Vendor OpenClaw source as Adolf fork baseline

Adolf is a fork/vendored clone of github.com/openclaw/openclaw (v2026.6.11),
free to diverge. Tree copied sans upstream .git; upstream remote added for
future syncs. Node pinned to 24 (.nvmrc); engines already require >=22.19.
Preserves docs/ARCHITECTURE.md.

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01LeqyaxJF2nbRXJtae2kNB2

extensions/open-prose/skills/prose/lib/cost-analyzer.prose

@@ -0,0 +1,174 @@
+# 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