Skill: rmarji/auto-optimizer (version 1.2.0)

--- name: auto-optimizer description: > Guide someone through optimizing anything using iterative self-improvement loops, OR run autonomous optimization loops with binary eval scoring. Systematically improve prompts, skills, websites, emails, landing pages, code, ad copy, prediction strategies, or any artifact through automated eval-mutate-select cycles. Supports two modes: binary eval (batch scoring with yes/no criteria, best for soft domains like copy/prompts/UX) and scalar eval (single float metric with git revert, best for code/ML/configs). Use this skill whenever someone says: "optimize", "improve", "make better", "iterate on", "benchmark", "eval", "self-improving", "test and improve", "split test", "A/B test", "improve my prompt", "optimize performance", "increase conversion", "improve accuracy", "score higher", "this isn't good enough", "how do I make this better", "run autoresearch", "start optimization loop", "run experiments on", "improve my metric", "autoresearch loop", "autonomous optimization", "karpathy loop", or any variation of wanting to measurably improve something over time. Also trigger for setting up automated quality loops, evaluation criteria, or self-improving systems. --- # Auto-Optimizer Zero-friction optimization engine. Tell your OpenClaw agent "optimize my [thing]" and it runs autonomous improve-eval-keep/revert cycles until your metric goes up. Works on anything: copy, prompts, code, prediction strategies, configs. **Results in under 5 minutes. Zero configuration required for demos.** --- ## Quick Setup (AI Agent Instructions) Follow these steps exactly. No judgment calls needed. ### Step 1 — Install ```bash clawhub install auto-optimizer ``` ### Step 2 — Initialize a git repo (required) The optimizer uses git to revert bad changes. Must be in a git repo. ```bash cd your-project git init && git add . && git commit -m "baseline" ``` ### Step 3 — Pick your mode **Scalar mode** — you have a command that outputs a single number (test score, Brier score, conversion rate, word count, etc.) **Binary mode** — you want to evaluate quality with yes/no criteria (copy quality, prompt effectiveness, UX clarity) ### Step 4 — Run it **Scalar mode:** ```bash ./skills/auto-optimizer/auto-optimizer.sh \ --file ./your-file.md \ --metric "bash ./your-metric.sh" \ --budget 10 ``` **Binary mode:** ```bash ./skills/auto-optimizer/auto-optimizer.sh \ --eval-mode binary \ --file ./your-file.md \ --evals ./your-evals.md \ --batch-size 10 \ --budget 10 ``` **Not sure? Use the wizard:** ```bash ./skills/auto-optimizer/auto-optimizer.sh --wizard ``` --- ## Pre-Built Starter Packs Three self-contained demos that run immediately. No files to create, no config needed. ### Demo 1: Cold Outreach Optimizer Optimizes a cold email template using a mock scoring metric (hook strength + clarity + CTA quality + length). ```bash ./skills/auto-optimizer/auto-optimizer.sh --demo outreach --budget 5 ``` **What it does:** - Creates a sample outreach template in `/tmp/demo-outreach/outreach.md` - Runs a mock metric that scores: hook ≤15 words, single CTA, body ≤120 words, value prop clarity - Iterates 5 times, keeps improvements, reverts regressions - Prints a final report showing baseline → best score **Sample outreach template used:** ``` Subject: Quick question about [Company] Hi [Name], I wanted to reach out because I've been following [Company]'s work and think there might be a great opportunity for us to collaborate. We help companies like yours improve their sales process using AI-powered outreach tools. Our clients typically see a 3x improvement in reply rates within the first month. Would you be open to a 15-minute call next week to explore if this could be valuable for [Company]? Looking forward to hearing from you, [Your Name] ``` **Mock metric logic (inline in demo):** ```bash # Score 0-100 based on: # - Hook length <= 15 words: +25 pts # - Single CTA (not multiple asks): +25 pts # - Body <= 120 words: +25 pts # - Contains specific value/number: +25 pts ``` ### Demo 2: Prediction Market Strategy Runs the optimization loop on a prediction strategy file, scoring by mock accuracy. ```bash ./skills/auto-optimizer/auto-optimizer.sh --demo prediction --budget 5 ``` **What it does:** - Creates a sample prediction strategy in `/tmp/demo-prediction/strategy.md` - Runs a mock metric that scores: specificity of criteria, use of base rates, calibration language - Shows how the loop works on structured reasoning files ### Demo 3: Prompt Quality Optimizer (Binary Mode) Optimizes a system prompt using 5 yes/no quality criteria. ```bash ./skills/auto-optimizer/auto-optimizer.sh --demo prompt --budget 5 --eval-mode binary ``` **What it does:** - Creates a sample system prompt in `/tmp/demo-prompt/system-prompt.md` - Evaluates each iteration against 5 criteria (inline): 1. Does the prompt specify a clear role/persona? 2. Does it include explicit output format instructions? 3. Does it define what NOT to do? 4. Is it under 500 words? 5. Does it include at least one concrete example? - Batch size 10: generates 10 outputs, scores each, calculates pass rate % - Keeps versions that increase the pass rate **Sample system prompt used:** ``` You are a helpful assistant. Answer questions clearly and accurately. Be concise but thorough. Help the user accomplish their goals. ``` --- ## Full Capability Guide ### `--wizard` — Interactive Setup Walks you through setup interactively. Best when you're not sure which mode to use. ```bash ./skills/auto-optimizer/auto-optimizer.sh --wizard ``` Prompts you to choose: 1. Cold outreach / email copy → sets up binary mode with outreach evals 2. LLM prompt / system prompt → sets up binary mode with prompt quality evals 3. Prediction market strategy → sets up scalar mode with accuracy metric 4. Code / config file → sets up scalar mode, prompts for your test command 5. Custom → asks for your file and metric ### `--eval-mode scalar` (default) **When to use:** Anything with a measurable number. Test pass rate, Brier score, word count, latency, revenue, API response score. **Requirements:** Your `--metric` command must print a single float to stdout. ```bash # Example metric commands: --metric "python test_score.py" # outputs: 0.847 --metric "bash run_eval.sh | tail -1" # outputs: 73.2 --metric "node score.js" # outputs: 0.91 ``` **How it works:** Runs metric → agent proposes change → run metric again → if improved, commit; else `git checkout` to revert. ### `--eval-mode binary` **When to use:** Copy, prompts, UX, anything where quality is multi-dimensional and hard to reduce to one number. **Requirements:** An evals file (markdown list of yes/no criteria) and a `--batch-size` (default 10). ```bash # Example evals.md: 1. Is the hook under 15 words? 2. Is there exactly one call-to-action? 3. Does it mention a specific outcome or number? 4. Is the total length under 150 words? 5. Does it address a specific pain point? ``` **How it works:** For each iteration, generates `batch-size` outputs from the current file, scores each against all criteria, calculates overall pass % → agent proposes change → compare pass % → keep or revert. ### `--budget N` Number of optimization iterations to run. Each iteration = one agent call + one eval. | Budget | Time (approx) | Best for | |--------|---------------|----------| | 5 | ~2 min | Quick demo, sanity check | | 10 | ~5 min | Initial optimization pass | | 20 | ~10 min | Production runs | | 50+ | ~30 min | Overnight deep optimization | **Minimum effective budget:** 5 iterations. Below 5, not enough signal. ### `--goal minimize` / `--goal maximize` (default: maximize) ```bash # Minimize (e.g., Brier score, error rate, latency): --goal minimize --metric "python score_brier.py" # Maximize (default — e.g., accuracy, pass rate, revenue): --metric "python score_accuracy.py" ``` ### `--session NAME` Name your session for organized results. Results saved to `./skills/auto-optimizer/results/NAME/`. ```bash --session "outreach-v2-$(date +%Y%m%d)" ``` ### `--batch-size N` (binary mode only) How many outputs to generate per iteration for scoring. Higher = more reliable signal, slower. - 5 = fast, less reliable - 10 = balanced (default) - 20 = slow, high confidence ### Hypothesis Memory Every iteration is logged to `results/SESSION/hypothesis_log.jsonl`. The agent reads the last 5 entries before each iteration, so it never retries approaches that already failed. This is what makes multi-iteration runs productive rather than random. The optimizer builds on what worked, avoids what didn't. --- ## OpenClaw Integration ### The natural way (just tell your agent) ``` "Run auto-optimizer on ./outreach.md optimizing for reply rate, 20 iterations" "Optimize my system prompt at ./prompts/classifier.md using binary eval mode" "Start an overnight optimization loop on my prediction strategy, minimize Brier score, budget 50" "Set up auto-optimizer for my cold outreach template" ``` Your OpenClaw agent reads this SKILL.md, picks the right mode, sets up the files, and runs the loop. ### Direct invocation ```bash # Outreach optimization (binary) ./skills/auto-optimizer/auto-optimizer.sh \ --eval-mode binary \ --file ./outreach.md \ --evals ./evals/outreach-evals.md \ --batch-size 10 \ --budget 20 \ --session "outreach-$(date +%Y%m%d)" # Prediction strategy (scalar, minimize) ./skills/auto-optimizer/auto-optimizer.sh \ --file ./strategy.md \ --metric "python eval_strategy.py" \ --goal minimize \ --budget 30 \ --session "prediction-strategy-v2" # Code optimization (scalar, maximize test score) ./skills/auto-optimizer/auto-optimizer.sh \ --file ./src/classifier.py \ --metric "python -m pytest tests/ -q 2>&1 | grep -oP '\d+(?= passed)'" \ --budget 20 \ --session "classifier-v2" ``` --- ## Real Results (What to Expect) From actual runs: **Prediction market strategy — 5 iterations:** - Brier score: 0.23642 → 0.23563 (↓ better) - ROI: +2.83% → +6.25% - What changed: added base rate anchoring, tightened confidence thresholds **Cold outreach template — 10 iterations:** - Binary pass rate: 60% → 85% - Version: v1.0 → v1.1 - What changed: shorter hook (22 words → 11 words), single clear CTA, ICP-specific pain point added **System prompt — 20 iterations (binary):** - Pass rate: 40% → 92% - What changed: added explicit persona, output format section, negative constraints, inline example Typical pattern: - Iterations 1-3: big gains (low-hanging fruit) - Iterations 4-10: diminishing returns, more targeted changes - Iterations 10+: fine-tuning, marginal improvements --- ## Troubleshooting **"Not a git repo"** ```bash cd your-project && git init && git add . && git commit -m "baseline" ``` **"Metric command failed" or returns 0 always** Your metric command must print a single float to stdout. Test it standalone: ```bash bash ./your-metric.sh # Should output: 73.5 ``` If it outputs anything else (multiline, text, nothing), wrap it: ```bash --metric "bash ./your-metric.sh | grep -oE '[0-9]+\.?[0-9]*' | tail -1" ``` **"claude CLI not found"** Option A: Install claude CLI globally: `npm install -g @anthropic-ai/claude-code` Option B: The script falls back to OpenClaw's claude-code skill automatically if `skills/claude-code/claude-code.sh` exists. **"ERROR: --evals is required for binary eval mode"** Binary mode needs an evals file with numbered criteria: ```bash cat > ./my-evals.md << 'EOF' 1. Is the hook under 15 words? 2. Is there exactly one CTA? 3. Does it mention a specific number or outcome? 4. Is the total under 150 words? 5. Does it address a specific pain point? EOF ``` **"Budget too low"** Minimum 5 iterations to see meaningful improvement. Use `--budget 10` for first runs. **Results not improving after many iterations** - Check hypothesis log: `cat results/SESSION/hypothesis_log.jsonl` - The agent may be stuck in a local optimum — try a new session with `--session new-run-v2` - Consider rewriting the evals/metric to be more discriminating **"program.md.template not found"** ```bash ls skills/auto-optimizer/ # Should show: auto-optimizer.sh, SKILL.md, program.md.template, results/ ``` If missing, reinstall: `clawhub install auto-optimizer` --- ## MiroFish Integration MiroFish is a swarm intelligence engine that runs thousands of AI agents to simulate outcomes and generate prediction reports. Combined with auto-optimizer, you can autonomously improve the "seed" inputs that drive MiroFish simulations. ### What MiroFish does - Takes a "seed" (market data, news, signals) as input - Runs multi-agent social simulation in a digital world (Twitter + Reddit environments) - Simulates distinct personas: RetailTrader, WhaleInvestor, AlgorithmicTrader, etc. - Outputs agent actions, discussions, and quantitative probability estimates ### The combination - **Mutable asset:** the seed file / prediction prompt sent to MiroFish - **Scalar metric:** confidence score OR prediction accuracy on known outcomes - **Loop:** auto-optimizer iterates on the seed to maximize simulation confidence ### Proven optimization results (2026-03-26 run) | Iteration | Seed Content | Confidence Score | Delta | |-----------|-------------|-----------------|-------| | Baseline | Price + Fear/Greed only | 0.35 | — | | Iter 1 | + Technical signals (TTM Squeeze, funding rates) | 0.58 | +0.23 ✅ | | Iter 2 | + Whale on-chain data + price levels | 0.67 | +0.09 ✅ | | Iter 3 | + Cross-asset correlation (BTC) | 0.61 | specific question | **Key insight:** Adding technical structure data (funding rates, squeeze, key levels) produces the biggest confidence boost. Whale on-chain context is the #2 improvement driver. ### Setup MiroFish must be running (backend on port 5001): ```bash cd /data/workspace-crypto/mirofish npm run backend # Or check status: curl http://localhost:5001/health ``` Build a seed from live market data: ```bash cd /data/workspace-crypto .venv/bin/python3 -m src.alpha.mirofish_adapter ``` Run a simulation manually via the API: ```bash # 1. Get or create a project_id curl http://localhost:5001/api/graph/project/list # 2. Create simulation curl -X POST http://localhost:5001/api/simulation/create \ -H "Content-Type: application/json" \ -d '{"project_id": "proj_XXXX"}' # 3. Start simulation curl -X POST http://localhost:5001/api/simulation/start \ -H "Content-Type: application/json" \ -d '{"simulation_id": "sim_XXXX"}' # 4. Check status curl http://localhost:5001/api/simulation/sim_XXXX/run-status # 5. Get agent actions (raw predictions) curl http://localhost:5001/api/simulation/sim_XXXX/run-status/detail ``` ### Scoring function for auto-optimizer Extract confidence from MiroFish agent actions: ```python import urllib.request, json def score_mirofish_seed(simulation_id: str) -> float: """Score a MiroFish simulation — returns 0.0-1.0 confidence.""" url = f"http://localhost:5001/api/simulation/{simulation_id}/run-status/detail" with urllib.request.urlopen(url) as r: d = json.loads(r.read()) actions = d['data']['all_actions'] bull_words = ['bullish','accumulation','buy','upside','reversal','support'] bear_words = ['bearish','sell','drop','fear','panic','bleeding'] bull = sum(1 for a in actions for w in bull_words if w in a.get('action_args',{}).get('content','').lower()) bear = sum(1 for a in actions for w in bear_words if w in a.get('action_args',{}).get('content','').lower()) total = bull + bear return bull / total if total > 0 else 0.5 ``` ### Use cases - **Crypto market predictions** — BTC/ETH/SOL price direction (24-72h) - **Prediction market research** — Polymarket/Kalshi question research - **Seed quality optimization** — find which data signals drive the highest swarm confidence - **Any "what if" scenario** you want to simulate at scale with diverse AI personas ### Files - Seed builder: `/data/workspace-crypto/src/alpha/mirofish_adapter.py` - Results example: `/data/workspace-crypto/mirofish-autooptimizer-results.md` - MiroFish API: `http://localhost:5001` (Flask backend, port 5001)

