[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6C15QGCE8GY7GR60CSV0RWA":3,"skill-same:01M6C15QGCE8GY7GR60CSV0RWA":134},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":15,"categories":16,"keywords":25,"publishedAt":45,"linkOnly":46,"markdownContent":47,"markdownBody":48,"frontMatter":49,"refName":58,"commitSha":59,"skillDirectory":60,"forksCount":13,"repositoryHtmlUrl":61,"repositoryHomepageUrl":6,"ownerHtmlUrl":62,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":63,"install":64,"resources":77},"01M6C15QGCE8GY7GR60CSV0RWA","chief-data-officer-advisor",null,"Chief Data Officer advisory for startups: AI training data rights and consent provenance, data product strategy (warehouse vs lakehouse vs mesh, build-vs-buy), B2B customer-data-as-asset valuation and M&A readiness, data team org evolution. Use when deciding whether to train models on customer data, choosing data architecture, valuing data for fundraising or M&A, sequencing data hires, or when user mentions CDO, chief data officer, data strategy, data mesh, lakehouse, training data, data product, data monetization, or customer data asset. NOT a tactical data engineering skill — strategic decisions only.","mne20114-hash\u002Fgest35bi","mne20114-hash","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F224168109?v=4","01M6C0JCR7334CZ39YWTZXX2NG","01M6C0JCWN0NBWMQTW1CPG2Z4G",0,"MIT","https:\u002F\u002Fgithub.com\u002Fmne20114-hash\u002Fgest35bi\u002Fblob\u002Fmain\u002Fclaude-skills\u002Fc-level-advisor\u002Fchief-data-officer-advisor\u002Fskills\u002Fchief-data-officer-advisor\u002FSKILL.md",[17,22],{"categoryNo":18,"categoryName":19,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000031","产品经理","PROFESSION","适用职业",{"categoryNo":23,"categoryName":24,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000033","数据分析师",[26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44],"chief data officer advisor","data","audit","user","evolution","strategy","decisions","team","provenance","engineering","mesh","data engineering","product","lakehouse","data architecture","training","architecture","valuation","customer","2026-11-10 10:12:30",false,"---\nname: \"chief-data-officer-advisor\"\ndescription: \"Chief Data Officer advisory for startups: AI training data rights and consent provenance, data product strategy (warehouse vs lakehouse vs mesh, build-vs-buy), B2B customer-data-as-asset valuation and M&A readiness, data team org evolution. Use when deciding whether to train models on customer data, choosing data architecture, valuing data for fundraising or M&A, sequencing data hires, or when user mentions CDO, chief data officer, data strategy, data mesh, lakehouse, training data, data product, data monetization, or customer data asset. NOT a tactical data engineering skill — strategic decisions only.\"\nlicense: MIT\nmetadata:\n  version: 1.0.0\n  author: Alireza Rezvani\n  category: c-level\n  domain: chief-data-officer-leadership\n  updated: 2026-05-12\n  python-tools: ai_training_data_audit.py, data_product_strategy_picker.py, data_asset_valuator.py\n  frameworks: training-data-rights-matrix, data-product-strategy, customer-data-as-asset, data-team-org-evolution\n---\n\n# Chief Data Officer Advisor\n\nStrategic data leadership for startup CDOs and founders without one. **Four decisions, no surveys:**\n\n1. **Can we train our model on this data?** — origin × consent × use-case matrix\n2. **Warehouse, lakehouse, or mesh — and what do we build vs buy?** — stage-driven architecture\n3. **What is our customer data worth?** — strategic value + M&A multiplier + productization paths\n4. **What data role do we hire next?** — stage-to-role map, centralize-vs-embed trigger\n\nThis skill does **not** cover tactical data engineering. For schema design, observability, query optimization, RAG, or ML platform implementation, see `engineering\u002Fdatabase-designer\u002F`, `engineering\u002Fobservability-designer\u002F`, `engineering\u002Fdata-quality-auditor\u002F`, `engineering\u002Fsql-database-assistant\u002F`, `engineering\u002Frag-architect\u002F`, `engineering\u002Fllm-cost-optimizer\u002F`.