# Cost Analyzer
# Analyzes runs for token usage and cost patterns
#
# Usage:
#   prose run @openprose/lib/cost-analyzer
#
# Inputs:
#   run_path: Path to run to analyze, or "recent" for latest runs
#   scope: single | compare | trend
#
# Outputs:
#   - Token usage breakdown by agent/phase
#   - Model tier efficiency analysis
#   - Cost hotspots
#   - Optimization recommendations

input run_path: "Path to run, or 'recent' for latest runs in .prose/runs/"
input scope: "Scope: single (one run) | compare (multiple runs) | trend (over time)"

# ============================================================
# Agents
# ============================================================

agent collector:
  model: sonnet
  prompt: """
    You collect and structure cost/token data from .prose runs.

    Extract from run artifacts:
    - Model used per session (haiku/sonnet/opus)
    - Approximate token counts (estimate from content length)
    - Session count per agent
    - Parallel vs sequential execution
  """

agent analyzer:
  model: opus
  prompt: """
    You analyze cost patterns and identify optimization opportunities.

    Consider:
    - Model tier appropriateness (is opus needed, or would sonnet suffice?)
    - Token efficiency (are contexts bloated?)
    - Parallelization (could sequential steps run in parallel?)
    - Caching opportunities (repeated computations?)
  """

agent tracker:
  model: haiku
  persist: user
  prompt: """
    You track cost metrics across runs for trend analysis.
    Store compactly: run_id, program, total_cost_estimate, breakdown.
  """

# ============================================================
# Phase 1: Collect Run Data
# ============================================================

let runs_to_analyze = session: collector
  prompt: """
    Find runs to analyze.

    Input: {run_path}
    Scope: {scope}

    If run_path is a specific path, use that run.
    If run_path is "recent", find the latest 5-10 runs in .prose/runs/

    For scope=compare, find runs of the same program.
    For scope=trend, find runs over time.

    Return: list of run paths to analyze
  """

let run_data = runs_to_analyze | pmap:
  session: collector
    prompt: """
      Extract cost data from run: {item}

      Read state.md and bindings to determine:
      1. Program name
      2. Each session spawned:
         - Agent name (or "anonymous")
         - Model tier
         - Estimated input tokens (context size)
         - Estimated output tokens (binding size)
      3. Parallel blocks (how many concurrent sessions)
      4. Total session count

      Estimate costs using rough rates:
      - haiku: $0.25 / 1M input, $1.25 / 1M output
      - sonnet: $3 / 1M input, $15 / 1M output
      - opus: $15 / 1M input, $75 / 1M output

      Return structured JSON.
    """
    context: item

# ============================================================
# Phase 2: Analyze
# ============================================================

let analysis = session: analyzer
  prompt: """
    Analyze cost patterns across these runs.

    Data: {run_data}
    Scope: {scope}

    For single run:
    - Break down cost by agent and phase
    - Identify the most expensive operations
    - Flag potential inefficiencies

    For compare:
    - Show cost differences between runs
    - Identify which changes affected cost
    - Note if cost increased/decreased

    For trend:
    - Show cost over time
    - Identify if costs are stable, growing, or improving
    - Flag anomalies

    Always include:
    - Model tier efficiency (are expensive models used appropriately?)
    - Context efficiency (are contexts lean or bloated?)
    - Specific optimization recommendations

    Return structured JSON with:
    {
      "summary": { total_cost, session_count, by_model: {...} },
      "hotspots": [ { agent, cost, percent, issue } ],
      "recommendations": [ { priority, description, estimated_savings } ],
      "details": { ... }
    }
  """
  context: run_data

# ============================================================
# Phase 3: Track for Trends
# ============================================================

resume: tracker
  prompt: """
    Record this cost analysis for future trend tracking.

    {analysis.summary}

    Add to your historical record.
  """
  context: analysis

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

output report = session "Format report"
  prompt: """
    Format the cost analysis as a readable report.

    Analysis: {analysis}

    Include:
    1. Executive summary (total cost, key finding)
    2. Cost breakdown table
    3. Hotspots (where money goes)
    4. Recommendations (prioritized)
    5. If scope=trend, include trend chart (ascii or description)

    Format as markdown.
  """
  context: analysis