{"uid":"cap_I1oCPvoUj_EIGFhQ5YjcY","slug":"openrefine-fingerprint-clustering-collision-review-plan-7b4e0d1a","name":"OpenRefine Fingerprint Clustering & Collision Review Plan","description":"Clusters a key column with native OpenRefine fingerprinting and returns a consolidation plan tied to actual row ids. The plan distinguishes the two cases that matter: rows that repeat an identical value and can simply be merged, and rows that collide on the key but disagree in other fields — those are flagged as unresolved rather than silently collapsed, because merging them would destroy information. Excluded rows and singletons are reported too, so the plan accounts for every input row.","url":"https://agent.halowerk.com/v1/oss-openrefine-table-collision-review-plan?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"table":{"type":"object","required":["columns","rows"],"properties":{"rows":{"type":"array","items":{"type":"object","required":["id","values"],"properties":{"id":{"type":"string","pattern":"^[A-Za-z][A-Za-z0-9_]{0,31}$"},"values":{"type":"array","items":{"anyOf":[{"type":"string","maxLength":4096},{"type":"null"}]},"maxItems":16,"minItems":1}},"additionalProperties":false},"maxItems":1000,"minItems":1},"columns":{"type":"array","items":{"type":"string","pattern":"^[A-Za-z][A-Za-z0-9_]{0,31}$"},"maxItems":16,"minItems":1}},"additionalProperties":false},"match_column":{"type":"string","pattern":"^[A-Za-z][A-Za-z0-9_]{0,31}$"}}},"responseSchema":null,"example":null,"exampleRequest":null,"tags":["x402"],"displayCostAmount":"0.0035","displayCostAsset":"USDC","priceDynamic":false,"priceHint":null,"priceStatus":"priced","priceSource":"probe","requiresHandshake":false,"reviewCount":0,"rating":{"score":"0.00","successRate":"0.00","reviews":0,"stars":null,"state":"unrated"},"availabilityStatus":"unknown","priceObserved":null,"sessionDeposit":null,"pricing":{"kind":"static","summary":"$0.0035/call","primary":{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.0035","per":"call","confidence":"exact"},"accepted":[{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.0035","per":"call","confidence":"exact"}]},"paymentMethods":[{"uid":"pm_ONO5ej5tvfv07vK1aHUpd","protocol":"x402","methodType":"crypto","chain":"base","mode":"charge","costAmount":"0.0035","costPer":"request","priority":0,"asset":"0x833589fCD6eDb6E08f4c7C32D4f71b54bdA02913","unit":"request","depositMicros":null,"planRef":null}],"brandName":null,"brandSlug":null,"brandBaseUrl":null,"brandDocsUrl":null,"whatItDoes":"Clusters a key column using OpenRefine fingerprinting and returns a row-level consolidation plan that distinguishes clean merges, unresolved collisions, excluded rows, and singletons.","exampleAgentPrompt":"Cluster the 'company_name' column in this table using OpenRefine fingerprinting and give me a consolidation plan — flag which rows can be merged cleanly and which have collisions because their other fields disagree.","exampleUseCases":[{"title":"CRM contact deduplication review","prompt":"I have a customer table with a 'email' column that probably has duplicates — some rows match on email but have different phone numbers or addresses. Run OpenRefine fingerprinting on the email column and give me a plan showing which rows can be merged and which are flagged as conflicts."},{"title":"Product catalog key collision detection","prompt":"My product catalog has a 'sku' column and I think some SKUs appear multiple times with different descriptions. Cluster on 'sku' and tell me which rows are safe to merge, which have conflicting values I need to review, and which are unique singletons."},{"title":"Research dataset entity consolidation","prompt":"I've got a dataset of academic institutions with a 'institution_name' column full of messy spellings. Use OpenRefine fingerprinting to cluster the names and show me a consolidation plan with row IDs so I know exactly which records to unify and which need manual review."}],"resultDescription":"A consolidation plan covering every input row, organized into: clean merge groups (rows with identical key values that can be collapsed), unresolved collisions (rows that share a fingerprinted key but disagree on other fields), excluded rows, and singletons — each group referencing actual row IDs from the input.","failureModes":["match_column not found in columns array — returns validation error","rows array is empty or exceeds 1000 row limit — schema rejection","columns and rows value arrays are misaligned in length — schema error","match_column pattern violation (invalid characters or too long) — 400 error","all rows are singletons — returns plan with no merge or collision groups, only singletons"],"whenToPreferThis":"Choose this endpoint when you need a safe, auditable deduplication plan for tabular data and want to avoid silently destroying information by merging conflicting records. It is especially valuable when rows sharing a key may legitimately differ in other fields — the unresolved collision flag prevents bad merges. Prefer it over generic deduplication tools when you need row-level traceability (actual IDs in the plan) and OpenRefine-compatible fingerprint clustering semantics, without needing to run a full OpenRefine instance.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-29T00:32:58.679Z","isFirstParty":false,"canonicalSlug":"openrefine-fingerprint-clustering-collision-review-plan-7b4e0d1a"}