\n\n## Keywords\n\nCDO, chief data officer, AI training data, consent provenance, training rights, GDPR Article 6 lawful basis, GDPR Article 22, EU AI Act high-risk, ePrivacy, copyright fair use, hiQ v. LinkedIn, scraped data, synthetic data, data product, data mesh, lakehouse, medallion architecture, dbt, Snowflake, BigQuery, Databricks, Fivetran, Airbyte, reverse ETL, feature store, customer data as asset, data monetization, data productization, anonymization, k-anonymity, differential privacy, M&A data diligence, data org, analytics engineer, data engineer, data scientist, data product manager, centralize vs embed, hub and spoke\n\n## Quick Start\n\n```bash\n# Audit data sources for AI training eligibility\npython scripts\u002Fai_training_data_audit.py                              # uses embedded sample\npython scripts\u002Fai_training_data_audit.py path\u002Fto\u002Fsources.json\n\n# Pick data architecture + build-vs-buy + sequencing\npython scripts\u002Fdata_product_strategy_picker.py                        # uses embedded Series A SaaS\npython scripts\u002Fdata_product_strategy_picker.py path\u002Fto\u002Fprofile.json\n\n# Value the customer data corpus + productization viability\npython scripts\u002Fdata_asset_valuator.py                                 # uses embedded B2B sample\npython scripts\u002Fdata_asset_valuator.py path\u002Fto\u002Fcorpus.json\n```\n\n## Key Questions (ask these first)\n\n- **What decision does this data drive?** (If none, why are we collecting it?)\n- **What's the consent provenance of every source we want to train on?** (TOS-only is not the same as explicit opt-in.)\n- **Who are the internal data consumers, and how many distinct domains do they span?** (Drives centralize-vs-embed and warehouse-vs-mesh.)\n- **In an M&A scenario, is our data a moat or a liability?** (Customer carve-outs in MSAs can flip the answer.)\n- **Are we hiring an analytics engineer or a data scientist next?** (They solve different problems; founders confuse them.)\n- **Have we run an anonymization audit before any external sharing?** (k-anonymity ≥ 5 is the floor, not the ceiling.)\n\n## Core Responsibilities\n\n### 1. AI Training Data Rights\n\nThe 2026 question every startup is facing: **can we use customer data to train our model?**\n\nThe answer is rarely binary. It depends on three independent dimensions:\n\n| Dimension | Values |\n|---|---|\n| **Origin** | 1st-party-explicit-opt-in \u002F 1st-party-TOS-only \u002F partner-licensed \u002F scraped \u002F synthetic |\n| **Data class** | Anonymous aggregate \u002F behavioral \u002F PII \u002F 3rd-party content \u002F regulated (PHI, PCI, kids) |\n| **Use case** | In-product personalization \u002F fine-tune our model \u002F train foundation model \u002F external sharing |\n\nEach combination produces GO \u002F MITIGATE \u002F NO-GO. **Run** `ai_training_data_audit.py` on a JSON inventory of sources.\n\nSee `references\u002Fai_training_data_rights.md` for the full matrix + GDPR Art. 6 lawful basis decision tree + EU AI Act high-risk triggers.\n\n### 2. Data Product Strategy\n\n**Architecture choice (warehouse vs lakehouse vs mesh) is stage-driven, not preference-driven:**\n\n- **Warehouse only** (Snowflake \u002F BigQuery \u002F Postgres): ≤5 data consumers, \u003C2TB, no ML use cases\n- **Lakehouse** (warehouse + object storage, often Databricks or Snowflake-with-Iceberg): 5–25 data consumers, 2TB–1PB, 1–3 ML use cases\n- **Data mesh**: 25+ data consumers across 4+ domains, federated ownership culture in place\n\n**Build vs buy is decided per layer:**\n\n| Layer | Buy unless | Build only if |\n|---|---|---|\n| Storage \u002F warehouse | Never build | (You’re a data infra company) |\n| ELT \u002F ingest | Never build | Source isn’t supported by Fivetran\u002FAirbyte |\n| Modeling (dbt) | Always build | This is your IP |\n| BI \u002F dashboards | Buy at \u003C100 consumers | Embedded analytics for customers |\n| Feature store | Defer until 3+ prod models | Then build OR buy Tecton\u002FHopsworks |\n| ML platform | Defer until 5+ prod models | Then buy SageMaker\u002FVertex\u002FDatabricks |\n\n**Run** `data_product_strategy_picker.py` for a stage-specific recommendation. See `references\u002Fdata_product_strategy.md` for kill criteria per architecture and the build-vs-buy decision tree.\n\n### 3. B2B Customer-Data-as-Asset\n\n**The shift:** at Series B+, customer data is no longer just operational — it’s an asset that can be:\n- A defensibility moat (replicating requires years of customer cohort)\n- An M&A multiplier (1.2x–2x ARR uplift for strategic buyers)\n- A direct revenue stream (anonymized industry benchmarks, embedding endpoints, licensing)\n\nBut it can also be a **liability**:\n- 47\u002F380 customers with MSA carve-outs makes productization legally infeasible\n- Anonymization audits often reveal re-identification risk above tolerable thresholds\n- Regulatory exposure increases linearly with productization (GDPR Art. 28 processors vs Art. 26 joint controllers)\n\n**Run** `data_asset_valuator.py` with corpus characteristics to get strategic value score + productization paths + risk-adjusted value.