path: SETUP.md
content: # Auto-Optimizer Setup Guide

## For OpenClaw Users (Easiest)

Tell your agent:

```
"Install and set up auto-optimizer for cold outreach"
"Set up auto-optimizer on my prediction strategy file"
"Optimize my system prompt at ./prompts/assistant.md"
```

That's it. Your agent reads the SKILL.md, installs the skill, sets up the files, picks the right mode, and runs the loop. You get results in under 5 minutes.

---

## For Manual Setup (Human-Friendly)

### 1. Install

```bash
clawhub install auto-optimizer
```

Or clone directly:
```bash
git clone https://github.com/rmarji/autoresearch-openclaw.git
cp -r autoresearch-openclaw/skills/auto-optimizer ./skills/
```

### 2. Make sure you're in a git repo

The optimizer uses git to revert bad iterations. If you're not in a git repo:

```bash
cd your-project
git init && git add . && git commit -m "baseline"
```

### 3. Try a demo first

```bash
# Run the outreach demo (no files needed, creates everything)
./skills/auto-optimizer/auto-optimizer.sh --demo outreach --budget 5

# Or the prompt quality demo
./skills/auto-optimizer/auto-optimizer.sh --demo prompt --budget 5 --eval-mode binary
```

### 4. Run on your own file

**If you have a measurable metric (test score, accuracy, etc.):**
```bash
./skills/auto-optimizer/auto-optimizer.sh \
  --file ./your-file.md \
  --metric "bash ./your-metric.sh" \
  --budget 10
```

**If you want yes/no quality evaluation (copy, prompts, UX):**

First create an evals file:
```bash
cat > ./my-evals.md << 'EOF'
1. Is the hook under 15 words?
2. Is there exactly one call-to-action?
3. Does it mention a specific outcome or number?
4. Is the total under 150 words?
5. Does it address a specific pain point?
EOF
```

Then run:
```bash
./skills/auto-optimizer/auto-optimizer.sh \
  --eval-mode binary \
  --file ./your-file.md \
  --evals ./my-evals.md \
  --batch-size 10 \
  --budget 10
```

### 5. Not sure? Use the wizard

```bash
./skills/auto-optimizer/auto-optimizer.sh --wizard
```

---

## For AI Agents Setting This Up

Follow these exact steps. No ambiguity.

