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 kouroshez/coding-os --skill search-infragit clone --depth 1 https://github.com/kouroshez/coding-osWrote 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/kouroshez/coding-os/search-infra)<a href="https://agentmods.dev/skills/kouroshez/coding-os/search-infra"><img src="https://agentmods.dev/badge/skills/kouroshez/coding-os/search-infra.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 | $0.00195 | $0.02176 |
| Opus 5 | $0.00097 | $0.01088 |
| Sonnet 5 | $0.00039 | $0.00435 |
| Haiku 4.5 | $0.00019 | $0.00218 |
Grade A, and why
search-infra 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 7d 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 — 110 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Search Infrastructure — Ranked Retrieval Done Right
A practical guide to building search that returns the relevant result, not just a matching row. Covers classic full-text (inverted index, BM25) and modern semantic (embeddings, vector ANN) retrieval, and the index-sync discipline that keeps either honest. Stack-agnostic; recipes target Elasticsearch/OpenSearch, Meilisearch, Typesense, and pgvector/Qdrant as the reference engines.
When to Use This Skill
- Adding a search box, autocomplete, or "find similar" to a product.
- Choosing a search engine — managed Elastic vs Meilisearch vs Typesense vs Postgres FTS vs a vector DB.
- Designing an index mapping: which fields are searchable, which are filters, which analyzer.
- Tuning relevance — results are "technically matching but useless", boosting, synonyms, typo tolerance.
- Deciding keyword vs vector vs hybrid retrieval for a given query distribution.
- Keeping the search index consistent with the database that owns the data.
Skip when: the lookup is by exact key / known field on a small set — that is a database index (WHERE id = ?), see db-design, not a search engine. Search earns its complexity only for ranked, fuzzy, or free-text retrieval.
The Index Is Derived, Never the Source of Truth
The single most important rule: the search index is a denormalized, rebuildable projection of data that lives authoritatively elsewhere (the transactional DB). It is eventually consistent and disposable.
- Never write user data only to the search engine. If the index is lost, it must be reconstructable from the source of truth by a full reindex.
- The index is denormalized on purpose — flatten the joins at index time so query time is a single fast lookup. This is the opposite of the normalized source schema (db-design owns that), and that is correct.
- Accept eventual consistency: a document may be ~seconds stale after a write. If a use case cannot tolerate any staleness, read it from the DB, not the index.
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.
- 7d ago First seen · 110 lines · 195 tokens per session scan A 8b8887c3a761
search-infra is a skill published in the GitHub repository kouroshez/coding-os (6 stars, last pushed yesterday), licensed Apache-2.0. It adds 195 tokens to every session and 2,176 once invoked, about $0.0010 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 2026-08-31.
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