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Summarize huge articles (URL or local file) via a Codex CLI-driven chunk→reduce pipeline, keeping only the final short summary in context and saving it to summaries/*.md.
--- name: codex-summarize-pipeline description: Summarize huge articles (URL or local file) via a Codex CLI-driven chunk→reduce pipeline, keeping only the final short summary in context and saving it to summaries/*.md. --- # Codex Summarize Pipeline Use this skill to summarize long articles without loading the full text into the chat context. ## Guarantees / constraints - Use only local tools and the `codex` CLI (no direct API calls in your code). - Do not paste the full article into the conversation. - Keep only the final short summary (and the output file path) in the assistant response. - Always save the summary to a markdown file under `summaries/`. See `README.md` in this skill for detailed usage and recommended defaults for HTML-extracted blogs/docs. ## Default flow (URL) 1. Pick a slug for filenames (short, filesystem-safe). 2. Fetch + extract text: - `python3 <skill>/tools/fetch_article_text.py --url "<URL>" --output .codex-pipeline/inputs/<slug>.txt` 3. Summarize (default `map-reduce`): - `python3 <skill>/tools/codex_summarize_pipeline.py --input .codex-pipeline/inputs/<slug>.txt --final-max-words 120 > summaries/<slug>.summary.md` 4. Respond with: - The path `summaries/<slug>.summary.md` - The final summary content only (short) ## Default flow (local file) - `python3 <skill>/tools/codex_summarize_pipeline.py --input <path> --final-max-words 120 > summaries/<slug>.summary.md` ## Modes - `map-reduce`: best general-purpose coverage. - `extract-synthesize`: best when names/numbers/timeline matter. - `question-led`: best when user has a specific goal (use `--goal`). ## Troubleshooting - If extraction looks wrong, save the HTML and rerun: - `python3 <skill>/tools/fetch_article_text.py --input-html page.html --output .codex-pipeline/inputs/<slug>.txt` - If `codex` is configured to run remote models and you want local-only, run with `--oss` + `--local-provider` (requires local LM server).--- name: codex-summarize-pipeline description: Summarize huge articles (URL or local file) via a Codex CLI-driven chunk→reduce pipeline, keeping only the final short summary in context and saving it to summaries/*.md. --- # Codex Summarize Pipeline Use this skill to summarize long articles without loading the full text into the chat context. ## Guarantees / constraints - Use only local tools and the `codex` CLI (no direct API calls in your code). - Do not paste the full article into the conversation. - Keep only the final short summary (and the output file path) in the assistant response. - Always save the summary to a markdown file under `summaries/`. See `README.md` in this skill for detailed usage and recommended defaults for HTML-extracted blogs/docs. ## Default flow (URL) 1. Pick a slug for filenames (short, filesystem-safe). 2. Fetch + extract text: - `python3 <skill>/tools/fetch_article_text.py --url "<URL>" --output .codex-pipeline/inputs/<slug>.txt` 3. Summarize (default `map-reduce`): - `python3 <skill>/tools/codex_summarize_pipeline.py --input .codex-pipeline/inputs/<slug>.txt --final-max-words 120 > summaries/<slug>.summary.md` 4. Respond with: - The path `summaries/<slug>.summary.md` - The final summary content only (short) ## Default flow (local file) - `python3 <skill>/tools/codex_summarize_pipeline.py --input <path> --final-max-words 120 > summaries/<slug>.summary.md` ## Modes - `map-reduce`: best general-purpose coverage. - `extract-synthesize`: best when names/numbers/timeline matter. - `question-led`: best when user has a specific goal (use `--goal`). ## Troubleshooting - If extraction looks wrong, save the HTML and rerun: - `python3 <skill>/tools/fetch_article_text.py --input-html page.html --output .codex-pipeline/inputs/<slug>.txt` - If `codex` is configured to run remote models and you want local-only, run with `--oss` + `--local-provider` (requires local LM server).
Imported from the listed AgentSkill.sh category. Complete supplied package files and notices are retained. Includes source scripts; review them before use. Required external tools and credentials are not installed. No bundled code was executed during import. Individual agent task behavior has not been tested.