From 1d47665dd4fe80e972c0ad1d113c25f22cac0f98 Mon Sep 17 00:00:00 2001
From: Alexander Whitestone <alexander@alexanderwhitestone.com>
Date: Wed, 15 Apr 2026 14:47:26 +0000
Subject: [PATCH] feat: add improvement proposal generator (#168)

---
 scripts/improvement_proposals.py | 451 +++++++++++++++++++++++++++++++
 1 file changed, 451 insertions(+)
 create mode 100644 scripts/improvement_proposals.py

diff --git a/scripts/improvement_proposals.py b/scripts/improvement_proposals.py
new file mode 100644
index 0000000..4027434
--- /dev/null
+++ b/scripts/improvement_proposals.py
@@ -0,0 +1,451 @@
+#!/usr/bin/env python3
+"""
+Improvement Proposal Generator for compounding-intelligence.
+
+Analyzes fleet session data to identify waste patterns and generates
+concrete improvement proposals with ROI estimates.
+
+Input: Session analytics JSON (from fleet metrics or session database)
+Output: Markdown proposal document + JSON proposals
+
+Usage:
+    python3 scripts/improvement_proposals.py --input analytics.json
+    python3 scripts/improvement_proposals.py --input analytics.json --format json
+    python3 scripts/improvement_proposals.py --input analytics.json --output proposals.md
+    python3 scripts/improvement_proposals.py --input analytics.json --threshold 5.0
+"""
+
+import argparse
+import json
+import os
+import sys
+from collections import defaultdict
+from dataclasses import dataclass, field, asdict
+from datetime import datetime, timezone
+from typing import Any, Dict, List, Optional
+
+
+# ── Data types ─────────────────────────────────────────────────
+
+@dataclass
+class WastePattern:
+    """A detected waste pattern in fleet sessions."""
+    pattern_type: str       # "repeated_error", "manual_process", "slow_tool", "failed_retry"
+    description: str
+    occurrences: int
+    total_time_hours: float
+    affected_repos: List[str] = field(default_factory=list)
+    example_sessions: List[str] = field(default_factory=list)
+
+
+@dataclass
+class ImprovementProposal:
+    """A concrete improvement proposal with ROI estimate."""
+    title: str
+    problem: str
+    proposed_solution: str
+    estimated_weekly_hours_saved: float
+    estimated_monthly_hours_saved: float
+    implementation_hours: float
+    roi_weeks: float           # weeks to break even
+    priority: str              # "critical", "high", "medium", "low"
+    affected_area: str
+    supporting_evidence: List[str] = field(default_factory=list)
+
+
+# ── Session analysis ──────────────────────────────────────────
+
+def analyze_sessions(sessions: List[dict]) -> List[WastePattern]:
+    """
+    Analyze session data to find waste patterns.
+
+    Looks for:
+    - Repeated errors (same error across sessions)
+    - Manual processes (long sequences of similar actions)
+    - Slow tools (tools with high latency)
+    - Failed retries (multiple attempts at same task)
+    """
+    patterns = []
+
+    # Track error frequency across sessions
+    error_counts: Dict[str, List[dict]] = defaultdict(list)
+    tool_latencies: Dict[str, List[float]] = defaultdict(list)
+    retry_counts: Dict[str, int] = defaultdict(int)
+    manual_sequences: List[dict] = []
+
+    for session in sessions:
+        session_id = session.get("session_id", "unknown")
+        repo = session.get("repo", "global")
+        messages = session.get("messages", [])
+        errors = session.get("errors", [])
+        tool_calls = session.get("tool_calls", [])
+        duration_min = session.get("duration_minutes", 0)
+
+        # 1. Repeated errors
+        for err in errors:
+            err_key = _normalize_error(err.get("message", ""))
+            if err_key:
+                error_counts[err_key].append({
+                    "session_id": session_id,
+                    "repo": repo,
+                })
+
+        # 2. Tool latency tracking
+        for tc in tool_calls:
+            tool_name = tc.get("tool", "unknown")
+            latency = tc.get("latency_ms", 0)
+            if latency > 0:
+                tool_latencies[tool_name].append(latency)
+
+        # 3. Failed retries (same tool called 3+ times in sequence)
+        prev_tool = None
+        streak = 0
+        for tc in tool_calls:
+            tool_name = tc.get("tool", "unknown")
+            if tool_name == prev_tool:
+                streak += 1
+            else:
+                if streak >= 3:
+                    retry_counts[prev_tool] += 1
+                streak = 1
+            prev_tool = tool_name
+        if streak >= 3:
+            retry_counts[prev_tool] += 1
+
+        # 4. Manual processes (10+ sequential tool calls with no automation)
+        if len(tool_calls) > 10:
+            tool_sequence = [tc.get("tool") for tc in tool_calls]
+            unique_tools = set(tool_sequence)
+            if len(unique_tools) <= 3 and len(tool_calls) > 10:
+                manual_sequences.append({
+                    "session_id": session_id,
+                    "repo": repo,
+                    "tool_count": len(tool_calls),
+                    "unique_tools": list(unique_tools),
+                    "duration_min": duration_min,
+                })
+
+    # Generate patterns from collected data
+
+    # Repeated errors (appearing in 3+ sessions)
+    for err_key, occurrences in error_counts.items():
+        if len(occurrences) >= 3:
+            repos = list(set(o["repo"] for o in occurrences))
+            sessions_list = [o["session_id"] for o in occurrences[:5]]
+            # Estimate time wasted: 5 min per error occurrence
+            hours = len(occurrences) * 5 / 60
+            patterns.append(WastePattern(
+                pattern_type="repeated_error",
+                description=f"Error: {err_key[:100]}",
+                occurrences=len(occurrences),
+                total_time_hours=round(hours, 1),
+                affected_repos=repos,
+                example_sessions=sessions_list,
+            ))
+
+    # Slow tools (avg latency > 5000ms across 5+ calls)
+    for tool, latencies in tool_latencies.items():
+        if len(latencies) >= 5:
+            avg_ms = sum(latencies) / len(latencies)
+            if avg_ms > 5000:
+                hours = sum(latencies) / 1000 / 3600
+                patterns.append(WastePattern(
+                    pattern_type="slow_tool",
+                    description=f"Tool '{tool}' averages {avg_ms:.0f}ms latency",
+                    occurrences=len(latencies),
+                    total_time_hours=round(hours, 1),
+                    affected_repos=["global"],
+                    example_sessions=[],
+                ))
+
+    # Failed retries
+    for tool, count in retry_counts.items():
+        if count >= 2:
+            hours = count * 10 / 60  # ~10 min per failed retry sequence
+            patterns.append(WastePattern(
+                pattern_type="failed_retry",
+                description=f"Tool '{tool}' had {count} retry sequences (3+ consecutive calls)",
+                occurrences=count,
+                total_time_hours=round(hours, 1),
+                affected_repos=["global"],
+                example_sessions=[],
+            ))
+
+    # Manual processes
+    for seq in manual_sequences:
+        hours = seq["duration_min"] / 60
+        patterns.append(WastePattern(
+            pattern_type="manual_process",
+            description=f"Session {seq['session_id'][:12]}: {seq['tool_count']} tool calls with only {len(seq['unique_tools'])} unique tools",
+            occurrences=1,
+            total_time_hours=round(hours, 1),
+            affected_repos=[seq["repo"]],
+            example_sessions=[seq["session_id"]],
+        ))
+
+    return sorted(patterns, key=lambda p: p.total_time_hours, reverse=True)
+
+
+def _normalize_error(message: str) -> str:
+    """Normalize error message to a common key."""
+    if not message:
+        return ""
+    msg = message.lower().strip()
+    # Remove variable parts (paths, IDs, timestamps)
+    import re
+    msg = re.sub(r'/\S+', '/PATH', msg)
+    msg = re.sub(r'\b[0-9a-f]{8,}\b', 'HASH', msg)
+    msg = re.sub(r'\d{4}-\d{2}-\d{2}[tT]\d{2}:\d{2}', 'TIME', msg)
+    return msg[:150]
+
+
+# ── Proposal generation ───────────────────────────────────────
+
+def generate_proposals(
+    patterns: List[WastePattern],
+    hourly_rate: float = 50.0,
+    implementation_overhead: float = 1.5,
+) -> List[ImprovementProposal]:
+    """
+    Generate improvement proposals from waste patterns.