\n\nSee `references\u002Fcustomer_data_as_asset.md` for the valuation framework, M&A diligence prep checklist, and contractual constraint audit pattern.\n\n### 4. Data Team Org Evolution\n\n**The wrong question:** \"Should we hire a data scientist?\"\n**The right question:** \"What’s the next decision we can’t make because we lack data, and what role unblocks that?\"\n\nStage-to-role map (B2B SaaS baseline):\n\n| Stage | First hire | Then | Then |\n|---|---|---|---|\n| Pre-seed \u002F seed | Founder-as-analyst (SQL + spreadsheets) | — | — |\n| Series A (Series A) | Analyst | Analytics engineer (dbt) | — |\n| Series B | Data engineer | Senior analyst (embedded in GTM) | Data PM (if 3+ teams need data) |\n| Growth | Manager of analytics | ML engineer (if model is core) | Head of Data |\n| Late-stage | Head of Data → CDO | Specialized: BI, MLE, DPO | Federated owners per domain (mesh) |\n\n**Centralize-vs-embed trigger:** when 3+ functional areas (sales, marketing, product, ops, CS) need bespoke data weekly, the central team becomes the bottleneck. Move to hub-and-spoke (central platform + embedded analysts) before that becomes a hiring crisis.\n\nSee `references\u002Fdata_team_org_evolution.md`.\n\n## Workflows\n\n### Workflow 1: AI Training Decision (1 hour)\n**Goal:** Decide whether a specific data source can train a specific use case.\n\n```bash\n# 1. Build sources.json with one entry per data source\n# 2. Run the audit\npython scripts\u002Fai_training_data_audit.py sources.json\n# 3. For each MITIGATE: assign owner + remediation\n# 4. For each NO-GO: document the kill reason for the legal log\n# 5. Cross-check with cs-general-counsel-advisor on top-3 mitigation items\n# 6. Log via \u002Fcs:decide\n```\n\n### Workflow 2: Architecture Decision (1 day)\n**Goal:** Pick warehouse \u002F lakehouse \u002F mesh and the build-vs-buy split for the next 12 months.\n\n```bash\npython scripts\u002Fdata_product_strategy_picker.py profile.json\n# Cross-check with cs-cto-advisor on engineering capacity\n# Cross-check with cs-cfo-advisor on 3-year TCO\n# Log via \u002Fcs:decide; consider \u002Fcs:freeze 90 if signing a multi-year SaaS contract\n```\n\n### Workflow 3: Data Asset Valuation for M&A Prep (3 days)\n**Goal:** Value the data corpus and prepare for due diligence.\n\n1. Inventory the corpus: size, freshness, exclusivity, customer overlap, contractual restrictions\n2. Run `data_asset_valuator.py`\n3. Run the M&A diligence prep checklist in `customer_data_as_asset.md`\n4. Surface contractual carve-outs to cs-general-counsel-advisor for re-papering plan\n5. Decide productization path (benchmark report \u002F embedding endpoint \u002F direct license)\n6. Log via \u002Fcs:decide\n\n### Workflow 4: Data Team Roadmap (1 week)\n**Goal:** Build the next 18 months of data hires aligned to business decisions.\n\n1. List the top 5 decisions the business can’t make today due to missing data or analysis\n2. Map each decision to the role that unblocks it\n3. Sequence hires (one role at a time, ramp before next)\n4. Cross-check with cs-chro-advisor on comp bands and leveling\n5. Identify the centralize-vs-embed trigger date\n\n## Output Standards (when invoked via cs-cdo-advisor)\n\n```\n**Bottom Line:** [one sentence — decision and rationale]\n**The Decision:** [one of the 4 framings]\n**The Evidence:** [numbers, not adjectives]\n**How to Act:** [3 concrete next steps]\n**Your Decision:** [the call only the founder can make]\n```\n\n## Adjacent Skills\n\n- `c-level-advisor\u002Fskills\u002Fcto-advisor\u002F` — architecture capacity, scaling cliffs\n- `c-level-advisor\u002Fskills\u002Fciso-advisor\u002F` — data security, threat modeling for productized data\n- `c-level-advisor\u002Fskills\u002Fgeneral-counsel-advisor\u002F` — contractual constraints, DPA, training-data rights\n- `c-level-advisor\u002Fskills\u002Fcfo-advisor\u002F` — build-vs-buy TCO, M&A valuation math\n- `c-level-advisor\u002Fskills\u002Fchro-advisor\u002F` — data team hiring, leveling, comp\n- `engineering\u002Fskills\u002Fdatabase-designer\u002F` — tactical schema design\n- `engineering\u002Fskills\u002Frag-architect\u002F` — tactical AI\u002FRAG implementation\n- `engineering\u002Fllm-cost-optimizer\u002F` — model cost management\n\n## References\n\n- [ai_training_data_rights.md](references\u002Fai_training_data_rights.md) — The training-rights matrix + GDPR Art. 6 \u002F EU AI Act decision tree\n- [data_product_strategy.md](references\u002Fdata_product_strategy.md) — Warehouse \u002F lakehouse \u002F mesh kill criteria + build-vs-buy decision tree\n- [customer_data_as_asset.md](references\u002Fcustomer_data_as_asset.md) — Valuation framework + M&A diligence prep + productization paths\n- [data_team_org_evolution.md](references\u002Fdata_team_org_evolution.md) — Stage-to-role map + centralize-vs-embed trigger\n\n---\n\n**Version:** 1.0.0\n**Status:** Production Ready\n**Disclaimer:** Decisions touching training data rights, data productization, or M&A data diligence should involve qualified counsel. This skill surfaces decisions and tradeoffs — it does not replace legal review.