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 proyecto26/system-design-skills --skill distributed-searchgit clone --depth 1 https://github.com/proyecto26/system-design-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/proyecto26/system-design-skills/distributed-search)<a href="https://agentmods.dev/skills/proyecto26/system-design-skills/distributed-search"><img src="https://agentmods.dev/badge/skills/proyecto26/system-design-skills/distributed-search/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/proyecto26/system-design-skills/distributed-search"><img src="https://agentmods.dev/badge/skills/proyecto26/system-design-skills/distributed-search.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00117 | $0.02823 |
| Opus 5 | $0.00059 | $0.01411 |
| Sonnet 5 | $0.00023 | $0.00565 |
| Haiku 4.5 | $0.00012 | $0.00282 |
Grade A, and why
distributed-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 9d 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 — 201 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Distributed search
Find the documents that best match a free-text query, ranked by relevance, fast,
across more data than one machine holds. Getting it wrong means either slow
LIKE '%term%' scans that melt the primary database, or a search box that
returns the wrong results and erodes user trust — both are silent until traffic
or corpus size exposes them.
When to reach for this
Users type words and expect ranked, relevant matches — not exact-key lookups.
The corpus is text-heavy (documents, products, logs, messages), queries are
ad-hoc (any term, any combination), and results need ranking, highlighting,
facets, or typeahead. Reach for it when a WHERE col LIKE or full-table scan is
already the read bottleneck, or when you need fuzzy/partial matching a B-tree
index cannot serve.
When NOT to
The access pattern is fetch-by-known-key or a fixed filter — a primary database
index serves that far more cheaply and consistently; keep it in data-storage.
The corpus is tiny (thousands of rows): an in-process filter or the database's
built-in full-text index is enough — a separate search cluster is pure
operational overhead (YAGNI). Search is a derived, eventually-consistent copy
of your data; never make it the system of record.
Clarify first
- Corpus size and growth — document count, average doc size, total index
bytes? (→
back-of-the-envelope) This decides shard count. - Query QPS and shape — read-heavy? term queries, phrase, fuzzy, facets, autocomplete? Latency target (p99)?
- Indexing freshness — must a new/edited doc be searchable in seconds (near-real-time) or is minutes/hours of lag fine?
- Relevance bar — is exact term-match enough, or do users expect "best" results (ranking, synonyms, typo tolerance)?
- Write rate — how many docs/sec change? This sizes the indexing pipeline.
The options
The pipeline (almost always present): a source emits document changes → an indexing pipeline transforms/analyzes them → the inverted index stores term→document postings → the query path matches and ranks. For a crawl-based system (web search), prepend crawl → parse → dedupe; that crawler is its own subsystem feeding the same pipeline.
What ships with it
5 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.
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.
- 9d ago First seen · 201 lines · 117 tokens per session scan A c460f598b429
distributed-search is a skill published in the GitHub repository proyecto26/system-design-skills (69 stars, last pushed 3mo ago), licensed MIT. It adds 117 tokens to every session and 2,823 once invoked, about $0.0006 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 2026-08-30.
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