### Prerequisites check

```bash
# Check git
git --version || echo "MISSING: install git"

# Check claude CLI
claude --version || echo "MISSING: npm install -g @anthropic-ai/claude-code"

# Check bc and jq (used by the script)
bc --version && jq --version || echo "MISSING: apt-get install bc jq"
```

### Step 1: Install the skill

```bash
clawhub install auto-optimizer
# Installs to ./skills/auto-optimizer/
```

### Step 2: Ensure the target file is in a git repo

```bash
# Check if target dir is a git repo
git -C /path/to/target/dir rev-parse --show-toplevel 2>/dev/null || {
  cd /path/to/target/dir
  git init
  git add .
  git commit -m "baseline before auto-optimizer"
}
```

### Step 3: Determine the right mode

| Situation | Mode | Required flags |
|-----------|------|----------------|
| File with a test command | scalar | `--metric "your test cmd"` |
| Copy / prompt / UX quality | binary | `--eval-mode binary --evals ./evals.md` |
| Not sure | wizard | `--wizard` |

### Step 4: Run

**Scalar:**
```bash
./skills/auto-optimizer/auto-optimizer.sh \
  --file /path/to/target.md \
  --metric "bash /path/to/metric.sh" \
  --goal maximize \
  --budget 10 \
  --session "my-optimization-$(date +%Y%m%d)"
```

**Binary:**
```bash
# Create evals file first with numbered yes/no criteria
cat > /tmp/my-evals.md << 'EOF'
1. [Criterion 1 — yes/no question]
2. [Criterion 2 — yes/no question]
3. [Criterion 3 — yes/no question]
4. [Criterion 4 — yes/no question]
5. [Criterion 5 — yes/no question]
EOF

./skills/auto-optimizer/auto-optimizer.sh \
  --eval-mode binary \
  --file /path/to/target.md \
  --evals /tmp/my-evals.md \
  --batch-size 10 \
  --budget 10 \
  --session "my-optimization-$(date +%Y%m%d)"
```

### Step 5: Report results back

Results are saved to:
```
./skills/auto-optimizer/results/SESSION_NAME/
├── report.md          # Full summary
├── results.tsv        # Iteration-by-iteration log
├── best.md            # Best version of the file
└── hypothesis_log.jsonl  # What was tried and why
```

Read the report:
```bash
cat ./skills/auto-optimizer/results/SESSION_NAME/report.md
```

---

## Common Error Recovery

| Error | Fix |
|-------|-----|
| "Not a git repo" | `git init && git add . && git commit -m "baseline"` |
| "Metric command failed" | Test standalone: `bash ./metric.sh` — must output a float |
| "claude CLI not found" | `npm install -g @anthropic-ai/claude-code` |
| "--evals required" | Create a file with numbered yes/no criteria, pass via `--evals` |
| "program.md.template not found" | Reinstall: `clawhub install auto-optimizer` |
| "Budget too low" | Use minimum `--budget 5` |


path: auto-optimizer.sh
content: #!/usr/bin/env bash
# =============================================================================
# auto-optimizer.sh — Universal Optimization Loop
# Combines Karpathy's scalar autoresearch with Rayo's binary eval framework.
# Includes hypothesis memory to prevent retrying failed approaches.
#
# Usage (scalar mode — hard metrics):
#   ./auto-optimizer.sh --metric "cmd" --file "path/to/file" [--budget 20]
#
# Usage (binary mode — soft domains):
#   ./auto-optimizer.sh --eval-mode binary --evals ./evals.md --file "path/to/file"
#
# Requirements: git, claude CLI (or fallback), bc, jq (for hypothesis log)
# =============================================================================

set -euo pipefail

SKILL_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"

# ── Wizard Mode ───────────────────────────────────────────────────────────────
if [[ "${1:-}" == "--wizard" ]]; then
  echo ""
  echo "╔══════════════════════════════════════════════════════════════╗"
  echo "║              🔧 Auto-Optimizer Setup Wizard                  ║"
  echo "╚══════════════════════════════════════════════════════════════╝"
  echo ""
  echo "What do you want to optimize?"
  echo "  1) Cold outreach / email copy"
  echo "  2) LLM prompt / system prompt"
  echo "  3) Prediction market strategy"
  echo "  4) Code / config file"
  echo "  5) Custom (bring your own)"
  echo ""
  read -r -p "Choice [1-5]: " CHOICE

  read -r -p "Path to file you want to optimize: " WIZARD_FILE
  read -r -p "Budget (iterations, default 10): " WIZARD_BUDGET
  WIZARD_BUDGET="${WIZARD_BUDGET:-10}"

  WIZARD_ARGS="--file $WIZARD_FILE --budget $WIZARD_BUDGET"

  case "$CHOICE" in
    1)
      echo ""
      echo "Setting up binary eval mode for outreach optimization."
      EVALS_PATH="/tmp/wizard-outreach-evals.md"
      cat > "$EVALS_PATH" <<'EVEOF'
1. Is the subject line under 10 words?
2. Is the hook (first sentence) under 15 words?
3. Is there exactly one call-to-action?
4. Does the email mention a specific outcome or number?
5. Is the total body under 150 words?
6. Does it address a specific pain point of the recipient?
EVEOF
      echo "Evals written to $EVALS_PATH"
      echo "Running: $0 --eval-mode binary --evals $EVALS_PATH $WIZARD_ARGS"
      exec "$0" --eval-mode binary --evals "$EVALS_PATH" $WIZARD_ARGS
      ;;
    2)
      echo ""
      echo "Setting up binary eval mode for prompt optimization."
      EVALS_PATH="/tmp/wizard-prompt-evals.md"
      cat > "$EVALS_PATH" <<'EVEOF'
1. Does the prompt specify a clear role or persona?
2. Does it include explicit output format instructions?
3. Does it define what NOT to do?
4. Is it under 500 words?
5. Does it include at least one concrete example?
EVEOF
      echo "Evals written to $EVALS_PATH"
      echo "Running: $0 --eval-mode binary --evals $EVALS_PATH $WIZARD_ARGS"
      exec "$0" --eval-mode binary --evals "$EVALS_PATH" $WIZARD_ARGS
      ;;
    3)
      echo ""
      echo "Setting up scalar mode for prediction strategy optimization."
      METRIC_PATH="/tmp/wizard-prediction-metric.sh"
      cat > "$METRIC_PATH" <<'METEOF'
#!/usr/bin/env bash
# Mock prediction metric — replace with real eval
# Scores the strategy file on specificity, base rates, calibration
FILE="${1:-./strategy.md}"
WORDS=$(wc -w < "$FILE" 2>/dev/null || echo 0)
HAS_BASERATES=$(grep -ci "base rate\|historical\|prior" "$FILE" 2>/dev/null || echo 0)
HAS_CALIBRATION=$(grep -ci "calibrat\|confident\|uncertain" "$FILE" 2>/dev/null || echo 0)
HAS_CRITERIA=$(grep -ci "criteri\|threshold\|signal" "$FILE" 2>/dev/null || echo 0)
# Score: more specific = better, but not too long
SCORE=$(echo "scale=4; ($HAS_BASERATES * 20 + $HAS_CALIBRATION * 20 + $HAS_CRITERIA * 20 + [[ $WORDS -gt 50 && $WORDS -lt 500 ]] && echo 40 || echo 0)" | bc -l 2>/dev/null || echo "0.5")
echo "$SCORE"
METEOF
      chmod +x "$METRIC_PATH"
      read -r -p "Minimize or maximize? (min/max, default max): " GOAL_INPUT
      GOAL_INPUT="${GOAL_INPUT:-max}"
      GOAL_FLAG="maximize"
      [[ "$GOAL_INPUT" == "min"* ]] && GOAL_FLAG="minimize"
      echo "Running: $0 --metric 'bash $METRIC_PATH' --goal $GOAL_FLAG $WIZARD_ARGS"
      exec "$0" --metric "bash $METRIC_PATH" --goal "$GOAL_FLAG" $WIZARD_ARGS
      ;;
    4)
      echo ""
      read -r -p "Enter your metric command (must output a float): " WIZARD_METRIC
      read -r -p "Minimize or maximize? (min/max, default max): " GOAL_INPUT
      GOAL_INPUT="${GOAL_INPUT:-max}"
      GOAL_FLAG="maximize"
      [[ "$GOAL_INPUT" == "min"* ]] && GOAL_FLAG="minimize"
      echo "Running: $0 --metric '$WIZARD_METRIC' --goal $GOAL_FLAG $WIZARD_ARGS"
      exec "$0" --metric "$WIZARD_METRIC" --goal "$GOAL_FLAG" $WIZARD_ARGS
      ;;
    5)
      echo ""
      echo "Custom mode. Choose eval type:"
      echo "  s) Scalar (metric command that outputs a float)"
      echo "  b) Binary (yes/no eval criteria file)"
      read -r -p "Type [s/b]: " EVAL_TYPE
      if [[ "$EVAL_TYPE" == "b"* ]]; then
        read -r -p "Path to evals.md file: " WIZARD_EVALS
        exec "$0" --eval-mode binary --evals "$WIZARD_EVALS" $WIZARD_ARGS
      else
        read -r -p "Metric command (outputs a float): " WIZARD_METRIC
        read -r -p "Goal (minimize/maximize): " GOAL_FLAG
        GOAL_FLAG="${GOAL_FLAG:-maximize}"
        exec "$0" --metric "$WIZARD_METRIC" --goal "$GOAL_FLAG" $WIZARD_ARGS
      fi
      ;;
    *)
      echo "Invalid choice. Run with --wizard again."
      exit 1
      ;;
  esac
fi

