# Detect Duplicates > Scans all feature requests for a WooCommerce Marketplace product and surfaces duplicates. Use when asked to find or clean up duplicate feature requests on WooCommerce.com. - Skill: `woocommerce/detect-duplicates` (Agent Skill) - Install (CLI): `npx skillmds@latest add woocommerce/detect-duplicates` - Raw SKILL.md: https://api.skillmd.com/api/skills/woocommerce/detect-duplicates/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Coding & Dev Tools - Author: woocommerce (https://skillmd.com/u/woocommerce) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/woocommerce/detect-duplicates --- # Detect Duplicate Feature Requests You are helping a WooCommerce Marketplace team member identify and act on duplicate feature requests for a specific product. **Read first:** - `.claude/skills/shared/RULES.md` - `.claude/skills/shared/DISPLAY.md` - `.claude/skills/shared/PHASE_LOOP.md` --- ## Step 1 — Resolve the product Follow `.claude/skills/shared/RESOLVE_PRODUCT.md`. --- ## Step 2 — Fetch all open feature requests > **Skip this step if invoked by the orchestrator.** The orchestrator passes > the FRs in `input_path` (a JSONL file already on disk). Use it directly as > the input to the detection script in Step 3 and **skip writing > `/tmp/frs-.jsonl`**. Call `wccom-feature-requests-list` with `per_page=100` and `status: "publish"`. Paginate. Collect `id`, `title`, `description`, `status`, `votes`, `date`, `url`. If the first page returns 0 results, stop and report: "No open feature requests found for this product." --- ## Step 2b — Choose comparison depth > **Skip this step if invoked by the orchestrator.** The orchestrator passes > `comparison_mode` (`fast` or `in-depth`). Ask: ``` How thorough should the duplicate scan be? [F] Fast — title-based matching only (script + false-positive pruning). Good for quick cleanup. Misses duplicates with different titles but similar descriptions. [D] In-depth — title + full description comparison. Claude reads every description to catch hidden duplicates. More thorough, takes longer. Reply F or D. ``` Store as **comparison_mode** (`fast` or `in-depth`). Default to `fast`. --- ## Step 2c — Write FRs to a temp file > **Skip this step if invoked by the orchestrator.** Use the orchestrator's > `input_path` directly. Use `Write` to create `$TMPDIR/frs-.jsonl`. One JSON object per line: `id`, `title`, `description`, `votes`, `date`, `url`. --- ## Step 3 — DETECTION STARTS HERE — Run the duplicate detection script (Orchestrator subagent: begin reading from this step. Use `` wherever the script command shows `$TMPDIR/frs-.jsonl`.) Choose the command based on **comparison_mode**: - **fast:** `python3 scripts/detect_duplicates.py $TMPDIR/frs-.jsonl --json` - **in-depth:** `.venv/bin/python3 scripts/detect_duplicates.py $TMPDIR/frs-.jsonl --semantic --rerank --json` The `--semantic --rerank` flags require `sentence-transformers` in `.venv/`. If import error, fall back to fast and note it. Parse the JSON output: ```json { "groups": [ { "confidence": "high" | "low", "max_similarity": 0.75, "primary": {"id": 123, "title": "...", "votes": 5, "date": "...", "url": "..."}, "duplicates": [{"id": 456, "title": "...", "votes": 2, "date": "...", "url": "..."}] } ], "borderline_pairs": [ {"similarity": 0.58, "fr_a": {...}, "fr_b": {...}} ] } ``` Normalise script confidence (`high`/`low` lowercase) to `High`/`Low` per `RULES.md`. Store `groups` as **script groups** and `borderline_pairs` for Step 3b-ii. If the script fails entirely, fall back to Step 3b and note it. --- ## Step 3b — Claude semantic review ### 3b-i — Review script groups for false positives For each group, read full titles and descriptions of every member. If a pair is clearly NOT the same ask (same keyword but different intent), remove the false-positive member. If a group reduces to one member, discard it. **Freed FRs go to a `re-evaluation pool`** — they may be true duplicates of a *different* FR that the script missed. **Do not discard a group for being support/spam/off-topic.** Duplicates are duplicates regardless of quality. Surface them here. Upgrade or downgrade confidence: - Upgrade to **High** if full text confirms a match beyond title similarity. - Downgrade to **Low** if the match is only superficial. ### 3b-ii — Semantic review of borderline pairs **Skip entirely if `comparison_mode = fast`.** Proceed to 3b-iii. If `in-depth`: Review two inputs: 1. `borderline_pairs` from the script — pairs near the threshold. 