name: reflection-manager description: > Generates reflection (R-xxx) upon completion of a Schedule/Task and updates cognitive links. Version: 1.1 Author: Entropy Control Theory License: MIT
Based on: Structure DNA v1.0, M-PPS v1.0, LLC v1.0, PROFILE-GENERATOR-SPEC v1.0

reflection.manager — v1.1

Goal

When a Schedule (S-) entry reaches done, generate a Reflection (R-) entry to capture outcomes, deltas, and next steps.
v1.1 Extension: Add energy_zone and energy_match fields for energy-aware learning and meta-feedback in the Language Logic Core (LLC).

Inputs

Outputs

Writes to /ledger/reflection.json (Structure DNA compliant):

Required

• id (R-xxx)
• title/action
• status = "done"
• created_at, updated_at

Optional

• related_entries (links to S-xxx / G-xxx)
• notes, tags
• New (v1.1):
  • energy_zone: string → "peak" | "stable" | "low"
  • energy_match: boolean → whether the execution matched the preferred energy window

Mechanism

1. Load the source entry (S-xxx / T-xxx) from /ledger/schedule.json.
2. Extract contextual info:
   • goal_id
   • related_entries
   • notes or prior metadata (for continuity)
3. (NEW) Detect energy_zone:
   • Parse from source_entry.notes (e.g., [energy_zone:peak]),
     or infer by comparing start/due times to profile windows.
4. (NEW) Compute energy_match:
   • Load /ledger/profile.json if available.
   • Compare actual start/due window to preferred time range for the detected task type.
   • Mark as true or false.
5. Create a new R-entry, link it to the source and parent goal, write to /ledger/reflection.json.
6. Return reflection summary and optional “re-goal” suggestion for goal.manager.

Pseudocode

def create_reflection(source_id, result=None, next_intent=None, ledger_dir="/ledger"):
    source = find_entry(source_id, ledger_dir)
    profile = try_load_json(f"{ledger_dir}/profile.json")

    # Extract or infer energy zone
    zone = extract_energy_zone(source)
    match = compute_energy_match(source, zone, profile)

    reflection = {
        "id": new_id("R-"),
        "title": f"Reflection for {source_id}",
        "status": "done",
        "goal_id": source.get("goal_id"),
        "related_entries": [source_id, source.get("goal_id")],
        "notes": (result or "") + f" | energy_zone:{zone} | energy_match:{match}",
        "energy_zone": zone,
        "energy_match": match,
        "created_at": now_iso(),
        "updated_at": now_iso(),
        "dispatch_to": "goal.manager"
    }

    append_to_ledger(f"{ledger_dir}/reflection.json", reflection)
    return reflection


def extract_energy_zone(entry):
    # parse [energy_zone:peak] from notes, fallback to "unknown"
    import re
    note = entry.get("notes", "")
    m = re.search(r"\[energy_zone:(.*?)\]", note)
    return m.group(1) if m else "unknown"


def compute_energy_match(entry, zone, profile):
    if not profile or zone == "unknown":
        return None
    # compare start time vs profile window
    return time_within_profile(entry["start"], zone, profile)

Example

Input

{
  "source_id": "S-210",
  "result": "completed article draft; felt productive"
}

Output

{
  "id": "R-310",
  "title": "Reflection for S-210",
  "status": "done",
  "goal_id": "G-101",
  "related_entries": ["S-210","G-101"],
  "energy_zone": "peak",
  "energy_match": true,
  "notes": "completed article draft; felt productive | energy_zone:peak | energy_match:true",
  "created_at": "2025-11-03T21:55:00-05:00",
  "updated_at": "2025-11-03T21:55:00-05:00",
  "dispatch_to": "goal.manager"
}

Notes

• energy_zone and energy_match are optional and auto-filled if profile.json exists.
• No change to dispatch rules — triggers remain done → reflection.manager.
• These new fields can be used by LLC for meta-learning or entropy-delta tracking.
• Works seamlessly with personal.schedule.manager v1.1.

“Reflection links time, energy, and intention — transforming action into learning.” — Entropy Control Theory, 2025

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