# ── Demo Mode ─────────────────────────────────────────────────────────────────
if [[ "${1:-}" == "--demo" ]]; then
  DEMO_TYPE="${2:-outreach}"
  DEMO_BUDGET="5"
  DEMO_EVAL_MODE="scalar"
  # Parse remaining args
  shift 2 || true
  while [[ $# -gt 0 ]]; do
    case "$1" in
      --budget)    DEMO_BUDGET="$2"; shift 2 ;;
      --eval-mode) DEMO_EVAL_MODE="$2"; shift 2 ;;
      *) shift ;;
    esac
  done

  echo ""
  echo "╔══════════════════════════════════════════════════════════════╗"
  echo "║              🚀 Auto-Optimizer Demo: $DEMO_TYPE              "
  echo "╚══════════════════════════════════════════════════════════════╝"
  echo ""

  case "$DEMO_TYPE" in
    outreach)
      DEMO_DIR="/tmp/demo-outreach-$$"
      mkdir -p "$DEMO_DIR"
      cd "$DEMO_DIR"
      git init -q && git config user.email "demo@auto-optimizer" && git config user.name "Auto-Optimizer Demo"

      cat > "$DEMO_DIR/outreach.md" <<'TMPLEOF'
Subject: Quick question about [Company]

Hi [Name],

I wanted to reach out because I've been following [Company]'s work and think there might be a great opportunity for us to collaborate.

We help companies like yours improve their sales process using AI-powered outreach tools. Our clients typically see a 3x improvement in reply rates within the first month.

Would you be open to a 15-minute call next week to explore if this could be valuable for [Company]? I'm also happy to share a few case studies from companies similar to yours if that would be helpful.

Looking forward to hearing from you,
[Your Name]
TMPLEOF

      cat > "$DEMO_DIR/metric.sh" <<'METRICEOF'
#!/usr/bin/env bash
# Outreach scorer: 0-100 based on best practices
FILE="${1:-./outreach.md}"
CONTENT=$(cat "$FILE" 2>/dev/null || echo "")
SCORE=0

# Hook under 15 words (first line after Subject:)
HOOK=$(echo "$CONTENT" | grep -v "^Subject:" | grep -v "^$" | head -1)
HOOK_WORDS=$(echo "$HOOK" | wc -w | tr -d ' ')
[[ "$HOOK_WORDS" -le 15 ]] && SCORE=$((SCORE + 25))

# Single CTA (count question marks)
QMARKS=$(echo "$CONTENT" | grep -o "?" | wc -l | tr -d ' ')
[[ "$QMARKS" -eq 1 ]] && SCORE=$((SCORE + 25))

# Body under 120 words
WORDS=$(echo "$CONTENT" | wc -w | tr -d ' ')
[[ "$WORDS" -le 120 ]] && SCORE=$((SCORE + 25))

# Contains specific value/number
echo "$CONTENT" | grep -qE '[0-9]+[x%]|[0-9]+ (percent|times|minutes|days)' && SCORE=$((SCORE + 25))

echo "$SCORE"
METRICEOF
      chmod +x "$DEMO_DIR/metric.sh"

      git add . && git commit -q -m "baseline"

      echo "Demo files created in $DEMO_DIR"
      echo "Running $DEMO_BUDGET iterations..."
      echo ""
      exec "$0" \
        --file "$DEMO_DIR/outreach.md" \
        --metric "bash $DEMO_DIR/metric.sh $DEMO_DIR/outreach.md" \
        --budget "$DEMO_BUDGET" \
        --session "demo-outreach-$$"
      ;;

    prediction)
      DEMO_DIR="/tmp/demo-prediction-$$"
      mkdir -p "$DEMO_DIR"
      cd "$DEMO_DIR"
      git init -q && git config user.email "demo@auto-optimizer" && git config user.name "Auto-Optimizer Demo"

      cat > "$DEMO_DIR/strategy.md" <<'STRATEOF'
# Prediction Strategy

When evaluating prediction markets, look for clear yes/no outcomes.

Consider the available information and make a judgment call about likelihood.

Bet when you feel confident.
STRATEOF

      cat > "$DEMO_DIR/metric.sh" <<'METRICEOF'
#!/usr/bin/env bash
# Prediction strategy scorer: higher = better calibration
FILE="${1:-./strategy.md}"
CONTENT=$(cat "$FILE" 2>/dev/null || echo "")
SCORE=0

# Has base rates / historical reference
echo "$CONTENT" | grep -qiE "base rate|historical|prior|base-rate" && SCORE=$((SCORE + 20))

# Has calibration language
echo "$CONTENT" | grep -qiE "calibrat|confident|uncertain|probability|likelihood" && SCORE=$((SCORE + 20))

# Has specific criteria / thresholds
echo "$CONTENT" | grep -qiE "threshold|signal|criteri|only (bet|trade) when" && SCORE=$((SCORE + 20))

# Has risk management
echo "$CONTENT" | grep -qiE "risk|kelly|position size|stake|bankroll" && SCORE=$((SCORE + 20))

# Sufficiently detailed (>100 words)
WORDS=$(echo "$CONTENT" | wc -w | tr -d ' ')
[[ "$WORDS" -gt 100 ]] && SCORE=$((SCORE + 20))

echo "$SCORE"
METRICEOF
      chmod +x "$DEMO_DIR/metric.sh"

      git add . && git commit -q -m "baseline"

      echo "Demo files created in $DEMO_DIR"
      echo "Running $DEMO_BUDGET iterations..."
      echo ""
      exec "$0" \
        --file "$DEMO_DIR/strategy.md" \
        --metric "bash $DEMO_DIR/metric.sh $DEMO_DIR/strategy.md" \
        --budget "$DEMO_BUDGET" \
        --session "demo-prediction-$$"
      ;;

    prompt)
      DEMO_DIR="/tmp/demo-prompt-$$"
      mkdir -p "$DEMO_DIR"
      cd "$DEMO_DIR"
      git init -q && git config user.email "demo@auto-optimizer" && git config user.name "Auto-Optimizer Demo"

      cat > "$DEMO_DIR/system-prompt.md" <<'PROMPTEOF'
You are a helpful assistant. Answer questions clearly and accurately.
Be concise but thorough. Help the user accomplish their goals.
PROMPTEOF

      cat > "$DEMO_DIR/evals.md" <<'EVALSEOF'
1. Does the prompt specify a clear role or persona (e.g., "You are a [specific role]...")?
2. Does it include explicit output format instructions (e.g., "Respond in bullet points", "Output JSON", etc.)?
3. Does it define at least one constraint on what NOT to do?
4. Is it under 500 words total?
5. Does it include at least one concrete example of desired behavior or output?
EVALSEOF

      git add . && git commit -q -m "baseline"

      echo "Demo files created in $DEMO_DIR"
      echo "Running $DEMO_BUDGET iterations in binary eval mode..."
      echo ""
      exec "$0" \
        --eval-mode binary \
        --file "$DEMO_DIR/system-prompt.md" \
        --evals "$DEMO_DIR/evals.md" \
        --batch-size 5 \
        --budget "$DEMO_BUDGET" \
        --session "demo-prompt-$$"
      ;;

    *)
      echo "Unknown demo type: $DEMO_TYPE"
      echo "Available: outreach, prediction, prompt"
      exit 1
      ;;
  esac
fi

# ── Defaults ─────────────────────────────────────────────────────────────────
METRIC_CMD=""
MUTABLE_FILE=""
BUDGET=20
SESSION_NAME="auto-optimizer-$(date +%Y%m%d-%H%M%S)"
GOAL="maximize"
PROGRAM_FILE=""
EVAL_MODE="scalar"
EVALS_FILE=""
BATCH_SIZE=""

# ── Parse args ────────────────────────────────────────────────────────────────
while [[ $# -gt 0 ]]; do
  case "$1" in
    --metric)     METRIC_CMD="$2";   shift 2 ;;
    --file)       MUTABLE_FILE="$2"; shift 2 ;;
    --budget)     BUDGET="$2";       shift 2 ;;
    --session)    SESSION_NAME="$2"; shift 2 ;;
    --goal)       GOAL="$2";         shift 2 ;;
    --program)    PROGRAM_FILE="$2"; shift 2 ;;
    --eval-mode)  EVAL_MODE="$2";    shift 2 ;;
    --evals)      EVALS_FILE="$2";   shift 2 ;;
    --batch-size) BATCH_SIZE="$2";   shift 2 ;;
    *) echo "Unknown argument: $1"; exit 1 ;;
  esac
done

# ── Set batch size default based on mode ─────────────────────────────────────
if [[ -z "$BATCH_SIZE" ]]; then
  if [[ "$EVAL_MODE" == "binary" ]]; then
    BATCH_SIZE=10
  else
    BATCH_SIZE=1
  fi
fi

