--- name: asciinema-analyzer-4bffad6cd252 description: Semantic analysis for Claude Code consumption. TRIGGERS - analyze cast, keyword extraction, density analysis, find patterns, sharpe, backtest, iteration, trading keywords, ML keywords. Use when extracting insights from recordings. allowed-tools: Read, Bash, Grep, Glob, AskUserQuestion --- # asciinema-analyzer Semantic analysis of converted .txt recordings for Claude Code consumption. Uses tiered analysis: ripgrep (primary, 50-200ms) -> YAKE (secondary, 1-5s) -> TF-IDF (optional). > **Platform**: macOS, Linux (requires ripgrep, optional YAKE) --- ## Analysis Tiers | Tier | Tool | Speed (4MB) | When to Use | | ---- | ------- | ----------- | ------------------------------ | | 1 | ripgrep | 50-200ms | Always start here (curated) | | 2 | YAKE | 1-5s | Auto-discover unexpected terms | | 3 | TF-IDF | 5-30s | Topic modeling (optional) | **Decision**: Start with Tier 1 (ripgrep + curated keywords). Only use Tier 2 (YAKE) when auto-discovery is explicitly requested. --- ## Requirements | Component | Required | Installation | Notes | | ----------- | -------- | ---------------------- | ----------------------- | | **ripgrep** | Yes | `brew install ripgrep` | Primary search tool | | **YAKE** | Optional | `uv run --with yake` | For auto-discovery tier | --- ## Workflow Phases (ALL MANDATORY) **IMPORTANT**: All phases are MANDATORY. Do NOT skip any phase. AskUserQuestion MUST be used at each decision point. ### Phase 0: Preflight Check **Purpose**: Verify input file exists and check for .txt (converted) format. ```bash /usr/bin/env bash << 'PREFLIGHT_EOF' INPUT_FILE="${1:-}" if [[ -z "$INPUT_FILE" ]]; then echo "NO_FILE_PROVIDED" elif [[ ! -f "$INPUT_FILE" ]]; then echo "FILE_NOT_FOUND: $INPUT_FILE" elif [[ "$INPUT_FILE" == *.cast ]]; then echo "WRONG_FORMAT: Convert to .txt first with /asciinema-tools:convert" elif [[ "$INPUT_FILE" == *.txt ]]; then SIZE=$(ls -lh "$INPUT_FILE" | awk '{print $5}') LINES=$(wc -l < "$INPUT_FILE" | tr -d ' ') echo "READY: $INPUT_FILE ($SIZE, $LINES lines)" else echo "UNKNOWN_FORMAT: Expected .txt file" fi PREFLIGHT_EOF ``` If no .txt file found, suggest running `/asciinema-tools:convert` first. --- ### Phase 1: File Selection (MANDATORY) **Purpose**: Discover .txt files and let user select which to analyze. #### Step 1.1: Discover .txt Files ```bash /usr/bin/env bash << 'DISCOVER_TXT_EOF' # Find .txt files that look like converted recordings for file in $(fd -e txt . --max-depth 3 2>/dev/null | head -10); do SIZE=$(ls -lh "$file" 2>/dev/null | awk '{print $5}') LINES=$(wc -l < "$file" 2>/dev/null | tr -d ' ') BASENAME=$(basename "$file") echo "FILE:$file|SIZE:$SIZE|LINES:$LINES|NAME:$BASENAME" done DISCOVER_TXT_EOF ``` #### Step 1.2: Present File Selection (MANDATORY AskUserQuestion) ``` Question: "Which file would you like to analyze?" Header: "File" Options: - Label: "{filename}.txt ({size})" Description: "{line_count} lines" - Label: "{filename2}.txt ({size2})" Description: "{line_count2} lines" - Label: "Enter path" Description: "Provide a custom path to a .txt file" - Label: "Convert first" Description: "Run /asciinema-tools:convert before analysis" ``` --- ### Phase 2: Analysis Type (MANDATORY) **Purpose**: Let user choose analysis depth. ``` Question: "What type of analysis do you need?" Header: "Type" Options: - Label: "Curated keywords (Recommended)" Description: "Fast search (50-200ms) with domain-specific keyword sets" - Label: "Auto-discover keywords" Description: "YAKE unsupervised extraction (1-5s) - finds unexpected patterns" - Label: "Full analysis" Description: "Both curated + auto-discovery for comprehensive results" - Label: "Density analysis" Description: "Find high-concentration sections (peak activity windows)" ``` --- ### Phase 3: Domain