pgvector-semantic-search/BB-SKILLS-REVIEW.json

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{
  "format": 1,
  "checked_at": "2026-10-03",
  "source_commit": "b236d3583fb51f5ef009d2c95d4fc361df748280",
  "requirements": "PostgreSQL with pgvector 0.8.0+ for all features, compatible binaries and installation rights. Generate embeddings separately with a suitable model; optional vectorscale is separate.",
  "cost_notes": "Apache-2.0 material; separate hosting, tooling, model access and services may have costs. Full skill/agent runtime evaluation has not been performed. Capacity, recall, speed and memory tables are author estimates, not measured guarantees. Use nonzero vectors of matching dimensions from the same embedding model. Review large maintenance_work_mem values against your limits. EXPLAIN ANALYZE executes queries. Optional maintenance/DDL is not read-only. No model, credentials or binaries bundled.",
  "runtime_tested": false,
  "checks": [
    "Pinned original bytes verified against Git blob hashes",
    "Complete Apache-2.0 license and publisher NOTICE retained",
    "Core identity and local references validated",
    "Selected SQL and version boundaries reviewed; limitations disclosed"
  ],
  "file_hashes": {
    "pgvector-semantic-search/SKILL.md": "0c23028b9bc8f9a8bc4f45042d5996877533f60d7798a074ce73373ebc3e24f6",
    "pgvector-semantic-search/LICENSE.upstream.txt": "5c272e206669b79ee20fa89fa8fa888d5b56186f16c55a5614753ad3412e2ae9",
    "pgvector-semantic-search/NOTICE.upstream.txt": "505ea3e3e023dcc469adaf6134a6d166bc91b18e3a7565665e65bec96f6ef02d",
    "pgvector-semantic-search/SOURCE.txt": "2de1be0bc6c1db87806cd7e3a75f2fb699408bfc04d059c741200f7ec5f41b5d"
  }
}