Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add vasilyu1983/AI-Agents-public --skill ai-vector-braingit clone --depth 1 https://github.com/vasilyu1983/AI-Agents-publicWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/vasilyu1983/ai-agents-public/ai-vector-brain)<a href="https://agentmods.dev/skills/vasilyu1983/ai-agents-public/ai-vector-brain"><img src="https://agentmods.dev/badge/skills/vasilyu1983/ai-agents-public/ai-vector-brain/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/vasilyu1983/ai-agents-public/ai-vector-brain"><img src="https://agentmods.dev/badge/skills/vasilyu1983/ai-agents-public/ai-vector-brain.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00040 | $0.05649 |
| Opus 5.5 | $0.00016 | $0.02260 |
| Sonnet 5.5 | $0.00008 | $0.01130 |
| Haiku 4.5 | $0.00004 | $0.00565 |
Grade A, and why
ai-vector-brain scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 15d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 239 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AI Vector Brain
Build a vector brain implementation. V1 default: Postgres + pgvector. Pick a corpus playbook.
For source code, first show a gain over ripgrep, tree-sitter, or code-graph retrieval (see Corpus Readiness Gate); pgvector fits docs and prose corpora.
Use this skill when the user asks to:
- build a vector brain, RAG brain, LLM brain, repo brain, docs brain, or compliance brain
- turn a repo, docs hub, policy corpus, guide set, or generated context artifacts into a repeatable retrieval layer
- choose a vector-brain backend and produce a concrete implementation path
- create SQL, manifests, ingestion scripts, eval seeds, or an agent retrieval tool contract
Boundary Contract
| Skill | Owns | Does Not Own |
|---|---|---|
ai-context-layer |
Where context lives: memory vs retrieval vs tools, grounding, provenance, lifecycle, app context architecture | Paste-ready SQL, vector DB schemas, embedding pipelines, ingest scripts |
ai-rag |
Retrieval theory: chunking principles, hybrid fusion concepts, reranking concepts, eval theory, when retrieval is wrong | Operational DDL, backend-specific SQL, concrete ingest scripts |
ai-agents |
Agent topology, tool use, planner/critic flows, agent vs workflow decisions | Retrieval backend implementation |
ai-bot-builder |
Bot UX, conversation flows, escalation, channel integration, KB use in a bot surface | KB/vector-brain construction |
ai-vector-brain |
How to build: corpus inventory, manifests, DDL, ingest scripts, embeddings, hybrid search, eval seeds, backend recipes | Broad RAG theory, app context strategy, agent topology, bot UX |
Quick Reference
| Need | Start Here |
|---|---|
| Decide vector vs relational vs graph (upstream choice) | ../software-database-design/references/storage-paradigm-selection.md |
| Build the default brain | references/postgres-pgvector-default.md |
| Choose backend (matrix + decision flow) | references/backend-selection.md |
| Per-backend deep dive (S3 Vectors, Turbopuffer, Pinecone Serverless, Bedrock KB, OpenSearch, Vertex, Azure, edge) | references/backend-selection-extended.md |
| S3 Vectors cost, limits (per-index vector limit, region availability), direct API vs Bedrock KB | references/s3-vectors-backend.md |
| Estimate cost (formulas, sizing, worked examples for hot vs object-backed vs managed) | references/cost-calculation.md |
| Pick corpus recipe | references/corpus-playbooks.md |
| Define portable manifest | references/framework.md |
| Expose brain to agents | references/agent-tool-contract.md |
| Choose eval gates | references/eval-by-corpus-type.md |
| Verify temporal facts and RLS on server PostgreSQL | postgres-memory-verification |
| Prove retrieval before ANN | ../ai-rag/scripts/exact_search_baseline.py + ../ai-rag/assets/eval/golden-retrieval-cases.jsonl |
| Compare backends fairly | ../ai-rag/references/backend-comparison-fixtures.md |
| Trace production retrieval | ../ai-rag/references/observability-tracing-contract.md |
| Debug quality drops | ../ai-rag/references/retrieval-debugging-runbook.md |
| Red-team retrieval security | ../ai-rag/references/security-red-team-cases.md |
| Lift retrieval with contextual summaries | references/contextual-retrieval.md |
| Add cross-encoder reranking | references/reranking-recipe.md |
| Decide the retrieval leg (lexical vs vector vs hybrid vs plain SQL, per query) | references/lexical-vs-vector-vs-hybrid.md |
| Add real BM25 when ts_rank's missing IDF/length-saturation fails evals | references/bm25-when-ts_rank-isnt-enough.md |
