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 agentmods add skills/richfrem/agent-plugins-skills/vector-db-searchnpx skills add richfrem/agent-plugins-skills --skill vector-db-searchgit clone --depth 1 https://github.com/richfrem/agent-plugins-skillsWrote 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/richfrem/agent-plugins-skills/vector-db-search)<a href="https://agentmods.dev/skills/richfrem/agent-plugins-skills/vector-db-search"><img src="https://agentmods.dev/badge/skills/richfrem/agent-plugins-skills/vector-db-search.svg" alt="Measured on agentmods" height="20"></a>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 | $0.00052 | $0.00472 |
| Opus 5 | $0.00026 | $0.00236 |
| Sonnet 5 | $0.00010 | $0.00094 |
| Haiku 4.5 | $0.00005 | $0.00047 |
Grade A, and why
vector-db-search 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 2d 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.
What it actually says
Dependencies
This skill requires the chromadb and langchain packages defined in the plugin root.
Vector DB Search
Semantic (meaning-based) search against the ChromaDB vector store using a high-precision Parent-Child architecture. Use for Phase 2 of the 3-phase search protocol (RLM -> Vector -> Grep).
Scripts
| Script | Role |
|---|---|
scripts/query.py |
Semantic search CLI -- recovers context-rich parent chunks. |
scripts/operations.py |
Core domain logic for retrieval. |
scripts/vector_config.py |
Unified profile-based configuration loader. |
Execution Mode
This skill defaults to In-Process mode for zero-latency direct disk access. No background server is required. This ensures maximum stability in isolated project environments.
When to Use
- Phase 1 (RLM Summary Ledger) returned no match or insufficient detail.
- User asks "how does X work?" / "find code that does Y".
- You need specific high-context snippets (Parent chunks) for reasoning.
Execution Protocol
1. Identify Search Profile
Verify available profiles in .agent/learning/vector_profiles.json. The default profile is usually wiki.
2. Run Query
Note: The --profile flag is mandatory to ensure the correct model and collection are loaded.
python ./scripts/query.py "your natural language question" --profile wiki --limit 5
Results include ranked parent chunks (2,000 chars) that provide broad context to the LLM for reasoning.
Rules
- Profile Sovereignty: Always pass
--profileto ensure the correct semantic space is searched. - API Integrity: NEVER attempt to read the database SQLite or parquet files directly. Always use
query.py. - Transparency: When search returns empty results, state which profile and scope were searched.
What ships with it
24 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.
- acceptance-criteria.md 713 B
- assets/resources/architecture_sequence.mmd 53 B
- assets/resources/deployment_model.mmd 48 B
- assets/resources/rag_design_choices.md 50 B
- assets/resources/stabilizers/README.md 53 B
- assets/resources/stabilizers/vector_consistency_check.md 71 B
- evals/evals.json 1.0 KB
- evals/results.tsv 301 B
- fallback-tree.md 1.4 KB
- references/acceptance-criteria.md 42 B
- references/cheapest_models.json 40 B
- references/cheapest_models.md 38 B
- references/fallback-tree.md 36 B
- references/logic.mmd 29 B
- references/unpacking.mmd 33 B
- references/workflow.mmd 32 B
- requirements.in 21 B
- requirements.txt 22 B
- scripts/ingest.py 26 B runs code
- scripts/init.py 24 B runs code
- scripts/operations.py 30 B runs code
- scripts/query.py 25 B runs code
- scripts/vector_config.py 33 B runs code
- scripts/vector_consistency_check.py 44 B 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.
- 2d ago First seen · 55 lines · 52 tokens per session scan A 29b08827f3fd
vector-db-search is a skill published in the GitHub repository richfrem/agent-plugins-skills (6 stars, last pushed yesterday), licensed MIT. It adds 52 tokens to every session and 472 once invoked, about $0.0003 per session on Opus 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 2026-09-03.
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