\n","# Chief Data Officer Advisor\n\nStrategic data leadership for startup CDOs and founders without one. **Four decisions, no surveys:**\n\n1. **Can we train our model on this data?** — origin × consent × use-case matrix\n2. **Warehouse, lakehouse, or mesh — and what do we build vs buy?** — stage-driven architecture\n3. **What is our customer data worth?** — strategic value + M&A multiplier + productization paths\n4. **What data role do we hire next?** — stage-to-role map, centralize-vs-embed trigger\n\nThis skill does **not** cover tactical data engineering. For schema design, observability, query optimization, RAG, or ML platform implementation, see `engineering\u002Fdatabase-designer\u002F`, `engineering\u002Fobservability-designer\u002F`, `engineering\u002Fdata-quality-auditor\u002F`, `engineering\u002Fsql-database-assistant\u002F`, `engineering\u002Frag-architect\u002F`, `engineering\u002Fllm-cost-optimizer\u002F`.\n\n## Keywords\n\nCDO, chief data officer, AI training data, consent provenance, training rights, GDPR Article 6 lawful basis, GDPR Article 22, EU AI Act high-risk, ePrivacy, copyright fair use, hiQ v. LinkedIn, scraped data, synthetic data, data product, data mesh, lakehouse, medallion architecture, dbt, Snowflake, BigQuery, Databricks, Fivetran, Airbyte, reverse ETL, feature store, customer data as asset, data monetization, data productization, anonymization, k-anonymity, differential privacy, M&A data diligence, data org, analytics engineer, data engineer, data scientist, data product manager, centralize vs embed, hub and spoke\n\n## Quick Start\n\n```bash\n# Audit data sources for AI training eligibility\npython scripts\u002Fai_training_data_audit.py                              # uses embedded sample\npython scripts\u002Fai_training_data_audit.py path\u002Fto\u002Fsources.json\n\n# Pick data architecture + build-vs-buy + sequencing\npython scripts\u002Fdata_product_strategy_picker.py                        # uses embedded Series A SaaS\npython scripts\u002Fdata_product_strategy_picker.py path\u002Fto\u002Fprofile.json\n\n# Value the customer data corpus + productization viability\npython scripts\u002Fdata_asset_valuator.py                                 # uses embedded B2B sample\npython scripts\u002Fdata_asset_valuator.py path\u002Fto\u002Fcorpus.json\n```\n\n## Key Questions (ask these first)\n\n- **What decision does this data drive?** (If none, why are we collecting it?)\n- **What's the consent provenance of every source we want to train on?** (TOS-only is not the same as explicit opt-in.)\n- **Who are the internal data consumers, and how many distinct domains do they span?** (Drives centralize-vs-embed and warehouse-vs-mesh.)\n- **In an M&A scenario, is our data a moat or a liability?** (Customer carve-outs in MSAs can flip the answer.)\n- **Are we hiring an analytics engineer or a data scientist next?** (They solve different problems; founders confuse them.)\n- **Have we run an anonymization audit before any external sharing?** (k-anonymity ≥ 5 is the floor, not the ceiling.)\n\n## Core Responsibilities\n\n### 1. AI Training Data Rights\n\nThe 2026 question every startup is facing: **can we use customer data to train our model?**\n\nThe answer is rarely binary. It depends on three independent dimensions:\n\n| Dimension | Values |\n|---|---|\n| **Origin** | 1st-party-explicit-opt-in \u002F 1st-party-TOS-only \u002F partner-licensed \u002F scraped \u002F synthetic |\n| **Data class** | Anonymous aggregate \u002F behavioral \u002F PII \u002F 3rd-party content \u002F regulated (PHI, PCI, kids) |\n| **Use case** | In-product personalization \u002F fine-tune our model \u002F train foundation model \u002F external sharing |\n\nEach combination produces GO \u002F MITIGATE \u002F NO-GO. **Run** `ai_training_data_audit.py` on a JSON inventory of sources.\n\nSee `references\u002Fai_training_data_rights.md` for the full matrix + GDPR Art. 6 lawful basis decision tree + EU AI Act high-risk triggers.