| Add a learned-sparse/SPLADE leg when dense misses rare-term precision and tsvector is too brittle | references/learned-sparse-splade-leg.md |
| Ship a local/notebook/single-binary brain (no server) | references/embedded-local-brain.md |
| Tune the lexical layer (tsvector weighting, exact tokens, multilingual, debug) | references/postgres-fts-tuning.md |
| Turn a dev-context compiled hub or repo artifact set into a vector brain | references/dev-context-hub-vector-recipe.md |
| Scale past ~10M vectors (HNSW tuning, DiskANN, quantization, sharding, graph-augmented retrieval) | references/graph-theory-at-scale.md |
| Cut vector RAM/latency at the default tier (quantize + rescore) | references/quantization-and-rescore.md |
| Run the embedder yourself (cost/residency bars a hosted API) | references/embedding-runtime.md |
| Know what v1 doesn't ship and when to add it | references/deferred-extensions.md |
| Ship to production (backups, RLS, observability, migration drills) | references/production-runbook.md |
| Handle model drift and corpus drift without full re-embedding | references/embedding-drift-mitigation.md |
| Validate inventory | scripts/check_brain_manifest.py |
| Embed and load a corpus | scripts/embed_and_load.py |
| Query the brain from the CLI | scripts/retrieve.py |
What ships with it
59 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- agents/openai.yaml 319 B
- assets/sql/001_schema.sql 3.5 KB
- assets/sql/002_indexes_hnsw.sql 3.2 KB
- assets/sql/003_hybrid_search_function.sql 6.8 KB
- assets/sql/004_ingest_ledger.sql 1.1 KB
- assets/sql/005_eval_tables.sql 1005 B
- assets/sql/006_rls_multitenant.sql 2.6 KB
- assets/sql/007_query_logs.sql 2.9 KB
- assets/sql/008_fts_hardening.sql 6.9 KB
- assets/sql/009_bm25_pg_search.sql 4.3 KB
- assets/sql/010_sparsevec.sql 6.0 KB
- assets/sql/011_quantize_rescore.sql 6.5 KB
- assets/sql/012_bitemporal_facts.sql 14 KB
- data/sources.json 38 KB
- examples/docs-hub-brain.manifest.json 1.2 KB
- examples/policy-brain.manifest.json 1.3 KB
- examples/repo-brain.manifest.json 941 B
- learnings.consolidated.md 591 B
- learnings.md 819 B
- references/agent-tool-contract.md 4.2 KB
- references/backend-selection-extended.md 13 KB
- references/backend-selection.md 11 KB
- references/bm25-when-ts_rank-isnt-enough.md 14 KB
- references/contextual-retrieval.md 5.4 KB
- references/corpus-playbooks.md 9.7 KB
- references/cost-calculation.md 17 KB
- references/deferred-extensions.md 9.6 KB
- references/dev-context-hub-vector-recipe.md 16 KB
- references/embedded-local-brain.md 18 KB
- references/embedding-drift-mitigation.md 3.6 KB
- references/embedding-runtime.md 16 KB
- references/eval-by-corpus-type.md 2.6 KB
- references/framework.md 8.4 KB
- references/graph-theory-at-scale.md 17 KB
- references/learned-sparse-splade-leg.md 16 KB
- references/lexical-vs-vector-vs-hybrid.md 12 KB
- references/postgres-fts-tuning.md 10 KB
- references/postgres-memory-verification.md 4.7 KB
- references/postgres-pgvector-default.md 13 KB
- references/production-runbook.md 13 KB
- references/quantization-and-rescore.md 16 KB
- references/reranking-recipe.md 7.9 KB
- references/s3-vectors-backend.md 9.6 KB
- scripts/build_eval_seed.py 6.1 KB runs code
- scripts/build_vector_hub.sh 3.9 KB runs code
- scripts/check_brain_manifest.py 6.5 KB runs code
- scripts/chunk_corpus_files.py 5.7 KB runs code
- scripts/chunk_markdown.py 6.2 KB runs code
- scripts/embed_and_load.py 13 KB runs code
- scripts/inventory_corpus.py 2.4 KB runs code
- scripts/prepare_documents.py 2.3 KB runs code
- scripts/retrieve.py 6.5 KB runs code
- scripts/test_build_vector_hub.py 3.8 KB runs code
- scripts/test_check_brain_manifest.py 2.1 KB runs code
- scripts/test_chunkers_fail_closed.py 1.0 KB runs code
- scripts/test_prepare_documents_metadata.py 1.3 KB runs code
- scripts/test_sql_asset_contracts.py 10 KB runs code
- scripts/test_verify_postgres_memory.py 7.6 KB runs code
- scripts/verify_postgres_memory.py 16 KB runs code
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 15d ago Changed · +2 lines 67a4af0758e0
- 22d ago Changed · -15 lines 21f613afd1c3
- 1mo ago Changed · +4 lines a101cd17c9e7
- 1mo ago First seen · 248 lines · 40 tokens per session scan A a243df011bf4
ai-vector-brain is a skill published in the GitHub repository vasilyu1983/AI-Agents-public (101 stars, last pushed 11d ago), licensed MIT. It adds 40 tokens to every session and 5,649 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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AgentDB Vector Search
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pinecone
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