# ── Validate ──────────────────────────────────────────────────────────────────
if [[ -z "$MUTABLE_FILE" ]]; then
  echo "ERROR: --file is required."
  echo "Usage: $0 --file <path> [--eval-mode binary|scalar] [--metric cmd] [--evals path]"
  exit 1
fi

if [[ "$EVAL_MODE" == "scalar" && -z "$METRIC_CMD" ]]; then
  echo "ERROR: --metric is required for scalar eval mode."
  exit 1
fi

if [[ "$EVAL_MODE" == "binary" && -z "$EVALS_FILE" ]]; then
  echo "ERROR: --evals is required for binary eval mode."
  exit 1
fi

if [[ "$EVAL_MODE" == "binary" && ! -f "$EVALS_FILE" ]]; then
  echo "ERROR: Evals file not found: $EVALS_FILE"
  exit 1
fi

if [[ ! -f "$MUTABLE_FILE" ]]; then
  echo "ERROR: Mutable file not found: $MUTABLE_FILE"
  exit 1
fi

# Ensure we're inside a git repo
REPO_ROOT=$(git -C "$(dirname "$MUTABLE_FILE")" rev-parse --show-toplevel 2>/dev/null || true)
if [[ -z "$REPO_ROOT" ]]; then
  echo "ERROR: $MUTABLE_FILE is not inside a git repository."
  echo "Run: git init && git add . && git commit -m 'initial' in the target directory."
  exit 1
fi

# ── Setup results dir ─────────────────────────────────────────────────────────
RESULTS_DIR="$SKILL_DIR/results/$SESSION_NAME"
mkdir -p "$RESULTS_DIR"
TSV_FILE="$RESULTS_DIR/results.tsv"
REPORT_FILE="$RESULTS_DIR/report.md"
BEST_FILE="$RESULTS_DIR/best.md"
HYPOTHESIS_LOG="$RESULTS_DIR/hypothesis_log.jsonl"

# Write TSV header
echo -e "iteration\tmetric_before\tmetric_after\tkept\tchange_desc" > "$TSV_FILE"

# Initialize hypothesis log
touch "$HYPOTHESIS_LOG"

# ── Helpers ───────────────────────────────────────────────────────────────────
log() { echo "[auto-optimizer] $*"; }

run_scalar_metric() {
  local result
  result=$(eval "$METRIC_CMD" 2>/dev/null | grep -oE '[0-9]+\.?[0-9]*([eE][+-]?[0-9]+)?' | tail -1)
  echo "${result:-0}"
}

is_improvement() {
  local before="$1" after="$2"
  if [[ "$GOAL" == "minimize" ]]; then
    echo "$after < $before" | bc -l
  else
    echo "$after > $before" | bc -l
  fi
}

# Log a hypothesis to the JSONL file
log_hypothesis() {
  local iter="$1" hypothesis="$2" change_desc="$3" metric_before="$4" metric_after="$5" kept="$6" reason="$7"
  local entry
  entry=$(jq -n \
    --argjson iter "$iter" \
    --arg hypothesis "$hypothesis" \
    --arg change_desc "$change_desc" \
    --arg metric_before "$metric_before" \
    --arg metric_after "$metric_after" \
    --argjson kept "$kept" \
    --arg reason "$reason" \
    '{iter: $iter, hypothesis: $hypothesis, change_desc: $change_desc, metric_before: $metric_before, metric_after: $metric_after, kept: $kept, reason: $reason}' 2>/dev/null || \
    echo "{\"iter\":$iter,\"hypothesis\":\"$hypothesis\",\"change_desc\":\"$change_desc\",\"metric_before\":\"$metric_before\",\"metric_after\":\"$metric_after\",\"kept\":$kept,\"reason\":\"$reason\"}")
  echo "$entry" >> "$HYPOTHESIS_LOG"
}

# Get last N entries from hypothesis log as context string
get_hypothesis_context() {
  local n="${1:-5}"
  if [[ ! -s "$HYPOTHESIS_LOG" ]]; then
    echo "(No previous iterations logged yet.)"
    return
  fi
  local count
  count=$(wc -l < "$HYPOTHESIS_LOG")
  if [[ "$count" -eq 0 ]]; then
    echo "(No previous iterations logged yet.)"
    return
  fi
  echo "## Previous Hypotheses (last $n iterations — do NOT retry failed approaches):"
  echo ""
  tail -n "$n" "$HYPOTHESIS_LOG" | while IFS= read -r line; do
    local iter hypothesis kept reason metric_before metric_after
    iter=$(echo "$line" | grep -o '"iter":[0-9]*' | grep -o '[0-9]*' || echo "?")
    hypothesis=$(echo "$line" | python3 -c "import sys,json; d=json.load(sys.stdin); print(d.get('hypothesis',''))" 2>/dev/null || echo "")
    change_desc=$(echo "$line" | python3 -c "import sys,json; d=json.load(sys.stdin); print(d.get('change_desc',''))" 2>/dev/null || echo "")
    kept=$(echo "$line" | python3 -c "import sys,json; d=json.load(sys.stdin); print('KEPT' if d.get('kept') else 'REVERTED')" 2>/dev/null || echo "?")
    metric_before=$(echo "$line" | python3 -c "import sys,json; d=json.load(sys.stdin); print(d.get('metric_before','?'))" 2>/dev/null || echo "?")
    metric_after=$(echo "$line" | python3 -c "import sys,json; d=json.load(sys.stdin); print(d.get('metric_after','?'))" 2>/dev/null || echo "?")
    reason=$(echo "$line" | python3 -c "import sys,json; d=json.load(sys.stdin); print(d.get('reason',''))" 2>/dev/null || echo "")
    echo "- Iter $iter [$kept] $metric_before → $metric_after: $change_desc"
    [[ -n "$reason" ]] && echo "  Reason: $reason"
  done
}

# Run binary eval: generate N outputs and score against evals.md
run_binary_eval() {
  local file="$1" evals="$2" batch="$3" iter="$4"
  local hypothesis_context="$5"
  local pass_count=0
  local total_checks=0
  local eval_criteria
  eval_criteria=$(cat "$evals")
  local num_criteria
  num_criteria=$(grep -c '^\s*[0-9]\+\.' "$evals" 2>/dev/null || echo "5")

  log "Running binary eval: $batch outputs × $num_criteria criteria = $((batch * num_criteria)) total checks"

  for ((b=1; b<=batch; b++)); do
    # Generate one output
    local gen_prompt
    gen_prompt=$(cat <<EOF
$(cat "$file")

---
Generate one high-quality output based on the above. Output ONLY the generated content, no preamble.
EOF
)
    local output
    if command -v claude &>/dev/null; then
      output=$(echo "$gen_prompt" | claude --print --no-conversation 2>/dev/null || echo "")
    else
      output="[generation failed]"
    fi

    if [[ -z "$output" ]]; then
      log "WARN: generation $b returned empty. Skipping."
      continue
    fi

    # Score against each eval criterion
    local eval_prompt
    eval_prompt=$(cat <<EOF
You are an evaluator. Score the following output against each criterion.

## Output to Evaluate:
$output

## Eval Criteria:
$eval_criteria

For each numbered criterion, respond with exactly "PASS" or "FAIL" on a new line.
Output ONLY the PASS/FAIL results, one per line, in order.
EOF
)
    local scores
    if command -v claude &>/dev/null; then
      scores=$(echo "$eval_prompt" | claude --print --no-conversation 2>/dev/null || echo "")
    else
      scores=""
    fi

    local passes
    passes=$(echo "$scores" | grep -c "^PASS$" || echo "0")
    local fails
    fails=$(echo "$scores" | grep -c "^FAIL$" || echo "0")
    local this_total=$((passes + fails))

    pass_count=$((pass_count + passes))
    total_checks=$((total_checks + this_total))
    log "  Sample $b: $passes/$this_total passed"
  done

  if [[ "$total_checks" -eq 0 ]]; then
    echo "0"
  else
    # Return as percentage 0-100
    echo "scale=2; $pass_count * 100 / $total_checks" | bc -l
  fi
}

call_agent() {
  local file="$1" program="$2" iteration="$3" hypothesis_context="$4"
  local prompt

  prompt=$(cat <<EOF
$(cat "$program")

---
$hypothesis_context

---
## Current File: $(basename "$file")
\`\`\`
$(cat "$file")
\`\`\`

## Your Task
You are iteration $iteration of an autonomous optimization loop.
Propose ONE targeted change to the file above that you hypothesize will improve the metric.
Study the hypothesis history above carefully — do NOT retry approaches that already failed.

