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Transaction Safety (weight: 0.40)\n- Appropriateness of transaction boundaries\n- ACID property guarantees\n- Deadlock avoidance design\n\n### 2. Data Consistency (weight: 0.35)\n- Acceptable range for eventual consistency\n- Conflict resolution strategies\n- Referential integrity constraints\n\n### 3. Schema Design Quality (weight: 0.25)\n- Appropriateness of normalization level\n- Index design\n- Migration safety\n\n## Execution\n\n### Step 1: Collect Input File Paths\n\nGlob for all available design documents:\n- `reports\u002F03_design\u002Fdata-layer-design.md`\n- `reports\u002F03_design\u002Ftarget-architecture.md`\n- `reports\u002F03_design\u002Ftransformation-plan.md`\n- `reports\u002F03_design\u002Fapi-specifications\u002F**\u002F*.md` (if exists)\n- `reports\u002F01_analysis\u002Fdata-model-analysis.md` (if exists)\n\nRecord the full list of found file paths — these will be passed to sub-agents.\n\n### Step 2: Spawn Three Parallel Dimension Reviewers\n\nIn a **single message**, issue all three Agent() calls simultaneously so they run in parallel:\n\n**Task A — Transaction Safety (DIN-1xx)**\n```\nAgent(\n  subagent_type: \"general-purpose\",\n  model: \"sonnet\",\n  description: \"Transaction safety dimension review\",\n  prompt: \"\"\"\nYou are a database architect reviewing designs for TRANSACTION SAFETY.\n\nRead all of the following files using the Read tool:\n\u003CFILE_LIST>\n[Insert the full list of file paths found in Step 1, one per line]\n\u003C\u002FFILE_LIST>\n\nEvaluate ONLY the Transaction Safety dimension:\n- Are transaction boundaries appropriate (not too broad causing lock contention, not too narrow losing atomicity)?\n- Are ACID properties (Atomicity, Consistency, Isolation, Durability) guaranteed for critical operations?\n- Is deadlock avoidance addressed (consistent lock ordering, timeout strategies, retry logic)?\n\nScore 1-5: 5=Exemplary, 4=Good, 3=Acceptable, 2=Concerning, 1=Critical\n\nReturn ONLY this JSON (no markdown fences, no explanation):\n{\n  \"name\": \"Transaction Safety\",\n  \"weight\": 0.40,\n  \"score\": \u003Cinteger 1-5>,\n  \"findings\": [\n    {\n      \"id\": \"DIN-1\u003CNN>\",\n      \"severity\": \"critical|major|minor|info\",\n      \"location\": \"\u003Cfile:section>\",\n      \"title\": \"\u003Cfinding title>\",\n      \"description\": \"\u003Ctransaction issue and its data integrity impact>\",\n      \"recommendation\": \"\u003Cspecific remediation>\"\n    }\n  ]\n}\n\"\"\"\n)\n```\n\n**Task B — Data Consistency (DIN-2xx)**\n```\nAgent(\n  subagent_type: \"general-purpose\",\n  model: \"sonnet\",\n  description: \"Data consistency dimension review\",\n  prompt: \"\"\"\nYou are a database architect reviewing designs for DATA CONSISTENCY.\n\nRead all of the following files using the Read tool:\n\u003CFILE_LIST>\n[Insert the full list of file paths found in Step 1, one per line]\n\u003C\u002FFILE_LIST>\n\nEvaluate ONLY the Data Consistency dimension:\n- Is the acceptable range for eventual consistency defined and aligned with business requirements?\n- Are conflict resolution strategies defined for concurrent updates (last-write-wins, merge, versioning)?\n- Are referential integrity constraints enforced (foreign keys, cascade rules, orphan prevention)?