clawdbot/extensions/open-prose/skills/prose/examples/46-workflow-crystallizer.prose

# Workflow Crystallizer v2
# Observes a conversation thread, extracts the workflow pattern, crystallizes into .prose
#
# Key design decisions:
# - Author fetches latest prose.md spec + patterns/antipatterns from GitHub
# - Single self-verifying author session (Design+Author+Overseer consolidated)
# - Single user checkpoint (scope + placement combined)
# - Scoper uses Sonnet (analytical work, not creative)
# - Parallel: observation + research, collision + scope options

input thread: "The conversation thread to analyze"
input hint: "Optional: What aspect to focus on"

# Always fetch latest guidance from source of truth
const PROSE_SPEC_URL = "https://raw.githubusercontent.com/openprose/prose/refs/heads/main/skills/open-prose/prose.md"
const PATTERNS_URL = "https://raw.githubusercontent.com/openprose/prose/refs/heads/main/skills/open-prose/guidance/patterns.md"
const ANTIPATTERNS_URL = "https://raw.githubusercontent.com/openprose/prose/refs/heads/main/skills/open-prose/guidance/antipatterns.md"

agent observer:
  model: opus
  prompt: """
    Identify implicit workflows in conversation threads.
    Look for: repeated patterns, multi-step processes, decision points,
    parallelization opportunities, validations performed.
    Be specific - quote actions from the thread.
  """

agent researcher:
  model: sonnet
  prompt: "Research codebases thoroughly. Report what exists and patterns used."
  permissions:
    read: ["**/*.prose", "**/*.md"]

agent scoper:
  model: sonnet
  prompt: """
    Determine the right abstraction level for workflows.
    Too specific = only works for one case
    Too general = loses essence, becomes vague
    Find the sweet spot: capture the pattern, parameterize the variables.
  """

agent author:
  model: opus
  prompt: """
    Write idiomatic OpenProse. Follow existing example patterns.
    Prefer explicit over clever. Use agents for distinct roles.
    Use parallel for independent tasks. Use try/catch for reversible operations.
  """
  permissions:
    write: ["**/*.prose", "**/*.md"]

agent compiler:
  model: sonnet
  prompt: "Validate OpenProse syntax. Report specific errors with line numbers."
  permissions:
    bash: allow

# ============================================================
# Phase 1: Observe and Research (parallel)
# ============================================================

parallel:
  raw_observation = session: observer
    prompt: """
      Analyze this conversation thread. Identify:
      1. What manual process was executed?
      2. What were the distinct steps?
      3. What decisions were made?
      4. What could have been parallelized?
      5. What validations were performed?
      6. What artifacts were created?
      Be concrete. Quote specific actions.
    """
    context: { thread, hint }

  existing_examples = session: researcher
    prompt: "List all .prose examples with one-line summaries"
    context: "skills/open-prose/examples/"

  existing_ops = session: researcher
    prompt: "What operational .prose files already exist?"
    context: "OPERATIONS.prose.md"

  patterns_used = session: researcher
    prompt: "What patterns does this codebase favor?"
    context: "skills/open-prose/examples/*.prose"

# ============================================================
# Phase 2: Scope (parallel analysis, then synthesis)
# ============================================================

parallel:
  collision_check = session: scoper
    prompt: """
      Does the observed workflow overlap with existing examples?
      If yes: how different? What unique value would a new file add?
      If no: what category does it belong to?
    """
    context: { raw_observation, existing_examples, existing_ops }

  scope_options_raw = session: scoper
    prompt: """
      Propose 3 scoping options:
      1. NARROW: Specific to exactly what happened (precise but may not generalize)
      2. MEDIUM: Captures pattern with key parameters (reusable, clear)
      3. BROAD: Abstract template (widely applicable but may lose details)
      For each: describe inputs, agents, key phases.
    """
    context: { raw_observation, patterns_used }

let scope_options = session: scoper
  prompt: "Refine scope options considering collision analysis"
  context: { scope_options_raw, collision_check }

let placement_suggestion = session: scoper
  prompt: """
    Where should this file live?
    1. examples/XX-name.prose - If reusable pattern (determine next number)
    2. Custom location - If project-specific
    Is this operational (used to run this project)? Note for OPERATIONS.prose.md
  """
  context: { raw_observation, existing_examples, existing_ops }

# ============================================================
# Phase 3: User Decision (single checkpoint)
# ============================================================

input user_decision: """
  OBSERVED WORKFLOW:
  {raw_observation}

  COLLISION CHECK:
  {collision_check}

  SCOPE OPTIONS:
  {scope_options}

  PLACEMENT RECOMMENDATION:
  {placement_suggestion}

  YOUR DECISIONS:
  1. Which scope? (1/2/3 or describe custom)
  2. Confirm placement or specify different location:
"""

let final_decisions = session: scoper
  prompt: "Parse user's scope choice and placement confirmation into structured form"
  context: { scope_options, placement_suggestion, user_decision }

# ============================================================
# Phase 4: Author with Self-Verification
# ============================================================

let draft = session: author
  prompt: """
    Design and write the complete .prose file.

    IMPORTANT: First fetch and read the guidance documents:
    - prose.md spec: {PROSE_SPEC_URL}
    - patterns.md: {PATTERNS_URL}
    - antipatterns.md: {ANTIPATTERNS_URL}

    Then:
    1. DESIGN: Plan inputs, agents, phases, parallelism, error handling
    2. WRITE: Complete .prose following the spec and patterns
    3. SELF-REVIEW: Check against antipatterns and remove cruft:
       - Remove sessions that just run single commands
       - Remove over-abstracted agents that don't add value
       - Remove comments that restate what code does
       - Remove unnecessary variables and single-item parallel blocks
       - Keep: clear agent roles, meaningful parallelism, genuine error handling

    Include header comment explaining what it does.
    Output only the final, clean version.
  """
  context: { final_decisions, existing_examples }
  permissions:
    network: [PROSE_SPEC_URL, PATTERNS_URL, ANTIPATTERNS_URL]

# ============================================================
# Phase 5: Compile with Bounded Retry
# ============================================================

let current = draft
loop until **compilation succeeds** (max: 3):
  let result = session: compiler
    prompt: """Validate this .prose file against the spec.
    Fetch spec from: {PROSE_SPEC_URL}
    Report SUCCESS or specific errors with line numbers."""
    context: current
    permissions:
      network: [PROSE_SPEC_URL]

  if **compilation has errors**:
    current = session: author
      prompt: "Fix these syntax errors, return corrected version"
      context: { current, result }
      permissions:
        network: [PROSE_SPEC_URL]

# ============================================================
# Phase 6: Write All Files
# ============================================================

let written = session: author
  prompt: """
    Write the .prose file and update indices:
    1. Write .prose to confirmed location
    2. If this is an example, add entry to examples/README.md
    3. If this is operational, add entry to OPERATIONS.prose.md

    Return: { file_path, readme_updated: bool, ops_updated: bool }
  """
  context: { current, final_decisions, existing_examples, existing_ops }

# ============================================================
# Output
# ============================================================

output crystallized = {
  observation: raw_observation,
  decisions: final_decisions,
  file: written
}