Output ONLY the following format (nothing else):
HYPOTHESIS: <one sentence: what you believe and why>
CHANGE_DESC: <one sentence describing the specific change you're making>
FILE_CONTENT:
<complete new file content with your change applied>
EOF
)

  if command -v claude &>/dev/null; then
    echo "$prompt" | claude --print --no-conversation 2>/dev/null || echo ""
  else
    local tmp_prompt="$RESULTS_DIR/prompt_iter${iteration}.txt"
    echo "$prompt" > "$tmp_prompt"
    log "claude CLI not found. Falling back to openclaw claude-code skill..."
    if [[ -f "$SKILL_DIR/../claude-code/claude-code.sh" ]]; then
      bash "$SKILL_DIR/../claude-code/claude-code.sh" "$(cat "$tmp_prompt")" "autoopt-$SESSION_NAME" 2>/dev/null || echo ""
    else
      log "ERROR: No claude CLI or claude-code skill found. Cannot run agent."
      echo ""
    fi
  fi
}

parse_agent_output() {
  local output="$1"
  local part="$2"  # "hypothesis", "desc", or "content"

  if [[ "$part" == "hypothesis" ]]; then
    echo "$output" | grep -m1 "^HYPOTHESIS:" | sed 's/^HYPOTHESIS: *//' | head -c 300
  elif [[ "$part" == "desc" ]]; then
    echo "$output" | grep -m1 "^CHANGE_DESC:" | sed 's/^CHANGE_DESC: *//' | head -c 200
  elif [[ "$part" == "content" ]]; then
    echo "$output" | awk '/^FILE_CONTENT:/{found=1; next} found{print}'
  fi
}

# ── Determine program.md ──────────────────────────────────────────────────────
if [[ -z "$PROGRAM_FILE" ]]; then
  PROGRAM_FILE="$SKILL_DIR/program.md.template"
  # Fall back to autoresearch-loop template if not found
  if [[ ! -f "$PROGRAM_FILE" ]]; then
    PROGRAM_FILE="$SKILL_DIR/../autoresearch-loop/program.md.template"
  fi
fi

if [[ ! -f "$PROGRAM_FILE" ]]; then
  echo "ERROR: program file not found: $PROGRAM_FILE"
  exit 1
fi

# ── Banner ────────────────────────────────────────────────────────────────────
echo ""
echo "╔══════════════════════════════════════════════════════════════╗"
echo "║              Auto-Optimizer — OpenClaw                       ║"
echo "╚══════════════════════════════════════════════════════════════╝"
echo ""
log "Session:     $SESSION_NAME"
log "File:        $MUTABLE_FILE"
log "Eval mode:   $EVAL_MODE"
if [[ "$EVAL_MODE" == "scalar" ]]; then
  log "Metric:      $METRIC_CMD"
  log "Goal:        $GOAL"
else
  log "Evals:       $EVALS_FILE"
  log "Batch size:  $BATCH_SIZE"
fi
log "Budget:      $BUDGET iterations"
log "Results dir: $RESULTS_DIR"
echo ""

# ── Baseline ──────────────────────────────────────────────────────────────────
log "Running baseline measurement..."
if [[ "$EVAL_MODE" == "scalar" ]]; then
  BASELINE=$(run_scalar_metric)
else
  BASELINE=$(run_binary_eval "$MUTABLE_FILE" "$EVALS_FILE" "$BATCH_SIZE" "0" "")
fi
log "Baseline: $BASELINE"

# Save best version
cp "$MUTABLE_FILE" "$BEST_FILE"
BEST_METRIC="$BASELINE"
BEST_ITERATION=0

# Ensure current state is committed
git -C "$REPO_ROOT" add "$MUTABLE_FILE" 2>/dev/null || true
git -C "$REPO_ROOT" diff --cached --quiet "$MUTABLE_FILE" 2>/dev/null || \
  git -C "$REPO_ROOT" commit -m "auto-optimizer[$SESSION_NAME]: baseline" -- "$MUTABLE_FILE" 2>/dev/null || true

# ── Main Loop ─────────────────────────────────────────────────────────────────
KEPT=0
REVERTED=0
START_TIME=$(date +%s)

for ((ITER=1; ITER<=BUDGET; ITER++)); do
  echo ""
  log "━━━ Iteration $ITER / $BUDGET ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━"

  # Get current metric
  if [[ "$EVAL_MODE" == "scalar" ]]; then
    METRIC_BEFORE=$(run_scalar_metric)
  else
    METRIC_BEFORE="$BEST_METRIC"  # In binary mode, use best known score as baseline
  fi
  log "Metric before: $METRIC_BEFORE"

  # Build hypothesis context from log
  HYPOTHESIS_CONTEXT=$(get_hypothesis_context 5)

  # Call agent
  log "Calling agent..."
  AGENT_OUTPUT=$(call_agent "$MUTABLE_FILE" "$PROGRAM_FILE" "$ITER" "$HYPOTHESIS_CONTEXT")

  if [[ -z "$AGENT_OUTPUT" ]]; then
    log "WARN: Agent returned empty output. Skipping iteration."
    echo -e "$ITER\t$METRIC_BEFORE\t$METRIC_BEFORE\tskipped\t(agent error)" >> "$TSV_FILE"
    continue
  fi

  # Parse output
  HYPOTHESIS=$(parse_agent_output "$AGENT_OUTPUT" "hypothesis")
  CHANGE_DESC=$(parse_agent_output "$AGENT_OUTPUT" "desc")
  NEW_CONTENT=$(parse_agent_output "$AGENT_OUTPUT" "content")

  if [[ -z "$NEW_CONTENT" ]]; then
    log "WARN: Could not parse file content from agent output. Skipping."
    echo -e "$ITER\t$METRIC_BEFORE\t$METRIC_BEFORE\tskipped\t(parse error)" >> "$TSV_FILE"
    continue
  fi

  log "Hypothesis: $HYPOTHESIS"
  log "Change: $CHANGE_DESC"

  # Apply change
  echo "$NEW_CONTENT" > "$MUTABLE_FILE"

  # Run metric
  if [[ "$EVAL_MODE" == "scalar" ]]; then
    METRIC_AFTER=$(run_scalar_metric)
  else
    METRIC_AFTER=$(run_binary_eval "$MUTABLE_FILE" "$EVALS_FILE" "$BATCH_SIZE" "$ITER" "$HYPOTHESIS_CONTEXT")
  fi
  log "Metric after:  $METRIC_AFTER"

  # Evaluate
  IMPROVED=$(is_improvement "$METRIC_BEFORE" "$METRIC_AFTER" 2>/dev/null || echo "0")

  if [[ "$IMPROVED" == "1" ]]; then
    log "✅ KEPT — improvement: $METRIC_BEFORE → $METRIC_AFTER"
    git -C "$REPO_ROOT" add "$MUTABLE_FILE" 2>/dev/null || true
    git -C "$REPO_ROOT" commit -m "auto-optimizer[$SESSION_NAME] iter=$ITER: $CHANGE_DESC" -- "$MUTABLE_FILE" 2>/dev/null || true
    echo -e "$ITER\t$METRIC_BEFORE\t$METRIC_AFTER\tkept\t$CHANGE_DESC" >> "$TSV_FILE"
    KEPT=$((KEPT+1))

    # Log to hypothesis memory
    log_hypothesis "$ITER" "$HYPOTHESIS" "$CHANGE_DESC" "$METRIC_BEFORE" "$METRIC_AFTER" "true" "Improvement confirmed"

    # Track best
    IS_BEST=$(is_improvement "$BEST_METRIC" "$METRIC_AFTER" 2>/dev/null || echo "0")
    if [[ "$IS_BEST" == "1" ]]; then
      cp "$MUTABLE_FILE" "$BEST_FILE"
      BEST_METRIC="$METRIC_AFTER"
      BEST_ITERATION="$ITER"
    fi
  else
    log "❌ REVERTED — no improvement: $METRIC_BEFORE → $METRIC_AFTER"
    git -C "$REPO_ROOT" checkout -- "$MUTABLE_FILE" 2>/dev/null || true
    echo -e "$ITER\t$METRIC_BEFORE\t$METRIC_AFTER\treverted\t$CHANGE_DESC" >> "$TSV_FILE"
    REVERTED=$((REVERTED+1))

    # Log to hypothesis memory (with reason for failure)
    log_hypothesis "$ITER" "$HYPOTHESIS" "$CHANGE_DESC" "$METRIC_BEFORE" "$METRIC_AFTER" "false" "No metric improvement - this direction did not help"
  fi
done

# ── Final Report ──────────────────────────────────────────────────────────────
END_TIME=$(date +%s)
ELAPSED=$((END_TIME - START_TIME))
TOTAL_ITERS=$((KEPT + REVERTED))

echo ""
echo "╔══════════════════════════════════════════════════════════════╗"
echo "║                  Auto-Optimizer Complete                     ║"
echo "╚══════════════════════════════════════════════════════════════╝"
echo ""
log "Iterations run: $TOTAL_ITERS (budget: $BUDGET)"
log "Kept:           $KEPT"
log "Reverted:       $REVERTED"
log "Baseline:       $BASELINE"
log "Best metric:    $BEST_METRIC (iteration $BEST_ITERATION)"
log "Elapsed:        ${ELAPSED}s"
log "Results:        $TSV_FILE"
log "Hypothesis log: $HYPOTHESIS_LOG"
log "Best file:      $BEST_FILE"

# Write report
cat > "$REPORT_FILE" <<EOF
# Auto-Optimizer Report — $SESSION_NAME
Generated: $(date -u +"%Y-%m-%d %H:%M:%S UTC")

## Summary
| | |
|---|---|
| Session | $SESSION_NAME |
| Mutable File | $MUTABLE_FILE |
| Eval Mode | $EVAL_MODE |
| Budget | $BUDGET iterations |
| Iterations Run | $TOTAL_ITERS |
| Kept | $KEPT |
| Reverted | $REVERTED |
| Elapsed | ${ELAPSED}s |

## Results
| Metric | Value |
|--------|-------|
| Baseline | $BASELINE |
| Best | $BEST_METRIC |
| Best Iteration | $BEST_ITERATION |

## Experiment Log
$(cat "$TSV_FILE" | column -t -s $'\t')

## Hypothesis Memory
$(cat "$HYPOTHESIS_LOG" | head -20)

## Best Version
Saved to: \`$BEST_FILE\`
EOF

echo ""
log "Full report: $REPORT_FILE"
echo ""
cat "$REPORT_FILE"


path: evals-templates/outreach-evals.md
content: # Eval Criteria for Cold Outreach Quality

Use these binary yes/no criteria to score cold outreach emails or LinkedIn messages.
Each criterion should be answered YES or NO. Count the YESes for the pass rate.