+
+    Args:
+        patterns: Detected waste patterns
+        hourly_rate: Developer hourly rate for ROI calculation
+        implementation_overhead: Multiplier for implementation time estimate
+    """
+    proposals = []
+
+    # Group patterns by type
+    by_type: Dict[str, List[WastePattern]] = defaultdict(list)
+    for p in patterns:
+        by_type[p.pattern_type].append(p)
+
+    # 1. Repeated errors → Create fix/skill
+    for p in by_type.get("repeated_error", []):
+        weekly_hours = p.total_time_hours / 4  # monthly → weekly
+        impl_hours = max(1.0, p.occurrences * 0.25) * implementation_overhead
+        roi_weeks = impl_hours / weekly_hours if weekly_hours > 0 else float('inf')
+
+        proposals.append(ImprovementProposal(
+            title=f"Fix repeated error: {p.description[:60]}",
+            problem=f"This error occurred {p.occurrences} times across {len(p.affected_repos)} repos, wasting ~{p.total_time_hours:.1f} hours.",
+            proposed_solution="Root-cause the error and create a permanent fix or mitigation skill.",
+            estimated_weekly_hours_saved=round(weekly_hours, 1),
+            estimated_monthly_hours_saved=round(p.total_time_hours, 1),
+            implementation_hours=round(impl_hours, 1),
+            roi_weeks=round(roi_weeks, 1),
+            priority=_priority_from_roi(roi_weeks),
+            affected_area="reliability",
+            supporting_evidence=[f"{p.occurrences} occurrences in sessions: {', '.join(p.example_sessions[:3])}"],
+        ))
+
+    # 2. Slow tools → Optimize or replace
+    for p in by_type.get("slow_tool", []):
+        weekly_hours = p.total_time_hours / 4
+        impl_hours = 3.0 * implementation_overhead  # optimization task
+        roi_weeks = impl_hours / weekly_hours if weekly_hours > 0 else float('inf')
+
+        proposals.append(ImprovementProposal(
+            title=f"Optimize slow tool: {p.description[:60]}",
+            problem=f"Tool has {p.occurrences} calls with high latency, wasting ~{p.total_time_hours:.1f} hours total.",
+            proposed_solution="Profile the tool, add caching, or replace with a faster alternative.",
+            estimated_weekly_hours_saved=round(weekly_hours, 1),
+            estimated_monthly_hours_saved=round(p.total_time_hours, 1),
+            implementation_hours=round(impl_hours, 1),
+            roi_weeks=round(roi_weeks, 1),
+            priority=_priority_from_roi(roi_weeks),
+            affected_area="performance",
+            supporting_evidence=[f"{p.occurrences} slow calls detected"],
+        ))
+
+    # 3. Failed retries → Add retry logic or validation
+    for p in by_type.get("failed_retry", []):
+        weekly_hours = p.total_time_hours / 4
+        impl_hours = 2.0 * implementation_overhead
+        roi_weeks = impl_hours / weekly_hours if weekly_hours > 0 else float('inf')
+
+        proposals.append(ImprovementProposal(
+            title=f"Reduce retries for tool '{p.description[:50]}'",
+            problem=f"Tool had {p.occurrences} retry sequences, wasting ~{p.total_time_hours:.1f} hours.",
+            proposed_solution="Add input validation, pre-flight checks, or automatic retry with backoff.",
+            estimated_weekly_hours_saved=round(weekly_hours, 1),
+            estimated_monthly_hours_saved=round(p.total_time_hours, 1),
+            implementation_hours=round(impl_hours, 1),
+            roi_weeks=round(roi_weeks, 1),
+            priority=_priority_from_roi(roi_weeks),
+            affected_area="reliability",
+            supporting_evidence=[f"{p.occurrences} retry sequences detected"],
+        ))
+
+    # 4. Manual processes → Automate
+    total_manual_hours = sum(p.total_time_hours for p in by_type.get("manual_process", []))
+    manual_patterns = by_type.get("manual_process", [])
+    if manual_patterns:
+        weekly_hours = total_manual_hours / 4
+        impl_hours = len(manual_patterns) * 2.0 * implementation_overhead
+        roi_weeks = impl_hours / weekly_hours if weekly_hours > 0 else float('inf')
+
+        proposals.append(ImprovementProposal(
+            title=f"Automate {len(manual_patterns)} manual processes",
+            problem=f"{len(manual_patterns)} sessions had long manual tool sequences, wasting ~{total_manual_hours:.1f} hours.",
+            proposed_solution="Create composite skills or scripts that combine the repeated tool sequences into single operations.",
+            estimated_weekly_hours_saved=round(weekly_hours, 1),
+            estimated_monthly_hours_saved=round(total_manual_hours, 1),
+            implementation_hours=round(impl_hours, 1),
+            roi_weeks=round(roi_weeks, 1),
+            priority=_priority_from_roi(roi_weeks),
+            affected_area="automation",
+            supporting_evidence=[f"{len(manual_patterns)} manual sessions detected"],
+        ))
+
+    return sorted(proposals, key=lambda p: p.estimated_monthly_hours_saved, reverse=True)
+
+
+def _priority_from_roi(roi_weeks: float) -> str:
+    """Determine priority from ROI break-even time."""
+    if roi_weeks <= 1:
+        return "critical"
+    elif roi_weeks <= 4:
+        return "high"
+    elif roi_weeks <= 12:
+        return "medium"
+    return "low"
+
+
+# ── Output formatting ─────────────────────────────────────────
+
+def format_proposals_markdown(
+    proposals: List[ImprovementProposal],
+    patterns: List[WastePattern],
+    generated_at: str,
+) -> str:
+    """Format proposals as a markdown document."""