\n\nScore 1-5: 5=Exemplary, 4=Good, 3=Acceptable, 2=Concerning, 1=Critical\n\nReturn ONLY this JSON (no markdown fences, no explanation):\n{\n  \"name\": \"Data Consistency\",\n  \"weight\": 0.35,\n  \"score\": \u003Cinteger 1-5>,\n  \"findings\": [\n    {\n      \"id\": \"DIN-2\u003CNN>\",\n      \"severity\": \"critical|major|minor|info\",\n      \"location\": \"\u003Cfile:section>\",\n      \"title\": \"\u003Cfinding title>\",\n      \"description\": \"\u003Cconsistency issue and its business impact>\",\n      \"recommendation\": \"\u003Cspecific remediation>\"\n    }\n  ]\n}\n\"\"\"\n)\n```\n\n**Task C — Schema Design Quality (DIN-3xx)**\n```\nAgent(\n  subagent_type: \"general-purpose\",\n  model: \"sonnet\",\n  description: \"Schema design quality dimension review\",\n  prompt: \"\"\"\nYou are a database architect reviewing designs for SCHEMA DESIGN QUALITY.\n\nRead all of the following files using the Read tool:\n\u003CFILE_LIST>\n[Insert the full list of file paths found in Step 1, one per line]\n\u003C\u002FFILE_LIST>\n\nEvaluate ONLY the Schema Design Quality dimension:\n- Is the normalization level appropriate (avoid over-normalization causing excessive joins; avoid under-normalization causing update anomalies)?\n- Is index design sound (covering indexes for common queries, no redundant indexes, index selectivity)?\n- Is migration safety addressed (backward-compatible migrations, no blocking ALTER TABLE on large tables)?\n- Read the design's \"Read Model, CQRS and Event Sourcing Decisions\" table: an aggregate with a contended write path or a reporting consumer and `none` \u002F `no` there is a finding, and an aggregate absent from the table is *undecided* — a finding too, not \"not needed\"\n\nScore 1-5: 5=Exemplary, 4=Good, 3=Acceptable, 2=Concerning, 1=Critical\n\nReturn ONLY this JSON (no markdown fences, no explanation):\n{\n  \"name\": \"Schema Design Quality\",\n  \"weight\": 0.25,\n  \"score\": \u003Cinteger 1-5>,\n  \"findings\": [\n    {\n      \"id\": \"DIN-3\u003CNN>\",\n      \"severity\": \"critical|major|minor|info\",\n      \"location\": \"\u003Cfile:section>\",\n      \"title\": \"\u003Cfinding title>\",\n      \"description\": \"\u003Cschema issue and its operational impact>\",\n      \"recommendation\": \"\u003Cspecific remediation>\"\n    }\n  ]\n}\n\"\"\"\n)\n```\n\n### Step 3: Merge and Write Output\n\nAfter all three Tasks complete, compute the weighted score and write output:\n\n```\nweighted_score = round(0.40 × scoreA + 0.35 × scoreB + 0.25 × scoreC, 2)\n```\n\nWrite `reports\u002Freview\u002Findividual\u002Freview-data-integrity.json`:\n```json\n{\n  \"perspective\": \"data-integrity\",\n  \"reviewer\": \"review-data-integrity\",\n  \"timestamp\": \"\u003CISO-8601 now>\",\n  \"dimensions\": [\u003CTask A result>, \u003CTask B result>, \u003CTask C result>],\n  \"weighted_score\": \u003Ccomputed>,\n  \"summary\": \"\u003C2-3 sentences synthesizing data integrity health and key risks>\"\n}\n```\n\n## Output Format\n\nFinding ID prefix: **DIN-**\n- DIN-1xx: Transaction Safety\n- DIN-2xx: Data Consistency\n- DIN-3xx: Schema Design Quality\n","# Data Integrity Review\n\n## Review Dimensions\n\n### 1. Transaction Safety (weight: 0.40)\n- Appropriateness of transaction boundaries\n- ACID property guarantees\n- Deadlock avoidance design\n\n### 2. Data Consistency (weight: 0.35)\n- Acceptable range for eventual consistency\n- Conflict resolution strategies\n- Referential integrity constraints\n\n### 3. Schema Design Quality (weight: 0.25)\n- Appropriateness of normalization level\n- Index design\n- Migration safety\n\n## Execution\n\n### Step 1: Collect Input File Paths\n\nGlob for all available design documents:\n- `reports\u002F03_design\u002Fdata-layer-design.md`\n- `reports\u002F03_design\u002Ftarget-architecture.md`\n- `reports\u002F03_design\u002Ftransformation-plan.md`\n- `reports\u002F03_design\u002Fapi-specifications\u002F**\u002F*.md` (if exists)\n- `reports\u002F01_analysis\u002Fdata-model-analysis.md` (if exists)\n\nRecord the full list of found file paths — these will be passed to sub-agents.