summarize

name	summarize
description	AI-powered iterative deep-dive analysis of converted recordings. TRIGGERS - summarize recording, analyze session, what happened
allowed-tools	Bash, Grep, Read, AskUserQuestion, Task, Write
argument-hint	[file] [--topic topic] [--depth quick|medium|deep] [--output file]

/asciinema-tools:summarize

AI-powered iterative deep-dive analysis for large .txt recordings. Uses guided sampling and AskUserQuestion to progressively explore the content.

Self-Evolving Skill: This skill improves through use. If instructions are wrong, parameters drifted, or a workaround was needed — fix this file immediately, don't defer. Only update for real, reproducible issues.

Philosophy

Large recordings (1GB+) cannot be read entirely. This command uses:

1. Initial guidance - What are you looking for?
2. Strategic sampling - Head, middle, tail + keyword-targeted sections
3. Iterative refinement - AskUserQuestion to drill deeper into findings
4. Progressive synthesis - Build understanding through multiple passes

Arguments

Argument	Description
file	Path to .txt file (converted from .cast)
--topic	Initial focus area (e.g., "ML training", "errors")
--depth	Analysis depth: quick, medium, deep
--output	Save findings to markdown file

Workflow

Phase 1: Initial Guidance

AskUserQuestion:
  question: "What are you trying to understand from this recording?"
  header: "Focus"
  options:
    - label: "General overview"
      description: "What happened in this session? Key activities and outcomes"
    - label: "Key findings/decisions"
      description: "Important discoveries, conclusions, or decisions made"
    - label: "Errors and debugging"
      description: "What went wrong? How was it resolved?"
    - label: "Specific topic"
      description: "I'll specify what I'm looking for"

Phase 2: File Statistics

/usr/bin/env bash << 'STATS_EOF'
FILE="$1"

echo "=== File Statistics ==="
SIZE=$(ls -lh "$FILE" | awk '{print $5}')
LINES=$(wc -l < "$FILE")
echo "Size: $SIZE"
echo "Lines: $LINES"

echo ""
echo "=== Content Sampling ==="
echo "First 20 lines:"
head -20 "$FILE"

echo ""
echo "Last 20 lines:"
tail -20 "$FILE"

echo ""
echo "=== Keyword Density ==="
echo "Errors/failures:"
grep -c -i "error\|fail\|exception" "$FILE" || echo "0"
echo "Success indicators:"
grep -c -i "success\|complete\|done\|pass" "$FILE" || echo "0"
echo "Key decisions:"
grep -c -i "decision\|chose\|selected\|using"  ||  
STATS_EOF

Phase 3: Strategic Sampling

Based on file size, sample strategically:

For files < 100MB:

# Sample head, middle, tail (1000 lines each)
head -1000 "$FILE" > /tmp/sample_head.txt
tail -1000 "$FILE" > /tmp/sample_tail.txt
TOTAL=$(wc -l < "$FILE")
MIDDLE=$((TOTAL / 2))
sed -n "${MIDDLE},$((MIDDLE + 1000))p" "$FILE" > /tmp/sample_middle.txt

For files > 100MB:

# Keyword-targeted sampling
grep -B5 -A20 -i "$TOPIC_KEYWORDS" "$FILE" | head -5000 > /tmp/sample_targeted.txt

Phase 4: Initial Analysis

Read the samples and provide initial findings. Then ask:

AskUserQuestion:
  question: "Based on initial analysis, what would you like to explore deeper?"
  header: "Drill down"
  multiSelect: true
  options:
    - label: "Specific timeframe"
      description: "Jump to a particular section (e.g., 'around line 50000')"
    - label: "Follow keyword trail"
      description: "Search for specific patterns and expand context"
    - label: "Error investigation"
      description: "Deep dive into errors and their resolution"
    - label: "Success moments"
      description: "What worked? What were the wins?"
    - label: "Generate summary"
      description: "Synthesize findings into a report"