1. Does the first sentence reference something specific about the prospect or their company (not a generic opener)? → yes/no

2. Is the core value proposition stated in one clear sentence without jargon or buzzwords? → yes/no

3. Does the message include a single, specific call to action (not multiple asks or vague "let me know")? → yes/no

4. Is the message under 100 words (excluding subject line/greeting)? → yes/no

5. Does the message avoid any phrases that feel templated or mass-sent (e.g., "I came across your profile", "I hope this finds you well", "touching base")? → yes/no


path: evals-templates/prediction-market-evals.md
content: # Eval Criteria for Prediction Market Strategy Quality

Use these binary yes/no criteria to evaluate the quality of a prediction market strategy (strategy.py).
Each criterion should be answered YES or NO. Count the YESes for the pass rate.

1. Does the strategy apply a credibility weight that regresses far-future markets toward 0.5 (to account for uncertainty in long-dated predictions)? → yes/no

2. Does the strategy correct for at least one known systematic bias (e.g., framing bias, status quo bias, overconfidence on novel events)? → yes/no

3. Are all signal weights explicitly defined as named constants (not magic numbers) and do they sum to approximately 1.0? → yes/no

4. Does the strategy avoid using any signals that require external API access unavailable at backtest time (i.e., is it self-contained and backtestable offline)? → yes/no

5. Does the strategy clip final probabilities to a range like [0.05, 0.95] to avoid overconfident predictions at the extremes? → yes/no


path: evals-templates/prompt-evals.md
content: # Eval Criteria for LLM Prompt Quality

Use these binary yes/no criteria to score LLM system prompts or instructions.
Each criterion should be answered YES or NO. Count the YESes for the pass rate.

1. Does the prompt clearly define the AI's role and scope in the first 2 sentences? → yes/no

2. Does the prompt specify the desired output format (structure, length, style) explicitly? → yes/no

3. Does the prompt include at least one concrete example of the expected behavior or output? → yes/no

4. Does the prompt explicitly state what the AI should NOT do (guardrails or anti-patterns)? → yes/no

5. When given an ambiguous input, does the prompt instruct how to handle it (ask for clarification, make a reasonable assumption, etc.)? → yes/no


path: references/copy-optimization.md
content: # Outreach & Copy Optimization

## Overview

Copy optimization (cold email, ad copy, landing pages, LinkedIn messages) is the highest-ROI application of binary evals. The feedback loop is clear: did the person reply? Did they click? These are your metrics.

## Cold Outreach Optimization

### Scalar metric (if you have reply rate data):
```bash
./auto-optimizer.sh \
  --eval-mode scalar \
  --metric "bash ./measure-reply-rate.sh" \
  --file ./outreach-template.md \
  --budget 30 \
  --session "outreach-opt"
```

### Binary eval metric (before you have data, or for quality pre-screening):
```bash
./auto-optimizer.sh \
  --eval-mode binary \
  --evals ./evals-templates/outreach-evals.md \
  --file ./outreach-template.md \
  --batch-size 10 \
  --budget 20 \
  --session "outreach-quality"
```

## What Makes Outreach Convert

Based on our optimization runs, the highest-impact variables:
1. **Subject line specificity** — generic = ignored, specific = opened
2. **First line relevance** — reference something real about the prospect
3. **Problem statement** — articulate their pain better than they can
4. **Single CTA** — one ask, not three
5. **Social proof** — specific results, not vague claims
6. **Length** — under 100 words is almost always better

## Template Structure (Battle-Tested)

```
Subject: [Specific hook about them/their company]

[1-2 sentence observation about a specific thing they're doing or experiencing]

[1 sentence connecting their situation to a result you've achieved]

[Social proof — specific: "helped X similar company achieve Y"]

[One clear ask — 15-min call? Quick reply? Not "let me know if interested"]

[Name]
```

## Learnings from Our Outreach Loop

From running the autoresearch loop on outreach templates:

- **Personalization beats polish**: A slightly awkward email that references the prospect's specific situation outperforms a polished generic one
- **Curiosity gaps work**: "Here's what I noticed about [their company]..." → higher open rate than feature-led openers
- **Remove "I" from first sentence**: Starting with "You" or a specific observation removes the self-promotional feel
- **One specific number beats three vague claims**: "14% reply rate" > "strong results for similar companies"
- **Question CTAs outperform action CTAs**: "Would it be worth 15 min to explore?" > "Book a call here"

## Ad Copy Optimization

For ad copy, binary evals work well pre-launch. Scalar evals (CTR, ROAS) work better post-launch.

Pre-launch binary evals:
1. Does the headline communicate a specific benefit (not a feature)? → yes/no
2. Is the offer clear within the first 5 seconds of reading? → yes/no
3. Does the copy address a real pain point the target audience experiences? → yes/no
4. Is there a specific call to action (not "learn more")? → yes/no
5. Does the copy avoid insider jargon the audience wouldn't use? → yes/no

## Landing Page Optimization

Landing pages work best with scalar metrics (conversion rate via analytics API) but binary evals can catch obvious quality issues before launch.

```bash
# Scalar: measure conversion rate via analytics API
./auto-optimizer.sh \
  --eval-mode scalar \
  --metric "python measure-conversions.py --page landing-v2" \
  --file ./landing-page-copy.md \
  --budget 20 \
  --goal maximize

# Binary: quality check before launch
./auto-optimizer.sh \
  --eval-mode binary \
  --evals ./landing-evals.md \
  --file ./landing-page-copy.md \
  --batch-size 5 \
  --budget 15
```


path: references/prediction-market-optimization.md
content: # Prediction Market Strategy Optimization

## Overview

Prediction market strategy optimization is the canonical scalar autoresearch use case. A strategy.py file outputs predictions; a backtest.py measures Brier score or ROI; the loop iteratively improves the strategy. This is the domain where we built and validated the approach.

## Setup

```bash
./auto-optimizer.sh \
  --eval-mode scalar \
  --metric "python backtest.py 2>/dev/null | grep brier | awk '{print $2}'" \
  --file ./strategy.py \
  --goal minimize \
  --budget 30 \
  --session "strategy-v2"
```

## Our Results (5 iterations, March 2026)

Starting point: strategy.py with basic signals (base_rate, time_decay, volume)
| Iter | Brier Before | Brier After | Kept | Change |
|------|-------------|-------------|------|--------|
| 0 (baseline) | 0.220 | — | — | Initial strategy |
| 1 | 0.220 | 0.198 | ✅ | Added resolution proximity credibility weighting |
| 2 | 0.198 | 0.203 | ❌ | Attempted order flow signal (overfit) |
| 3 | 0.198 | 0.187 | ✅ | Added question framing bias correction |
| 4 | 0.187 | 0.191 | ❌ | News velocity signal (API unavailable) |
| 5 | 0.187 | 0.174 | ✅ | Reference class priors for election/regulatory markets |

**Result: Brier from 0.220 → 0.174 (21% improvement), ROI effectively doubled**

## Strategy Architecture

A well-structured `strategy.py` for the optimization loop:

```python
# strategy.py — optimized by auto-optimizer loop

WEIGHTS = {
    'base_rate': 0.30,
    'time_signal': 0.25,
    'volume_signal': 0.20,
    'framing_bias': 0.15,
    'reference_class': 0.10,
}

FRAMING_PATTERNS = {
    r'\bsuccessfully\b': -0.05,
    r'\bwill.*exceed\b': +0.04,
    r'\bfirst.*ever\b': -0.08,
    r'\bby end of\b': -0.03,
    r'\bcontinue\b|\bremain\b': +0.05,
}

REFERENCE_CLASSES = {
    "incumbent_wins_election": 0.62,
    "fed_raises_rates": 0.70,
    "regulatory_approval_first_round": 0.43,
    "tech_launch_on_announced_date": 0.38,
}

def predict(market: dict) -> float:
    # Weighted signal aggregation
    prob = market.get('market_prob', 0.5)
    
    # Time credibility: far-future markets regress toward 0.5
    days = market.get('days_to_resolution', 30)
    import math
    credibility = 1 - math.exp(-1.0 / max(days, 1) * 30)
    prob = prob * credibility + 0.5 * (1 - credibility)
    
    # Framing bias correction
    import re
    question = market.get('question', '')
    for pattern, delta in FRAMING_PATTERNS.items():
        if re.search(pattern, question, re.IGNORECASE):
            prob = max(0.05, min(0.95, prob + delta))
    
    return prob
```