\n\n### 2. Data Product Strategy\n\n**Architecture choice (warehouse vs lakehouse vs mesh) is stage-driven, not preference-driven:**\n\n- **Warehouse only** (Snowflake \u002F BigQuery \u002F Postgres): ≤5 data consumers, \u003C2TB, no ML use cases\n- **Lakehouse** (warehouse + object storage, often Databricks or Snowflake-with-Iceberg): 5–25 data consumers, 2TB–1PB, 1–3 ML use cases\n- **Data mesh**: 25+ data consumers across 4+ domains, federated ownership culture in place\n\n**Build vs buy is decided per layer:**\n\n| Layer | Buy unless | Build only if |\n|---|---|---|\n| Storage \u002F warehouse | Never build | (You’re a data infra company) |\n| ELT \u002F ingest | Never build | Source isn’t supported by Fivetran\u002FAirbyte |\n| Modeling (dbt) | Always build | This is your IP |\n| BI \u002F dashboards | Buy at \u003C100 consumers | Embedded analytics for customers |\n| Feature store | Defer until 3+ prod models | Then build OR buy Tecton\u002FHopsworks |\n| ML platform | Defer until 5+ prod models | Then buy SageMaker\u002FVertex\u002FDatabricks |\n\n**Run** `data_product_strategy_picker.py` for a stage-specific recommendation. See `references\u002Fdata_product_strategy.md` for kill criteria per architecture and the build-vs-buy decision tree.\n\n### 3. B2B Customer-Data-as-Asset\n\n**The shift:** at Series B+, customer data is no longer just operational — it’s an asset that can be:\n- A defensibility moat (replicating requires years of customer cohort)\n- An M&A multiplier (1.2x–2x ARR uplift for strategic buyers)\n- A direct revenue stream (anonymized industry benchmarks, embedding endpoints, licensing)\n\nBut it can also be a **liability**:\n- 47\u002F380 customers with MSA carve-outs makes productization legally infeasible\n- Anonymization audits often reveal re-identification risk above tolerable thresholds\n- Regulatory exposure increases linearly with productization (GDPR Art. 28 processors vs Art. 26 joint controllers)\n\n**Run** `data_asset_valuator.py` with corpus characteristics to get strategic value score + productization paths + risk-adjusted value.\n\nSee `references\u002Fcustomer_data_as_asset.md` for the valuation framework, M&A diligence prep checklist, and contractual constraint audit pattern.\n\n### 4. Data Team Org Evolution\n\n**The wrong question:** \"Should we hire a data scientist?\"\n**The right question:** \"What’s the next decision we can’t make because we lack data, and what role unblocks that?\"\n\nStage-to-role map (B2B SaaS baseline):\n\n| Stage | First hire | Then | Then |\n|---|---|---|---|\n| Pre-seed \u002F seed | Founder-as-analyst (SQL + spreadsheets) | — | — |\n| Series A (Series A) | Analyst | Analytics engineer (dbt) | — |\n| Series B | Data engineer | Senior analyst (embedded in GTM) | Data PM (if 3+ teams need data) |\n| Growth | Manager of analytics | ML engineer (if model is core) | Head of Data |\n| Late-stage | Head of Data → CDO | Specialized: BI, MLE, DPO | Federated owners per domain (mesh) |\n\n**Centralize-vs-embed trigger:** when 3+ functional areas (sales, marketing, product, ops, CS) need bespoke data weekly, the central team becomes the bottleneck. Move to hub-and-spoke (central platform + embedded analysts) before that becomes a hiring crisis.\n\nSee `references\u002Fdata_team_org_evolution.md`.\n\n## Workflows\n\n### Workflow 1: AI Training Decision (1 hour)\n**Goal:** Decide whether a specific data source can train a specific use case.\n\n```bash\n# 1. Build sources.json with one entry per data source\n# 2. Run the audit\npython scripts\u002Fai_training_data_audit.py sources.json\n# 3. For each MITIGATE: assign owner + remediation\n# 4. For each NO-GO: document the kill reason for the legal log\n# 5. Cross-check with cs-general-counsel-advisor on top-3 mitigation items\n# 6. Log via \u002Fcs:decide\n```\n\n### Workflow 2: Architecture Decision (1 day)\n**Goal:** Pick warehouse \u002F lakehouse \u002F mesh and the build-vs-buy split for the next 12 months.\n\n```bash\npython scripts\u002Fdata_product_strategy_picker.py profile.json\n# Cross-check with cs-cto-advisor on engineering capacity\n# Cross-check with cs-cfo-advisor on 3-year TCO\n# Log via \u002Fcs:decide; consider \u002Fcs:freeze 90 if signing a multi-year SaaS contract\n```\n\n### Workflow 3: Data Asset Valuation for M&A Prep (3 days)\n**Goal:** Value the data corpus and prepare for due diligence.\n\n1. Inventory the corpus: size, freshness, exclusivity, customer overlap, contractual restrictions\n2. Run `data_asset_valuator.py`\n3. Run the M&A diligence prep checklist in `customer_data_as_asset.md`\n4. Surface contractual carve-outs to cs-general-counsel-advisor for re-papering plan\n5. Decide productization path (benchmark report \u002F embedding endpoint \u002F direct license)\n6. Log via \u002Fcs:decide\n\n### Workflow 4: Data Team Roadmap (1 week)\n**Goal:** Build the next 18 months of data hires aligned to business decisions.