: capture the result
   const slackResult = await ctx.run(sendSlackDm, { channel: inputs.slackChannel, message: summary });
   
   // BAD: discard the result
   await ctx.run(sendSlackDm, { channel: inputs.slackChannel, message: summary });
   

Naming Conventions

• Workflow export: camelCase (e.g., urlSummarizer)
• Schema names: PascalCase + Schema/Input/Output (e.g., UrlSummarizerInputSchema)
• Type names: PascalCase + Input/Output (e.g., UrlSummarizerInput)

---

Worked Example

A workflow enriches Gmail leads and sends results to Slack. Here's how the compiler turns descriptions into the run() method.

Input: Workflow description

Enrich the 10 most recent Gmail leads from Salesforce with web research and DM results on Slack.

## Tasks

### 1. Fetch Gmail Leads
**Node:** `fetch-gmail-leads` (node)

### 2. Enrich Lead
**Node:** `enrich-lead` (agent)
**Loop:** for each lead in leads

### 3. Send Slack DM
**Node:** `send-slack-dm` (node)

Input: Node schemas (from refined node files)

• fetch-gmail-leads — inputSchema: z.object({}), outputSchema: z.object({ leads: z.array(LeadSchema) })
• enrich-lead — inputSchema: z.object({ name: z.string(), email: z.string(), company: z.string().nullable(), title: z.string().nullable() }), outputSchema: z.object({ name: z.string(), email: z.string(), linkedinUrl: z.string().nullable(), ... })
• send-slack-dm — inputSchema: z.object({ enrichedLeads: z.array(EnrichedLeadSchema) }), outputSchema: z.object({ success: z.boolean(), channel: z.string() })

Output: Generated run() method

async run(ctx, inputs: LeadEnrichmentInput): Promise<LeadEnrichmentOutput> {
  // Task 1: Fetch the 10 most recent Gmail leads from Salesforce
  const leadsResult = await ctx.run(fetchGmailLeads, {});

  // Task 2: Enrich each lead with web research
  const enrichedLeads = [];
  for (const lead of leadsResult.leads) {
    const enriched = await ctx.run(enrichLead, {
      name: lead.name ?? "",
      email: lead.email ?? "",
      company: lead.company,
      title: lead.title,
    });
    enrichedLeads.push(enriched);
  }

  // Task 3: Send enriched lead summary as Slack DM
  const slackResult = await ctx.run(sendSlackDm, { enrichedLeads });

  return { success: slackResult.success, channel: slackResult.channel };
},

Key things the compiler did:

1. Data flow — wired leadsResult.leads fields into enrichLead's input schema, and enrichedLeads array into sendSlackDm's input
2. Loop — translated **Loop:** for each lead in leads into a for...of loop over leadsResult.leads
3. Schema alignment — matched field names from output schemas to input schemas (e.g., lead.name → name, lead.company → company)

After Compilation

1. Save a version — Call the create_version MCP tool with a message like:

   Finalize workflow: <workflow-name>
   
   <Describe what changed — like a good git commit message body.>
   

2. Tell the user:

   • "Updated src/crayon/workflows/<name>.ts"
   • If any missing nodes/agents were created: list the new files
   • If agent tools were updated from descriptions: list which agents were updated
   • "To test it, just say run it and I'll trigger it for you." — do NOT suggest using the CLI or opening the Dev UI manually

---

Compiler Principles

1. Draft first, ask later — make your best guess and let the user correct, rather than blocking on questions
2. No invention — only emit code that maps to descriptions; guesses should be flagged with comments
3. Deterministic — same descriptions → same output
4. Readable output — generated code should be understandable
5. Update descriptions — when clarifying, update the description field so it stays canonical
6. Run it yourself — when the user wants to test a workflow or node, use the runWorkflow / runNode MCP tools or run CLI commands yourself.
7. Side-effect targets are never fabricated — they must come from an explicit source or be a deliberate constant.