Selection (MANDATORY) **Purpose**: Let user select which keyword domains to search. ``` Question: "Which domain keywords to search?" Header: "Domain" multiSelect: true Options: - Label: "Trading/Quantitative" Description: "sharpe, sortino, calmar, backtest, drawdown, pnl, cagr, alpha, beta" - Label: "ML/AI" Description: "epoch, loss, accuracy, sota, training, model, validation, inference" - Label: "Development" Description: "iteration, refactor, fix, test, deploy, build, commit, merge" - Label: "Claude Code" Description: "Skill, TodoWrite, Read, Edit, Bash, Grep, iteration complete" ``` See [Domain Keywords Reference](./references/domain-keywords.md) for complete keyword lists. --- ### Phase 4: Execute Curated Analysis **Purpose**: Run Grep searches for selected domain keywords. #### Step 4.1: Trading Domain ```bash /usr/bin/env bash << 'TRADING_EOF' INPUT_FILE="${1:?}" echo "=== Trading/Quantitative Keywords ===" KEYWORDS="sharpe sortino calmar backtest drawdown pnl cagr alpha beta roi volatility" for kw in $KEYWORDS; do COUNT=$(rg -c -i "$kw" "$INPUT_FILE" 2>/dev/null || echo "0") if [[ "$COUNT" -gt 0 ]]; then echo " $kw: $COUNT" fi done TRADING_EOF ``` #### Step 4.2: ML/AI Domain ```bash /usr/bin/env bash << 'ML_EOF' INPUT_FILE="${1:?}" echo "=== ML/AI Keywords ===" KEYWORDS="epoch loss accuracy sota training model validation inference tensor gradient" for kw in $KEYWORDS; do COUNT=$(rg -c -i "$kw" "$INPUT_FILE" 2>/dev/null || echo "0") if [[ "$COUNT" -gt 0 ]]; then echo " $kw: $COUNT" fi done ML_EOF ``` #### Step 4.3: Development Domain ```bash /usr/bin/env bash << 'DEV_EOF' INPUT_FILE="${1:?}" echo "=== Development Keywords ===" KEYWORDS="iteration refactor fix test deploy build commit merge debug error" for kw in $KEYWORDS; do COUNT=$(rg -c -i "$kw" "$INPUT_FILE" 2>/dev/null || echo "0") if [[ "$COUNT" -gt 0 ]]; then echo " $kw: $COUNT" fi done DEV_EOF ``` #### Step 4.4: Claude Code Domain ```bash /usr/bin/env bash << 'CLAUDE_EOF' INPUT_FILE="${1:?}" echo "=== Claude Code Keywords ===" KEYWORDS="Skill TodoWrite Read Edit Bash Grep Write" for kw in $KEYWORDS; do COUNT=$(rg -c "$kw" "$INPUT_FILE" 2>/dev/null || echo "0") if [[ "$COUNT" -gt 0 ]]; then echo " $kw: $COUNT" fi done # Special patterns ITERATION=$(rg -c "iteration complete" "$INPUT_FILE" 2>/dev/null || echo "0") echo " 'iteration complete': $ITERATION" CLAUDE_EOF ``` --- ### Phase 5: YAKE Auto-Discovery (if selected) **Purpose**: Run unsupervised keyword extraction. ```bash /usr/bin/env bash << 'YAKE_EOF' INPUT_FILE="${1:?}" echo "=== Auto-discovered Keywords (YAKE) ===" uv run --with yake python3 -c " import yake kw = yake.KeywordExtractor( lan='en', n=2, # bi-grams dedupLim=0.9, # dedup threshold top=20 # top keywords ) with open('$INPUT_FILE') as f: text = f.read() keywords = kw.extract_keywords(text) for score, keyword in keywords: print(f'{score:.4f} {keyword}') " YAKE_EOF ``` --- ### Phase 6: Density Analysis (if selected) **Purpose**: Find sections with highest keyword concentration. ```bash /usr/bin/env bash << 'DENSITY_EOF' INPUT_FILE="${1:?}" KEYWORD="${2:-sharpe}" WINDOW_SIZE=100 # lines echo "=== Density Analysis: '$KEYWORD' ===" echo "Window size: $WINDOW_SIZE lines" echo "" TOTAL_LINES=$(wc -l < "$INPUT_FILE" | tr -d ' ') TOTAL_MATCHES=$(rg -c -i "$KEYWORD" "$INPUT_FILE" 2>/dev/null || echo "0") echo "Total matches: $TOTAL_MATCHES in $TOTAL_LINES lines" echo "Overall density: $(echo "scale=4; $TOTAL_MATCHES / $TOTAL_LINES * 1000" | bc) per 1000 lines" echo "" # Find peak windows echo "Top 5 densest windows:" awk -v ws="$WINDOW_SIZE" -v kw="$KEYWORD" ' BEGIN { IGNORECASE=1 } { lines[NR] = $0 if (tolower($0) ~ tolower(kw)) matches[NR] = 1 } END { for (start = 1; start <= NR - ws; start += ws/2) { count = 0 for (i = start; i < start + ws && i <= NR; i++) { if (matches[i]) count++ } if (count > 0) { printf "Lines %d-%d: %d matches (%.1f per 100)\n", start, start+ws-1, count, count*100/ws } } } ' "$INPUT_FILE" | sort -t: -k2 -rn | head -5 DENSITY_EOF ``` --- ### Phase 7: Report Format (MANDATORY) **Purpose**: Let user choose output format. ``` Question: "How should results be presented?" Header: "Output" Options: - Label: "Summary table (Recommended)" Description: "Keyword counts + top 5 peak sections" - Label: "Detailed report" Description: "Full analysis with timestamps and surrounding context" - Label: "JSON export" Description: "Machine-readable output for further processing" - Label: "Markdown report" Description: "Save formatted report to file" ``` --- ### Phase 8: Follow-up Actions (MANDATORY) **Purpose**: Guide user to next action. ``` Question: "Analysis complete. What's next?" Header: "Next" Options: - Label: "Jump to peak section" Description: "Read the highest-density section in the file" - Label: "Search for specific keyword" Description: "Grep for a custom term with context" - Label: "Cross-reference with .cast" Description: "Map findings back to original timestamps" - Label: "Done" Description: "Exit - no further action needed" ``` --- ## TodoWrite Task Template ``` 1. [Preflight] Check input file exists and is .txt format 2. [Preflight] Suggest /convert if .cast file provided 3. [Discovery] Find .txt files with line counts 4. [Selection] AskUserQuestion: file to analyze 5. [Type] AskUserQuestion: analysis type (curated/auto/full/density) 6. [Domain] AskUserQuestion: keyword domains (multi-select) 7. [Curated] Run Grep searches for selected domains 8. [Auto] Run YAKE if auto-discovery selected 9. [Density] Calculate density windows if requested 10. [Format] AskUserQuestion: report format 11. [Next] AskUserQuestion: follow-up actions ``` --- ## Post-Change Checklist After modifying this skill: 1. [ ] All bash blocks use heredoc wrapper 2. [ ] Curated keywords match references/domain-keywords.md 3. [ ] Analysis tiers match references/analysis-tiers.md 4. [ ] YAKE invocation uses `uv run --with yake` 5. [ ] All AskUserQuestion phases are present 6. [ ] TodoWrite template matches actual workflow --- ## Reference Documentation - [Domain Keywords Reference](./references/domain-keywords.md) - [Analysis Tiers Reference](./references/analysis-tiers.md) - [ripgrep Manual](https://github.com/BurntSushi/ripgrep) - [YAKE Documentation](https://github.com/LIAAD/yake) --- Source attribution: David-Li0406 Original project: https://github.com/David-Li0406/meta-skill-evloving/blob/ca3a335628981df10c36e00cb9850df2c247ab9a/skill-flow/data/skills-refined-36k/skillsmp/asciinema-analyzer/SKILL.md License: Apache-2.0 License information: https://www.apache.org/licenses/LICENSE-2.0 Original upstream instructions; downloaded from Mega AI Hub. ## Upstream license notice Apache License Version 2.0, January 2004 http://www.apache.org/licenses/ TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 1. Definitions. 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See the License for the specific language governing permissions and limitations under the License. # Mega AI Hub resource metadata Creator / publisher: David-Li0406 Original project: https://github.com/David-Li0406/meta-skill-evloving/blob/ca3a335628981df10c36e00cb9850df2c247ab9a/skill-flow/data/skills-refined-36k/skillsmp/asciinema-analyzer/SKILL.md Attribution basis: Source repository record Fields of work: Data & Analytics, Development & Engineering, Marketing Roles: Data Analyst, Marketing Analyst, ML / AI Engineer Specialties: API Tools: Ripgrep, Yake Country applicability: Country not specified Classification basis: Source catalog labels Instruction SHA-256: 416c19552a52347b13e43e2a392e5af6aa799802ab40bfdc2afe56a77854e962