Borrowing it
Nothing to install: this file belongs to WomenDefiningAI/claudecode-writer. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/WomenDefiningAI/claudecode-writer/main/.claude/commands/extract-themes.mdgit clone --depth 1 https://github.com/WomenDefiningAI/claudecode-writerWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/commands/womendefiningai/claudecode-writer/extract-themes)<a href="https://agentmods.dev/commands/womendefiningai/claudecode-writer/extract-themes"><img src="https://agentmods.dev/badge/commands/womendefiningai/claudecode-writer/extract-themes.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00000 | $0.00489 |
| Opus 5 | $0.00000 | $0.00244 |
| Sonnet 5 | $0.00000 | $0.00098 |
| Haiku 4.5 | $0.00000 | $0.00049 |
Grade A, and why
extract-themes scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 7d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 48 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Extract Themes Command
Use this command to analyze raw notes and identify coherent themes, patterns, and content opportunities.
Usage
/extract-themes
What This Command Does
- Analyzes all files in the
/rawnotesfolder - Identifies recurring themes and patterns across notes
- Connects disparate ideas into coherent narratives
- Surfaces the user's unique perspective and voice
- Creates structured content briefs ready for development
Process
- Note Discovery: Scan all files in
/rawnotesfor content and context - Pattern Recognition: Identify recurring topics, themes, and interests
- Connection Mapping: Link related ideas across different notes and timeframes
- Voice Analysis: Extract the user's distinctive perspective and angle on topics
- Narrative Threading: Weave scattered thoughts into coherent storylines
- Gap Identification: Find missing pieces needed to complete ideas
- Opportunity Spotting: Identify which themes are ready for full development
Output
Provides a structured theme analysis with:
- Core Themes: 3-5 main patterns identified across all notes
- Unique Angles: Your distinctive perspective on each theme
- Content Opportunities: Which themes are ready for article development
- Supporting Evidence: Quotes and insights from your raw notes
- Missing Pieces: What additional research or development each theme needs
- Platform Potential: How each theme could work across LinkedIn, newsletter, social
- Next Actions: Clear steps to develop themes into full content
Integration Points
The output is designed to feed directly into:
- LinkedIn Repurposing: Use
linkedin-repurposeragent on developed themes - Research Command: Feed theme briefs into
/researchfor deeper development - Writing Workflow: Use themes as starting points for
/writecommand
File Management
After completing the analysis, automatically save results to:
- File Location:
research/theme-analysis-[YYYY-MM-DD].md - File Format: Markdown with clear theme sections and actionable next steps
- Cross-Reference: Include links back to original raw note files
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 7d ago First seen · 48 lines · 0 tokens per session scan A 02bb3705859d
extract-themes is a command published in the GitHub repository WomenDefiningAI/claudecode-writer (220 stars, last pushed 1y ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 489 tokens. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other commands, from other repositories
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.
implement
Execute the implementation plan by processing and executing all tasks defined in tasks.md.