2. `re-evaluation pool` from 3b-i. For each borderline pair, read both `title` and `description` in full. Decide: same core ask? If yes, add as a new group. Be conservative. For pool FRs not covered by any pair, pay attention to vague short titles ("Booking", "Hi :)") — read full descriptions before comparing. Assign confidence (**High** / **Low**). Primary = most votes, then oldest if tied. Add as new groups. ### 3b-iii — Merge Combine script groups (after pruning) with new semantic groups. Sort: High first, then by primary vote count descending. --- ## Step 4 — Present the report Pagination — grouped phase, see `DISPLAY.md`. Decode HTML entities. Translate non-English excerpts per `RULES.md`. For each group: ``` ## Group [N]: [short description of the shared ask] Confidence: High / Low Reason: [one sentence — what makes these the same ask] ✅ Keep: ID [id] — "[title]" ([votes] votes · [status] · [date]) [url] "[description — first 2–3 sentences, trimmed if long]" ❌ Close: ID [id] — "[title]" ([votes] votes · [status] · [date]) [url] "[description — first 2–3 sentences, trimmed if long]" ``` End with: _X requests scanned · Y duplicate groups · Z requests could be closed._ ### Flagged record schema (for orchestrator output JSONL) One record **per duplicate-to-close**: ``` { "id": , "title": "", "url": "", "reason": "", "confidence": "High" | "Low", "excerpt": "", "votes": , "date": "", "group_id": , "primary": { "id": , "title": "", "url": "", "status": "", "votes": , "date": "", "excerpt": "" } } ``` --- ## Step 5 — Confirmation menu List every closeable duplicate grouped by group number. Within each group, sort High → Low. Assign sequential numbers across all groups (stable for pagination). ``` Which requests should I close? Group 1: [short group label] [1] ID [id] — "[title]" [High] [2] ID [id] — "[title]" [High] [3] ID [id] — "[title]" [Low] Group 2: [short group label] [4] ID [id] — "[title]" [High] ... [A] All of the above [H] High confidence only ← omit if no High items (see RULES.md) [N] None / skip Reply with numbers (e.g. "1 3"), A for all, H for High only, or N to skip. ``` Wait for the user's reply. Then group approved requests by their comment template (same primary + same primary status = same template). For each distinct template, show the preview **once** with the list of FRs: ``` 📝 Comment preview — applies to [N] request(s): • ID [id] — "[title]" • ID [id] — "[title]" --- [full comment text, with [primary_url] filled in] --- ``` After all previews: **"Post these comments and close the requests? (Y / N)"** If N, abort. Then for each approved duplicate, look up its group's primary and process in this order: 1. **Post closing comment** via `wccom-feature-requests-comment`. Template depends on **primary status**: **Primary `open` with more votes:** ``` Hi there, Thank you for your suggestion! We noticed that there is a similar request about this, which currently has more votes: [primary_url]. To better prioritize requests, we aim to keep 1 request open per feature. That's why I will close this one. I encourage you to upvote the above request instead. ``` **Primary `open` with equal votes (kept because older):** ``` Hi there, Thank you for your suggestion! We noticed that there is a similar request about this that has been open longer: [primary_url]. To better prioritize requests, we aim to keep 1 request open per feature. That's why I will close this one. I encourage you to upvote the above request instead. ``` **Primary `planned` or `in-progress`:** ``` Hi there, Thank you for your suggestion! We noticed that this feature is already planned and being tracked here: [primary_url]. That's why I will close this duplicate. You can follow progress on that request instead. ``` **Primary `completed`:** ``` Hi there, Thank you for your suggestion! Great news — this feature has already been implemented! You can find more details here: [primary_url]. That's why I will close this request. ``` 2. **Update status** via `wccom-feature-requests-update-status` → `"closed"`. 3. **Write failure handling** — on failure, do not record as actioned; report in summary; continue. If comment posted but status update failed, note the half-completed state. --- ## Step 6 — Extract insights for each primary After closing approved duplicates, for each group with at least one closure, compare full descriptions of every closed request against the primary's. Look for: - Additional use cases or scenarios not covered by the primary - Different user workflows or perspectives - Concrete details, examples, or edge cases - Explicit requirements/constraints mentioned only in closed requests If unique insights are found, draft a comment for the **primary**. Plain text only (see `RULES.md`). Example: ``` Hi there, We recently consolidated a few related requests into this one. In doing so, we noticed some additional use cases and perspectives worth capturing here: - [insight 1] - [insight 2] ... We've noted these for the team when reviewing this request. ``` Preview: ``` 📝 Draft comment for primary — ID [primary_id] — "[primary_title]" [primary_url] --- [full draft comment] --- Post this comment to the primary request? (Y / N) ``` If Y: call `wccom-feature-requests-comment` on the primary's ID. **Do not update the primary's status.** If no unique insights across any closed duplicate: _"No additional insights found — primary requests are already comprehensive."_ Skip the step. --- ## Step 7 — Confirmation summary ``` Done. ❌ Closed: ID [id] — "[title]" [closed_url] ✅ Primary: ID [id] — "[title]" ([status]) [primary_url] ⚠️ Failed: ID [id] — "[title]" — [error] ``` Then follow `PHASE_LOOP.md`. Additionally, the standalone re-scan prompt accepts a group-number reference: ``` Need more context on a group? Type the group number (e.g. "Group 3"). ``` If the user specifies a group number: show full `title`, `description`, `votes`, `date`, `url` for every request in that group (primary + all closeable duplicates), then re-ask the loop question.