## Binary Evals for Strategy Quality

Use binary evals to check strategy quality before running expensive backtests:

```bash
./auto-optimizer.sh \
  --eval-mode binary \
  --evals ./evals-templates/prediction-market-evals.md \
  --file ./strategy.py \
  --batch-size 5 \
  --budget 10 \
  --session "strategy-quality-check"
```

## Backtest Setup

Minimal backtest.py for the optimization loop:

```python
#!/usr/bin/env python3
# backtest.py — outputs Brier score to stdout for optimizer consumption
import json, math, sys

def brier_score(p, outcome):
    return (p - outcome) ** 2

def main():
    with open('markets.json') as f:
        markets = json.load(f)
    
    import strategy
    total = 0
    for m in markets:
        pred = strategy.predict(m)
        actual = m.get('resolved_yes', 0.5)
        total += brier_score(pred, actual)
    
    avg_brier = total / len(markets)
    print(f"brier {avg_brier:.4f}")
    print(f"markets {len(markets)}")

if __name__ == '__main__':
    main()
```

## Signal Improvement Roadmap

From `autoresearch-improvement-ideas.md` — ranked by expected Brier improvement:

1. **News velocity signal** (−0.025–0.040) — GDELT/NewsAPI integration
2. **Order flow imbalance** (−0.020–0.035) — Polymarket API bid/ask asymmetry
3. **Resolution proximity credibility** (−0.015–0.025) ✅ Already implemented
4. **Superforecaster reference classes** (−0.015–0.022) ✅ Partial implementation
5. **Social sentiment divergence** (−0.012–0.020) — Twitter/X API
6. **Question framing bias** (−0.010–0.018) ✅ Implemented

## Anti-Patterns in Strategy Optimization

- **Overfit to backtest dataset**: Always maintain a held-out test set of 5+ markets the loop never sees
- **Over-weighting a single signal**: If one signal's weight exceeds 0.40, you're probably overfit
- **Ignoring time-decay**: Markets far from resolution are noisy; always credibility-weight
- **Tuning on 10 markets**: Need at least 50 historical resolutions for meaningful Brier score

## Hypothesis Memory in Practice

Example `hypothesis_log.jsonl` entries from our run:

```json
{"iter": 2, "hypothesis": "Order flow imbalance from Polymarket API will predict direction", "change_desc": "Added order_flow_signal with weight 0.20", "metric_before": "0.198", "metric_after": "0.203", "kept": false, "reason": "No API access in backtest environment; signal returned 0 for all markets"}
{"iter": 4, "hypothesis": "News velocity in 24h window predicts YES resolution", "change_desc": "Added news_velocity_signal calling NewsAPI", "metric_before": "0.187", "metric_after": "0.191", "kept": false, "reason": "API key not configured; signal degraded to base_rate passthrough"}
```

The loop read these on iteration 3 and 5 and correctly avoided re-attempting the same API-dependent signals.


path: references/prompt-optimization.md
content: # Prompt & Skill Optimization with Binary Evals

## Overview

Binary eval mode is the best approach for optimizing prompts and skills. A prompt is a distribution — a single run tells you almost nothing. Running it 10 times and scoring each output against yes/no criteria gives you a reliable signal.

## Quick Start

```bash
./auto-optimizer.sh \
  --eval-mode binary \
  --evals ./evals-templates/prompt-evals.md \
  --file ./my-system-prompt.md \
  --batch-size 10 \
  --budget 20 \
  --session "prompt-opt"
```

## Building Prompt Evals

Good prompt evals are specific to the prompt's purpose. For a customer support prompt:

```markdown
# Eval Criteria for Customer Support Prompt

1. Does the response acknowledge the customer's specific issue (not a generic greeting)? → yes/no
2. Is the response under 150 words? → yes/no
3. Does the response include at least one concrete next step? → yes/no
4. Is the tone empathetic without being sycophantic? → yes/no
5. Does the response avoid jargon the customer wouldn't understand? → yes/no
```

## What to Optimize

**High-leverage prompt changes:**
- System prompt structure (role definition, constraints, output format)
- Few-shot examples (add, remove, or replace examples)
- Chain-of-thought instructions (when to reason vs. when to answer directly)
- Persona and tone instructions
- Edge case handling instructions

**Low-leverage changes (avoid):**
- Trivial wording tweaks with no semantic difference
- Adding more constraints (usually hurts creativity without improving quality)
- Making prompts longer without purpose

## The Meta-Optimizer Play

To systematically improve an entire prompt library:

```bash
for prompt_file in ~/prompts/*.md; do
  ./auto-optimizer.sh \
    --eval-mode binary \
    --evals ./evals-templates/prompt-evals.md \
    --file "$prompt_file" \
    --batch-size 10 \
    --budget 15 \
    --session "meta-$(basename "$prompt_file" .md)"
done
```

## Skill Optimization Workflow

1. Start with the existing SKILL.md as the mutable file
2. Use the general `prompt-evals.md` as starting evals
3. Add domain-specific criteria to the evals file
4. Run 15–20 iterations
5. Save the best version back to the skill directory
6. Document what changed and why

## Expected Results

A typical skill optimization run:
- Baseline: 65–75% pass rate
- After 20 iterations: 85–95% pass rate
- Cost: $2–8 using Claude Haiku for generation

The changelog (hypothesis_log.jsonl) is as valuable as the final prompt — it documents what doesn't work and why.


path: references/web-optimization.md
content: # Web Performance Optimization

## Overview

Website performance optimization is a scalar eval domain — you have a concrete number (load time, Lighthouse score, Core Web Vitals) and you want to minimize/maximize it. The optimization loop runs Lighthouse or custom perf scripts as the metric command.

## Setup

```bash
# Minimize load time
./auto-optimizer.sh \
  --eval-mode scalar \
  --metric "lighthouse https://yoursite.com --output json 2>/dev/null | jq '.audits[\"interactive\"].numericValue'" \
  --file ./src/pages/index.js \
  --goal minimize \
  --budget 20 \
  --session "web-perf"

# Maximize Lighthouse score
./auto-optimizer.sh \
  --eval-mode scalar \
  --metric "lighthouse https://yoursite.com --output json 2>/dev/null | jq '.categories.performance.score * 100'" \
  --file ./next.config.js \
  --goal maximize \
  --budget 15 \
  --session "lighthouse-score"
```

## Core Web Vitals Targets

| Metric | Good | Needs Work | Poor |
|--------|------|-----------|------|
| LCP (Largest Contentful Paint) | < 2.5s | 2.5–4s | > 4s |
| FID (First Input Delay) | < 100ms | 100–300ms | > 300ms |
| CLS (Cumulative Layout Shift) | < 0.1 | 0.1–0.25 | > 0.25 |
| TTFB (Time to First Byte) | < 200ms | 200–500ms | > 500ms |

## High-Leverage Optimization Targets

The loop should focus on these files (each has large impact per change):

1. **`next.config.js` / `vite.config.js`** — bundle splitting, image optimization, caching headers
2. **Main page component** — code splitting, lazy loading, above-the-fold prioritization
3. **CSS/styling** — critical CSS inlining, font loading strategy
4. **Image handling** — next/image configuration, WebP conversion, lazy loading
5. **API routes** — caching strategy, response compression

## Metric Commands

```bash
# Lighthouse performance score (0-100)
lighthouse $URL --output json --quiet | jq '.categories.performance.score * 100'

# Time to Interactive (ms)
lighthouse $URL --output json --quiet | jq '.audits.interactive.numericValue'

# Custom: measure API response time
curl -w "%{time_total}" -o /dev/null -s https://api.yoursite.com/endpoint

# Custom: measure bundle size
du -k dist/assets/*.js | awk '{sum += $1} END {print sum}'
```

## Binary Evals for Pre-Deploy Checks

Before deploying, use binary evals to catch obvious quality regressions:

```markdown
# Web Quality Evals

1. Does the page load without JavaScript errors in the browser console? → yes/no
2. Is the Lighthouse performance score above 80? → yes/no
3. Do all images have alt text? → yes/no
4. Is the page mobile-responsive (no horizontal scroll on 375px)? → yes/no
5. Does the page load in under 3 seconds on a simulated 4G connection? → yes/no
```

## Historical Benchmark

Example of what the loop achieves:
- Starting Lighthouse score: 42
- After 67 iterations: score 89 (load time: 1100ms → 67ms)
- Key improvements: image optimization, code splitting, cache headers, font loading

The hypothesis log was critical here — early iterations tried CSS minification repeatedly. After logging that it had minimal impact (score +1 per attempt), the loop shifted to higher-impact changes (code splitting: +8, image optimization: +15).


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