Phase 5: Iterative Deep-Dive

For each selected focus area:

1. Extract relevant sections using grep with context
2. Read and analyze the extracted content
3. Report findings to user
4. Ask for next action via AskUserQuestion

AskUserQuestion:
  question: "Found {N} relevant sections. What next?"
  header: "Continue"
  options:
    - label: "Show me the most significant"
      description: "Display top 3 most relevant excerpts"
    - label: "Search for related patterns"
      description: "Expand search to related keywords"
    - label: "Move on"
      description: "I have enough on this topic"

Phase 6: Synthesis

AskUserQuestion:
  question: "Ready to generate summary. What format?"
  header: "Output"
  options:
    - label: "Concise bullet points"
      description: "Key findings in 10-15 bullets"
    - label: "Detailed markdown report"
      description: "Full report with sections and evidence"
    - label: "Executive summary"
      description: "1-paragraph high-level summary"
    - label: "Save to file"
      description: "Write findings to markdown file"

Keyword Libraries

Trading/ML Domain

sharpe|drawdown|backtest|overfitting|regime|validation
model|training|loss|epoch|gradient|convergence
feature|indicator|signal|position|portfolio

Development Domain

error|exception|fail|bug|fix|debug
commit|push|merge|branch|deploy
test|assert|verify|validate|check

Claude Code Domain

tool|bash|read|write|edit|grep
task|agent|subagent|spawn
permission|approve|reject|block

Example Usage

# Interactive exploration
/asciinema-tools:summarize session.txt

# Focused on ML findings
/asciinema-tools:summarize session.txt --topic "ML training results"

# Quick overview
/asciinema-tools:summarize session.txt --depth quick

# Full analysis with report
/asciinema-tools:summarize session.txt --depth deep --output findings.md

Example Output

# Session Summary: alpha-forge-research_20251226

## Overview

- **Duration**: 4 days (Dec 26-30, 2025)
- **Size**: 12GB recording → 3.2GB text
- **Primary Focus**: ML robustness research

## Key Findings

### 1. Training-Evaluation Mismatch (CRITICAL)

- MSE loss optimizes magnitude, but Sharpe evaluates direction
- Result: 80% Sharpe collapse from 2024 to 2025

### 2. Fishr λ=0.1 Solution (BREAKTHROUGH)

- Gradient variance penalty solves V-REx binary threshold
- Feb'24 Sharpe: -6.14 → +6.14

### 3. Model Rankings

| Model  | Window | Sharpe |
| ------ | ------ | ------ |
| TFT    | 15mo   | 1.02   |
| BiLSTM | 12mo   | 0.50   |

## Evidence Locations

- Line 15234: "Fishr λ=0.1 SOLVES the V-REx binary threshold problem"
- Line 48102: Phase 4 results summary table

## Next Steps Identified

1. TFT 15mo + Fishr training
2. DSR/PBO statistical validation
3. Agent research synthesis

Troubleshooting

Issue	Cause	Solution
File too large	Recording exceeds memory limit	Use --depth quick for sampling only
No keywords found	Wrong domain or sparse content	Try different --topic focus area
Sampling timeout	Very large file	Increase terminal timeout or use grep
grep context error	Missing GNU grep	brew install grep (BSD grep limits)
Output file not saved	Permission denied	Check write permissions on --output

Related Commands

• /asciinema-tools:convert - Convert .cast to .txt first
• /asciinema-tools:analyze - Keyword-based analysis (faster, less deep)
• /asciinema-tools:finalize - Process orphaned recordings

Post-Execution Reflection

After this skill completes, reflect before closing the task:

0. Locate yourself. — Find this SKILL.md's canonical path before editing.
1. What failed? — Fix the instruction that caused it.
2. What worked better than expected? — Promote to recommended practice.
3. What drifted? — Fix any script, reference, or dependency that no longer matches reality.
4. Log it. — Evolution-log entry with trigger, fix, and evidence.

Do NOT defer. The next invocation inherits whatever you leave behind.