Workflow Design

Parse user's semantic workflow description → decompose into nodes → map to executors → auto-inject checkpoints → confirm pipeline → save as reusable workflow-template.json.

Architecture

User describes workflow in natural language
  -> Phase 1: Parse — extract intent steps + variables
  -> Phase 2: Resolve — map each step to executor (skill/cli/agent/command)
  -> Phase 3: Enrich — inject checkpoint nodes, set DAG edges
  -> Phase 4: Confirm — visualize pipeline, user approval/edit
  -> Phase 5: Persist — save .workflow/templates/<name>.json

Shared Constants

Constant	Value
Session prefix	WFD
Template dir	.workflow/templates/
Template ID format	wft-<slug>-<date>
Node ID format	N-<seq> (e.g. N-001), CP-<seq> for checkpoints
Max nodes	20

Entry Router

Parse $ARGUMENTS.

Detection	Condition	Handler
Resume design	--resume flag or existing WFD session	-> Phase 0: Resume
Edit template	--edit <template-id> flag	-> Phase 0: Load + Edit
New design	Default	-> Phase 1: Parse

Phase 0: Resume / Edit (optional)

Resume design session:

1. Scan .workflow/templates/design-drafts/WFD-*.json for in-progress designs
2. Multiple found → AskUserQuestion for selection
3. Load draft → skip to last incomplete phase

Edit existing template:

1. Load template from --edit path
2. Show current pipeline visualization
3. AskUserQuestion: which nodes to modify/add/remove
4. Re-enter at Phase 3 (Enrich) with edits applied

---

Phase 1: Parse

Read phases/01-parse.md and execute.

Objective: Extract structured semantic steps + context variables from natural language.

Success: design-session/intent.json written with: steps[], variables[], task_type, complexity.

---

Phase 2: Resolve

Read phases/02-resolve.md and execute.

Objective: Map each intent step to a concrete executor node.

Executor types:

• skill — invoke via Skill(skill=..., args=...)
• cli — invoke via ccw cli -p "..." --tool ... --mode ...
• command — invoke via Skill(skill="<namespace:command>", args=...)
• agent — invoke via Agent(subagent_type=..., prompt=...)
• checkpoint — state save + optional user pause

Success: design-session/nodes.json written with resolved executor for each step.

---

Phase 3: Enrich

Read phases/03-enrich.md and execute.

Objective: Build DAG edges, auto-inject checkpoints at phase boundaries, validate port compatibility.

Checkpoint injection rules:

• After every skill → skill transition that crosses a semantic phase boundary
• Before any long-running agent spawn
• After any node that produces a persistent artifact (plan, spec, analysis)
• At user-defined breakpoints (if any)

Success: design-session/dag.json with nodes[], edges[], checkpoints[], context_schema{}.

---

Phase 4: Confirm

Read phases/04-confirm.md and execute.

Objective: Visualize the pipeline, present to user, incorporate edits.

Display format:

Pipeline: <template-name>
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
N-001 [skill]      workflow-lite-plan       "{goal}"
  |
CP-01 [checkpoint] After Plan               auto-continue
  |
N-002 [skill]      workflow-test-fix        "--session N-001"
  |
CP-02 [checkpoint] After Tests              pause-for-user
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
Variables: goal (required)
Checkpoints: 2 (1 auto, 1 pause)

AskUserQuestion:

• Confirm & Save
• Edit node (select node ID)
• Add node after (select position)
• Remove node (select node ID)
• Rename template

Success: User confirmed pipeline. Final dag.json ready.

---

Phase 5: Persist

Read phases/05-persist.md and execute.

Objective: Assemble final template JSON, write to template library, output summary.

Output:

• .workflow/templates/<slug>.json — the reusable template
• Console summary with template path + usage command

Success: Template saved. User shown: Skill(skill="wf-player", args="<template-path>")

---

Specs Reference

Spec	Purpose
specs/node-catalog.md	Available executors, port definitions, arg templates
specs/template-schema.md	Full JSON template schema