\n\n1. List the top 5 decisions the business can’t make today due to missing data or analysis\n2. Map each decision to the role that unblocks it\n3. Sequence hires (one role at a time, ramp before next)\n4. Cross-check with cs-chro-advisor on comp bands and leveling\n5. Identify the centralize-vs-embed trigger date\n\n## Output Standards (when invoked via cs-cdo-advisor)\n\n```\n**Bottom Line:** [one sentence — decision and rationale]\n**The Decision:** [one of the 4 framings]\n**The Evidence:** [numbers, not adjectives]\n**How to Act:** [3 concrete next steps]\n**Your Decision:** [the call only the founder can make]\n```\n\n## Adjacent Skills\n\n- `c-level-advisor\u002Fskills\u002Fcto-advisor\u002F` — architecture capacity, scaling cliffs\n- `c-level-advisor\u002Fskills\u002Fciso-advisor\u002F` — data security, threat modeling for productized data\n- `c-level-advisor\u002Fskills\u002Fgeneral-counsel-advisor\u002F` — contractual constraints, DPA, training-data rights\n- `c-level-advisor\u002Fskills\u002Fcfo-advisor\u002F` — build-vs-buy TCO, M&A valuation math\n- `c-level-advisor\u002Fskills\u002Fchro-advisor\u002F` — data team hiring, leveling, comp\n- `engineering\u002Fskills\u002Fdatabase-designer\u002F` — tactical schema design\n- `engineering\u002Fskills\u002Frag-architect\u002F` — tactical AI\u002FRAG implementation\n- `engineering\u002Fllm-cost-optimizer\u002F` — model cost management\n\n## References\n\n- [ai_training_data_rights.md](references\u002Fai_training_data_rights.md) — The training-rights matrix + GDPR Art. 6 \u002F EU AI Act decision tree\n- [data_product_strategy.md](references\u002Fdata_product_strategy.md) — Warehouse \u002F lakehouse \u002F mesh kill criteria + build-vs-buy decision tree\n- [customer_data_as_asset.md](references\u002Fcustomer_data_as_asset.md) — Valuation framework + M&A diligence prep + productization paths\n- [data_team_org_evolution.md](references\u002Fdata_team_org_evolution.md) — Stage-to-role map + centralize-vs-embed trigger\n\n---\n\n**Version:** 1.0.0\n**Status:** Production Ready\n**Disclaimer:** Decisions touching training data rights, data productization, or M&A data diligence should involve qualified counsel. This skill surfaces decisions and tradeoffs — it does not replace legal review.\n",{"name":5,"license":14,"metadata":50,"description":7},{"author":51,"domain":52,"updated":53,"version":54,"category":55,"frameworks":56,"python-tools":57},"Alireza Rezvani","chief-data-officer-leadership",1778544000000,"1.0.0","c-level","training-data-rights-matrix, data-product-strategy, customer-data-as-asset, data-team-org-evolution","ai_training_data_audit.py, data_product_strategy_picker.py, data_asset_valuator.py","main","510dd2917e051cc953dc55f56fd6ef442aa01b38","claude-skills\u002Fc-level-advisor\u002Fchief-data-officer-advisor\u002Fskills\u002Fchief-data-officer-advisor","https:\u002F\u002Fgithub.com\u002Fmne20114-hash\u002Fgest35bi","https:\u002F\u002Fgithub.com\u002Fmne20114-hash","2026-10-28 07:50:31",{"source":8,"skillName":5,"commands":65,"prompt":75,"zipAvailable":76},[66,69,72],{"tool":67,"command":68},"npx","npx skills add mne20114-hash\u002Fgest35bi --skill 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a curated supplementary reading list from any course syllabus using Consensus academic search. Grill-me intake (syllabus input format + course audience + year range) plus a grouping forcing-options checkpoint before any search runs — so the reading list matches the course's level and recency need. Parses the syllabus to extract topics and learning outcomes, searches Consensus for recent peer-reviewed papers per topic, and produces a professionally formatted .docx with clickable Consensus links, plain-language summaries calibrated to audience level, and Bloom-higher-order discussion questions tied to course learning goals. Use when the user uploads a syllabus, course outline, or curriculum document and wants supplementary readings (e.g., 'create a reading list from this syllabus', 'find recent papers for my course') — even casual mentions with a syllabus attached should trigger this