\n\n### Step 2: Spawn Three Parallel Dimension Reviewers\n\nIn a **single message**, issue all three Agent() calls simultaneously so they run in parallel:\n\n**Task A — Transaction Safety (DIN-1xx)**\n```\nAgent(\n  subagent_type: \"general-purpose\",\n  model: \"sonnet\",\n  description: \"Transaction safety dimension review\",\n  prompt: \"\"\"\nYou are a database architect reviewing designs for TRANSACTION SAFETY.\n\nRead all of the following files using the Read tool:\n\u003CFILE_LIST>\n[Insert the full list of file paths found in Step 1, one per line]\n\u003C\u002FFILE_LIST>\n\nEvaluate ONLY the Transaction Safety dimension:\n- Are transaction boundaries appropriate (not too broad causing lock contention, not too narrow losing atomicity)?\n- Are ACID properties (Atomicity, Consistency, Isolation, Durability) guaranteed for critical operations?\n- Is deadlock avoidance addressed (consistent lock ordering, timeout strategies, retry logic)?\n\nScore 1-5: 5=Exemplary, 4=Good, 3=Acceptable, 2=Concerning, 1=Critical\n\nReturn ONLY this JSON (no markdown fences, no explanation):\n{\n  \"name\": \"Transaction Safety\",\n  \"weight\": 0.40,\n  \"score\": \u003Cinteger 1-5>,\n  \"findings\": [\n    {\n      \"id\": \"DIN-1\u003CNN>\",\n      \"severity\": \"critical|major|minor|info\",\n      \"location\": \"\u003Cfile:section>\",\n      \"title\": \"\u003Cfinding title>\",\n      \"description\": \"\u003Ctransaction issue and its data integrity impact>\",\n      \"recommendation\": \"\u003Cspecific remediation>\"\n    }\n  ]\n}\n\"\"\"\n)\n```\n\n**Task B — Data Consistency (DIN-2xx)**\n```\nAgent(\n  subagent_type: \"general-purpose\",\n  model: \"sonnet\",\n  description: \"Data consistency dimension review\",\n  prompt: \"\"\"\nYou are a database architect reviewing designs for DATA CONSISTENCY.\n\nRead all of the following files using the Read tool:\n\u003CFILE_LIST>\n[Insert the full list of file paths found in Step 1, one per line]\n\u003C\u002FFILE_LIST>\n\nEvaluate ONLY the Data Consistency dimension:\n- Is the acceptable range for eventual consistency defined and aligned with business requirements?\n- Are conflict resolution strategies defined for concurrent updates (last-write-wins, merge, versioning)?\n- Are referential integrity constraints enforced (foreign keys, cascade rules, orphan prevention)?\n\nScore 1-5: 5=Exemplary, 4=Good, 3=Acceptable, 2=Concerning, 1=Critical\n\nReturn ONLY this JSON (no markdown fences, no explanation):\n{\n  \"name\": \"Data Consistency\",\n  \"weight\": 0.35,\n  \"score\": \u003Cinteger 1-5>,\n  \"findings\": [\n    {\n      \"id\": \"DIN-2\u003CNN>\",\n      \"severity\": \"critical|major|minor|info\",\n      \"location\": \"\u003Cfile:section>\",\n      \"title\": \"\u003Cfinding title>\",\n      \"description\": \"\u003Cconsistency issue and its business impact>\",\n      \"recommendation\": \"\u003Cspecific remediation>\"\n    }\n  ]\n}\n\"\"\"\n)\n```\n\n**Task C — Schema Design Quality (DIN-3xx)**\n```\nAgent(\n  subagent_type: \"general-purpose\",\n  model: \"sonnet\",\n  description: \"Schema design quality dimension review\",\n  prompt: \"\"\"\nYou are a database architect reviewing designs for SCHEMA DESIGN QUALITY.