+    lines = [
+        "# Improvement Proposals",
+        "",
+        f"Generated: {generated_at}",
+        f"Based on analysis of {sum(p.occurrences for p in patterns)} waste events across {len(set(r for p in patterns for r in p.affected_repos))} repos.",
+        "",
+        "---",
+        "",
+        "## Summary",
+        "",
+        f"| Metric | Value |",
+        f"|--------|-------|",
+        f"| Total proposals | {len(proposals)} |",
+        f"| Critical priority | {sum(1 for p in proposals if p.priority == 'critical')} |",
+        f"| Total monthly hours wasted | {sum(p.estimated_monthly_hours_saved for p in proposals):.1f}h |",
+        f"| Total weekly hours recoverable | {sum(p.estimated_weekly_hours_saved for p in proposals):.1f}h |",
+        f"| Implementation investment | {sum(p.implementation_hours for p in proposals):.1f}h |",
+        "",
+        "---",
+        "",
+    ]
+
+    for i, prop in enumerate(proposals, 1):
+        lines.extend([
+            f"## {i}. {prop.title}",
+            "",
+            f"**Priority:** {prop.priority.upper()}  ",
+            f"**Area:** {prop.affected_area}  ",
+            f"**ROI break-even:** {prop.roi_weeks:.1f} weeks",
+            "",
+            "### Problem",
+            "",
+            prop.problem,
+            "",
+            "### Proposed Solution",
+            "",
+            prop.proposed_solution,
+            "",
+            "### ROI Estimate",
+            "",
+            f"- Weekly hours saved: **{prop.estimated_weekly_hours_saved}h**",
+            f"- Monthly hours saved: **{prop.estimated_monthly_hours_saved}h**",
+            f"- Implementation time: **{prop.implementation_hours}h**",
+            f"- Break-even: **{prop.roi_weeks:.1f} weeks**",
+            "",
+        ])
+
+        if prop.supporting_evidence:
+            lines.extend([
+                "### Evidence",
+                "",
+            ])
+            for ev in prop.supporting_evidence:
+                lines.append(f"- {ev}")
+            lines.append("")
+
+        lines.extend(["---", ""])
+
+    # Waste pattern appendix
+    lines.extend([
+        "## Appendix: Detected Waste Patterns",
+        "",
+        "| Type | Description | Occurrences | Hours |",
+        "|------|-------------|-------------|-------|",
+    ])
+    for p in patterns[:20]:
+        lines.append(
+            f"| {p.pattern_type} | {p.description[:60]} | {p.occurrences} | {p.total_time_hours}h |"
+        )
+    lines.append("")
+
+    return "\n".join(lines)
+
+
+def format_proposals_json(proposals: List[ImprovementProposal]) -> str:
+    """Format proposals as JSON."""
+    return json.dumps(
+        [asdict(p) for p in proposals],
+        indent=2,
+        default=str,
+    )
+
+
+# ── Main ──────────────────────────────────────────────────────
+
+def main():
+    parser = argparse.ArgumentParser(description="Generate improvement proposals from session analytics")
+    parser.add_argument("--input", required=True, help="Path to session analytics JSON file")
+    parser.add_argument("--output", help="Output file path (default: stdout)")
+    parser.add_argument("--format", choices=["markdown", "json"], default="markdown", help="Output format")
+    parser.add_argument("--hourly-rate", type=float, default=50.0, help="Developer hourly rate for ROI calc")
+    parser.add_argument("--threshold", type=float, default=2.0, help="Min monthly hours to include proposal")
+
+    args = parser.parse_args()
+
+    with open(args.input) as f:
+        data = json.load(f)
+
+    sessions = data if isinstance(data, list) else data.get("sessions", [])
+
+    if not sessions:
+        print("No sessions found in input file.", file=sys.stderr)
+        sys.exit(1)
+
+    # Analyze
+    patterns = analyze_sessions(sessions)
+    proposals = generate_proposals(patterns, hourly_rate=args.hourly_rate)
+
+    # Filter by threshold
+    proposals = [p for p in proposals if p.estimated_monthly_hours_saved >= args.threshold]
+
+    generated_at = datetime.now(timezone.utc).isoformat()
+
+    if args.format == "markdown":
+        output = format_proposals_markdown(proposals, patterns, generated_at)
+    else:
+        output = format_proposals_json(proposals)
+
+    if args.output:
+        with open(args.output, "w") as f:
+            f.write(output)
+        print(f"Wrote {len(proposals)} proposals to {args.output}", file=sys.stderr)
+    else:
+        print(output)
+
+
+if __name__ == "__main__":
+    main()