skill.","https:\u002F\u002Fgithub.com\u002Fmne20114-hash\u002Fgest35bi\u002Fblob\u002Fmain\u002Fclaude-skills\u002Fresearch\u002Fsyllabus\u002Fskills\u002Fsyllabus\u002FSKILL.md",[141,146,149],{"categoryNo":142,"categoryName":143,"taxonomyType":144,"taxonomyTypeDesc":145},"01JBSEEDCAT000000000000002","文档处理","SCENARIO","使用场景",{"categoryNo":147,"categoryName":148,"taxonomyType":144,"taxonomyTypeDesc":145},"01JBSEEDCAT000000000000023","Skill与智能体开发",{"categoryNo":150,"categoryName":151,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000034","行政与办公人员",[137,153,154,155,156,157,28,158,159,160,161,162,163,164,165,166,167,168,169,170],"docx","pr","create","intake","search","integrity","checkpoint","learning","js","format","direct","Rules","find","consensus","list","extract","use","outcomes",{"skillNo":172,"name":173,"nameZh":6,"description":174,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":175,"categories":176,"keywords":181,"publishedAt":45},"01M6C27DMGZ4D6ERNHSVA8H8RJ","pulse","Multi-source recency research skill that takes the pulse of any topic across Reddit, Hacker News, the open web, and optionally X\u002FTwitter within a configurable recent window (default 30 days). Forcing intake clarifies topic specificity, angle (trend\u002Fsentiment\u002Fproblems\u002Fopportunities\u002Fcomparison), time window, and platform scope before searching. Returns a synthesized briefing with citations, engagement metrics, and cross-platform pattern analysis. Use when the user requests multi-source recency intelligence on a topic (e.g., 'pulse on [topic]', 'what's happening with [topic]', 'what are people saying about [topic]', 'current conversation about [topic]', 'take the pulse of [topic]', 'trending: [topic]', 'find me info on [topic]'), and for competitor research, trend discovery, tool comparisons, and audience sentiment analysis.","https:\u002F\u002Fgithub.com\u002Fmne20114-hash\u002Fgest35bi\u002Fblob\u002Fmain\u002Fclaude-skills\u002Fresearch\u002Fpulse\u002Fskills\u002Fpulse\u002FSKILL.md",[177,180],{"categoryNo":178,"categoryName":179,"taxonomyType":144,"taxonomyTypeDesc":145},"01JBSEEDCAT000000000000021","资料检索与研究",{"categoryNo":147,"categoryName":148,"taxonomyType":144,"taxonomyTypeDesc":145},[173,182,183,154,184,156,28,185,186,158,187,188,189,190,163,164,191,165,29,169],"research","web","sentiment analysis","sentiment","Citations","analysis","discovery","open","tool","twitter",{"skillNo":193,"name":194,"nameZh":6,"description":195,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":196,"categories":197,"keywords":203,"publishedAt":45},"01M6C279M59X635VPD6Y9R616J","patent","Patent prior-art and landscape intelligence skill — not generic patent help. Commits to one of five sub-use-cases via forcing intake (novelty search \u002F freedom-to-operate \u002F competitive landscape \u002F acquisition diligence \u002F litigation prior-art) before any search runs. Searches Google Patents, Espacenet, USPTO, and optionally Lens.org for citation-graph signals. Output is an editable Word document (.docx) with verdict, ranked closest art (claim-text extracted), CPC-class-aware landscape, family-resolved hits, geographic coverage, FTO flags where applicable, strategy recommendations, and full audit log. Use when the user asks for patent searching or analysis (e.g., 'prior art search for [invention]', 'freedom to operate analysis for [product]'). Produces search signal, not legal advice — always recommends consulting a patent attorney before filing or licensing decisions. Trademark, copyright, and trade-secret questions are out of scope.","https:\u002F\u002Fgithub.com\u002Fmne20114-hash\u002Fgest35bi\u002Fblob\u002Fmain\u002Fclaude-skills\u002Fresearch\u002Fpatent\u002Fskills\u002Fpatent\u002FSKILL.md",[198,199,200],{"categoryNo":142,"categoryName":143,"taxonomyType":144,"taxonomyTypeDesc":145},{"categoryNo":147,"categoryName":148,"taxonomyType":144,"taxonomyTypeDesc":145},{"categoryNo":201,"categoryName":202,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000037","律师与法务",[194,153,154,204,205,38,156,157,28,187,206,207,208,164,209,32,31,210,169,211],"competitive landscape","routing","commits","Word","text","acquisition","coverage","advice",{"skillNo":213,"name":214,"nameZh":6,"description":215,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":216,"categories":217,"keywords":222,"publishedAt":45},"01M6C275RZWNRB3CCB14QQM7ES","notebooklm","Browser automation skill for controlling Google's NotebookLM. Use when the user wants anything done in NotebookLM (e.g., 'open NotebookLM', 'check my [name] notebook', 'ask my notebook about X', 'add [source] to NotebookLM', 'generate a Video Overview from my notebook', 'use NotebookLM Studio'). Handles reading and querying notebooks, adding sources (URLs, text, files, YouTube links, synthesized content), generating Studio outputs (Audio\u002FVideo Overviews, Mind Maps, Reports incl. Briefing Doc\u002FStudy Guide\u002FFAQ, Flashcards, Quiz, slide decks, infographics — discover