\n\nRead all of the following files using the Read tool:\n\u003CFILE_LIST>\n[Insert the full list of file paths found in Step 1, one per line]\n\u003C\u002FFILE_LIST>\n\nEvaluate ONLY the Schema Design Quality dimension:\n- Is the normalization level appropriate (avoid over-normalization causing excessive joins; avoid under-normalization causing update anomalies)?\n- Is index design sound (covering indexes for common queries, no redundant indexes, index selectivity)?\n- Is migration safety addressed (backward-compatible migrations, no blocking ALTER TABLE on large tables)?\n- Read the design's \"Read Model, CQRS and Event Sourcing Decisions\" table: an aggregate with a contended write path or a reporting consumer and `none` \u002F `no` there is a finding, and an aggregate absent from the table is *undecided* — a finding too, not \"not needed\"\n\nScore 1-5: 5=Exemplary, 4=Good, 3=Acceptable, 2=Concerning, 1=Critical\n\nReturn ONLY this JSON (no markdown fences, no explanation):\n{\n  \"name\": \"Schema Design Quality\",\n  \"weight\": 0.25,\n  \"score\": \u003Cinteger 1-5>,\n  \"findings\": [\n    {\n      \"id\": \"DIN-3\u003CNN>\",\n      \"severity\": \"critical|major|minor|info\",\n      \"location\": \"\u003Cfile:section>\",\n      \"title\": \"\u003Cfinding title>\",\n      \"description\": \"\u003Cschema issue and its operational impact>\",\n      \"recommendation\": \"\u003Cspecific remediation>\"\n    }\n  ]\n}\n\"\"\"\n)\n```\n\n### Step 3: Merge and Write Output\n\nAfter all three Tasks complete, compute the weighted score and write output:\n\n```\nweighted_score = round(0.40 × scoreA + 0.35 × scoreB + 0.25 × scoreC, 2)\n```\n\nWrite `reports\u002Freview\u002Findividual\u002Freview-data-integrity.json`:\n```json\n{\n  \"perspective\": \"data-integrity\",\n  \"reviewer\": \"review-data-integrity\",\n  \"timestamp\": \"\u003CISO-8601 now>\",\n  \"dimensions\": [\u003CTask A result>, \u003CTask B result>, \u003CTask C result>],\n  \"weighted_score\": \u003Ccomputed>,\n  \"summary\": \"\u003C2-3 sentences synthesizing data integrity health and key risks>\"\n}\n```\n\n## Output Format\n\nFinding ID prefix: **DIN-**\n- DIN-1xx: Transaction Safety\n- DIN-2xx: Data Consistency\n- DIN-3xx: Schema Design Quality\n",{"model":40,"description":48},"Design-review perspective for projects without ScalarDB: data integrity, transaction safety and\nschema quality.\n","main","fed4c7ebb79ed82ce5c961f944a1ab0ab21bae26","skills\u002Freview-data-integrity",1,"https:\u002F\u002Fgithub.com\u002Fwfukatsu\u002Fnexus-architect","https:\u002F\u002Fnexus-architect-docs-070772.gitlab.io\u002F","https:\u002F\u002Fgithub.com\u002Fwfukatsu","https:\u002F\u002Fwww.scalar-labs.com\u002F","Wataru Fukatsu | Co-Founder and CEO, Scalar, Inc.","2026-11-10 07:17:53",{"source":8,"skillName":5,"commands":60,"prompt":70,"zipAvailable":71},[61,64,67],{"tool":62,"command":63},"npx","npx skills add wfukatsu\u002Fnexus-architect --skill review-data-integrity",{"tool":65,"command":66},"bunx","bunx skills add wfukatsu\u002Fnexus-architect --skill review-data-integrity",{"tool":68,"command":69},"pnpm","pnpm dlx skills add wfukatsu\u002Fnexus-architect --skill review-data-integrity","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fwfukatsu\u002Fnexus-architect\u002Fblob\u002Fmain\u002Fskills\u002Freview-data-integrity\u002FSKILL.md",true,[],[74,103,132,161,186,204],{"skillNo":75,"name":76,"nameZh":6,"description":77,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":78,"categories":79,"keywords":84,"publishedAt":43},"01M6HRH10GPAVTZ8W90WD9HJK1","verify-sql-migration","Prove a SQL migration on data: golden checks against an authorized source database and\ndifferential tests against ScalarDB. 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