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catlog22catlog22/Claude-Code-Workflow/.claude/skills/wf-composer/SKILL.md

wf-composer

Skill2.1k starsChanged 7 months agoMITArchived repositoryView on GitHub

Semantic workflow composer — parse natural language workflow description into a DAG of skill/CLI/agent nodes, auto-inject checkpoint save nodes, confirm with user, persist as reusable JSON template. Triggers on "wf-composer " or "/wf-composer".

1 other project uses a version of this.

Adopted by

Most recently taken up by

The file

We split this skill into the rules it gives an agent

6 rules. About 0 are common, 3 are distinctive, and 3 are very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈3

    Distinctive · estimate

    This author's own choices. Worth a look.

  • 3

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Very specific:

    Read phases/01-parse.md and execute.

    line 60
  2. Very specific:

    After every skill → skill transition that crosses a semantic phase boundary

    line 92
  3. Very specific:

    Before any long-running agent spawn

    line 93
  4. Distinctive:

    After any node that produces a persistent artifact (plan, spec, analysis)

    line 94
  5. Distinctive:

    Add node after (select position)

    line 126
  6. Distinctive:

    Remove node (select node ID)

    line 127

≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

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

ConstantValue
Session prefixWFD
Template dir.workflow/templates/
Template ID formatwft-<slug>-<date>
Node ID formatN-<seq> (e.g. N-001), CP-<seq> for checkpoints
Max nodes20

Entry Router

Parse $ARGUMENTS.

DetectionConditionHandler
Resume design--resume flag or existing WFD session-> Phase 0: Resume
Edit template--edit <template-id> flag-> Phase 0: Load + Edit
New designDefault-> 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

SpecPurpose
specs/node-catalog.mdAvailable executors, port definitions, arg templates
specs/template-schema.mdFull JSON template schema

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