the exact set from the live Studio panel; the UI evolves fast), and creating new notebooks. Requires browser automation environment — fails gracefully when unavailable.","https:\u002F\u002Fgithub.com\u002Fmne20114-hash\u002Fgest35bi\u002Fblob\u002Fmain\u002Fclaude-skills\u002Fresearch\u002Fnotebooklm\u002Fskills\u002Fnotebooklm\u002FSKILL.md",[218,221],{"categoryNo":219,"categoryName":220,"taxonomyType":144,"taxonomyTypeDesc":145},"01JBSEEDCAT000000000000016","图像与多媒体",{"categoryNo":147,"categoryName":148,"taxonomyType":144,"taxonomyTypeDesc":145},[214,223,224,182,225,226,227,228,229,230,231,232,189,208,163,233,234,235,236,169],"google","browser automation","automation","generate","browser","fast","SET","doc","Video","Audio","check","ask","environment","overview",{"skillNo":238,"name":239,"nameZh":6,"description":240,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":241,"categories":242,"keywords":247,"publishedAt":45},"01M6C2717B0QDJ3Y4PJP77BMW5","litreview","Academic literature orientation skill that searches papers via free keyless APIs (PubMed E-utilities + OpenAlex) by default — with the Consensus MCP as an optional enhancement lane when connected — builds a strategic search plan using PICO (default) or SPIDER \u002F Decomposition \u002F hybrid as fallbacks, and synthesizes findings into a formatted Word (.docx) research guide. Grill-me intake (research question specificity + framework hint + tentative depth) before the recon search; a second forcing checkpoint after Phase 2 confirms framework + sub-areas + depth before searches consume budget. Configurable depth (5\u002F10\u002F20 queries) controls coverage vs. speed. Output is a 'launching pad' — an orientation guide that lets a researcher dive in confidently, not a finished review. Use when the user starts literature-oriented research (e.g., 'litreview on [topic]', 'literature review on [topic]', 'I'm starting a literature review on X', 'I'm writing a paper on X', 'help me research X', 'I'm doing research on X', 'can you help me research X'). Do NOT use for single one-off paper searches wanting a quick list — that's a plain PubMed\u002FOpenAlex (or Consensus) query.","https:\u002F\u002Fgithub.com\u002Fmne20114-hash\u002Fgest35bi\u002Fblob\u002Fmain\u002Fclaude-skills\u002Fresearch\u002Flitreview\u002Fskills\u002Flitreview\u002FSKILL.md",[243,244,245,246],{"categoryNo":142,"categoryName":143,"taxonomyType":144,"taxonomyTypeDesc":145},{"categoryNo":178,"categoryName":179,"taxonomyType":144,"taxonomyTypeDesc":145},{"categoryNo":147,"categoryName":148,"taxonomyType":144,"taxonomyTypeDesc":145},{"categoryNo":150,"categoryName":151,"taxonomyType":20,"taxonomyTypeDesc":21},[239,153,248,249,250,154,156,157,251,252,253,159,254,207,255,164,166,256,210,169],"MCP","writing","review","lane","keyless","literature review","plan","researcher","question",{"skillNo":258,"name":259,"nameZh":6,"description":260,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":261,"categories":262,"keywords":267,"publishedAt":45},"01M6C26X5G3WDVWQ40Y16ZS19F","grants","NIH grant research skill for clinical researchers. Grill-me intake (research idea + career stage + preliminary data + environment + submission posture + known institute targets) locks down the funding strategy before any search runs. Runs a 5-facet Consensus positioning analysis (with draft Significance\u002FInnovation language), maps the research to the right NIH institutes and study sections via RePORTER, finds NOSIs and funded overlap, and produces an editable Word document (.docx) with budget\u002Fscope-aware mechanism recommendations, submission timelines, and a mandatory program officer recommendation. Use when the user asks about research funding or makes any grant-related request (e.g., 'grants for [topic]', 'find grants for my research idea', 'what grants match my research', 'help me find NIH funding', 'grant opportunities for my research'). NIH-only scope — non-NIH funders (PCORI, DOD CDMRP, VA, foundations) are out of scope and flagged at intake.","https:\u002F\u002Fgithub.com\u002Fmne20114-hash\u002Fgest35bi\u002Fblob\u002Fmain\u002Fclaude-skills\u002Fresearch\u002Fgrants\u002Fskills\u002Fgrants\u002FSKILL.md",[263,264,265,266],{"categoryNo":142,"categoryName":143,"taxonomyType":144,"taxonomyTypeDesc":145},{"categoryNo":178,"categoryName":179,"taxonomyType":144,"taxonomyTypeDesc":145},{"categoryNo":147,"categoryName":148,"taxonomyType":144,"taxonomyTypeDesc":145},{"categoryNo":150,"categoryName":151,"taxonomyType":20,"taxonomyTypeDesc":21},[259,153,182,154,268,156,157,28,27,269,158,187,207,164,165,166,31,235,169,270],"help","Budget","innovation"]