# Elimination Research > This skill should be used for elimination-style research where the user wants to choose from a shortlist of products, tools, services, vendors, or other options using explicit criteria, numeric evidence, tournament-style comparison, source/domain classification, image-supported consumer reports, raw data tables, and ownership-cost estimates for replaceable parts. Use this skill whenever the user asks to compare options, buy something, shortlist candidates, rank alternatives, generate a "don't make me think" report, or produce a full audit report with raw numeric data. - Skill: `glebis/elimination-research` (Agent Skill, multi-file: 14 files) - Install (CLI): `npx skillmds@latest add glebis/elimination-research` - Raw SKILL.md: https://api.skillmd.com/api/skills/glebis/elimination-research/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Security - Author: glebis (https://skillmd.com/u/glebis) - Updated: 2026-09-09 - Page: https://skillmd.com/skills/glebis/elimination-research --- # Elimination Research ## Purpose Generate a reproducible elimination-research package: a shortlist dataset, numeric scoring model, quick consumer report, full audit report, raw data JSON, source/domain audit, purchase/info links, contextual images, and ownership-cost estimates. Use this skill to turn fuzzy "which one should I choose?" requests into a clean decision workflow with explicit criteria and inspectable data. ## Workflow Follow this sequence for new comparisons: 1. Read `references/workflow.md` for the full operating procedure. 2. Ask the intake questions before researching. Prefer `cenno` popup questions when available. Use closed choices and include a free-text comment field. 3. Gather candidate, source, price, spec, replacement-part, image, and evidence data. 4. Save all collected data into a dataset JSON matching `references/dataset-schema.md`. 5. Run `scripts/generate_elimination_report.py` to generate reports. 6. Verify the quick report and full report in a browser. 7. Preserve raw data and numeric tables; do not hide or discard evidence just because the quick report is simplified. ## Intake Questions Ask these at the start of a new comparison, not inside the final report: - What matters most: overall quality, lowest price, sensitive-skin/user-fit, low maintenance, or travel/portability? - What is the hard limit: budget ceiling, must-have features, excluded brands, or purchase country? - How much evidence is needed: quick consumer view, full audit report, or both? - Which source types are allowed: manufacturer, retailer, price aggregator, expert review, forum, or all with flags? Always include a comment field for constraints that do not fit the closed choices. ## Output Contract Produce these files in the chosen output directory: - `quick_report.html` — consumer-facing "don't make me think" report with cards/table switch, images in context, rounded prices, links, and visible ownership summaries. - `report.html` — full audit report with task, criteria, scoring, raw numeric data, source/domain tables, tournament, and embedded JSON. - `report.md` — markdown version of the full audit report. - `final_report.json` — normalized report payload. - `raw_research_data.json` — collected dataset before rendering. - `image_search_results.json` — cached Google image-search output when image refresh is used. The quick report should keep numeric detail behind expandable evidence links, but the full report must expose all numeric data as tables. ## Generator Run the bundled generator from the skill directory: ```bash python3 scripts/generate_elimination_report.py \ --dataset assets/examples/consumer_goods_dataset.example.json \ --output-dir /tmp/elimination-report \ --max-price-eur 200 ``` Common options: ```bash --dataset PATH Structured shortlist dataset JSON --output-dir PATH Output directory --max-price-eur NUMBER Purchase-price ceiling override --price-limit-basis FIELD Usually device_price_eur or three_year_cost_eur --question TEXT Override report task question --market TEXT Purchase market/country --currency TEXT Currency label --domain-registry PATH Optional domain registry JSON --refresh-images Refresh Google Custom Search image data --image-results NUMBER Image results per candidate when refreshing ``` For Google Images, load keys only from environment variables or SOPS-encrypted dotenv files. Never commit plaintext keys. The image helper checks `GOOGLE_CUSTOM_SEARCH_JSON_API_KEY`, `GOOGLE_CUSTOM_SEARCH_API_KEY`, `GOOGLE_CUSTOM_SEARCH_CX`, and `GOOGLE_IMAGE_SEARCH_ENV_FILE`. ## Data Rules Read `references/dataset-schema.md` before creating or editing the dataset. Key requirements: - Use stable candidate IDs. - Keep every numeric observation as a number, not prose. - Store prices in explicit currency fields such as `device_price_eur`. - For replaceable parts, include rough `replacement_unit_price_eur`, `replacement_quantity_3y`, `replacement_interval_months`, and `replacement_part_name`. - Include `item_links` or source references so each item has 1-3 purchase/info links. - Classify source domains by role and trust tier. Manufacturer/spec, retailer, price aggregator, expert review, forum, and affiliate sources should remain distinct. - Store caveats explicitly. Do not silently remove weak assumptions. ## Report Design Rules For consumer reports: - Let product images illustrate the options in context; do not create a standalone image-source section. - Keep image blocks on a light neutral background. - Hide image host/score/dimensions from the consumer report; keep them in JSON. - Round visible prices in the quick report. - Avoid eyebrow labels. - Provide a card/table switch where cards and table are mutually exclusive views. - Show ownership cost directly on each option card/table row when replaceable parts exist. - Keep source links as short action chips: price, official, review, parts, or head price. For audit reports: - Start with the task and criteria so the report is understandable without conversation context. - Show all numeric data as tables. - Include the full candidate dataset, domain/source audit, score formula, tournament rows, sensitivity rankings, and caveats. ## Verification Before handing off: - Run the generator on the dataset. - Validate JSON with `python3 -m json.tool`. - Open `quick_report.html` and verify the card/table switch replaces the options view rather than stacking table below cards. - Check mobile width for text overflow, low contrast, and touch targets under 44px. - Confirm `report.html` includes raw numeric columns for device price, replacement allowance, part unit price, interval, quantity, and three-year cost. - Commit and push changes when editing the skills repo or generated report project.