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 librefang/librefang-registry --skill elasticsearchgit clone --depth 1 https://github.com/librefang/librefang-registryWrote 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/librefang/librefang-registry/elasticsearch)<a href="https://agentmods.dev/skills/librefang/librefang-registry/elasticsearch"><img src="https://agentmods.dev/badge/skills/librefang/librefang-registry/elasticsearch/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/librefang/librefang-registry/elasticsearch"><img src="https://agentmods.dev/badge/skills/librefang/librefang-registry/elasticsearch.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 | $0.00020 | $0.00647 |
| Opus 5.5 | $0.00008 | $0.00259 |
| Sonnet 5.5 | $0.00004 | $0.00129 |
| Haiku 4.5 | $0.00002 | $0.00065 |
Grade A, and why
elasticsearch 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 1mo 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.
This is a copy
100% identical to elasticsearch — 3 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 43 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Elasticsearch Expert
A search and analytics specialist with deep expertise in Elasticsearch cluster architecture, query DSL, mapping design, and performance optimization. This skill provides production-grade guidance for building search experiences, log analytics pipelines, and time-series data platforms using the Elastic stack.
Key Principles
- Design mappings explicitly before indexing data; relying on dynamic mapping leads to field type conflicts and bloated indices
- Understand the difference between keyword fields (exact match, aggregations, sorting) and text fields (full-text search with analyzers)
- Use index aliases for zero-downtime reindexing, canary deployments, and time-based index rotation
- Size shards between 10-50 GB for optimal performance; too many small shards waste overhead, too few large shards limit parallelism
- Monitor cluster health (green/yellow/red) continuously and investigate yellow status immediately, as it indicates unassigned replica shards
Techniques
- Construct bool queries with must (scored AND), filter (unscored AND), should (OR with minimum_should_match), and must_not (exclusion) clauses
- Use match queries for full-text search with analyzer-aware tokenization, and term queries for exact keyword lookups without analysis
- Build aggregations: terms for top-N cardinality, date_histogram for time bucketing, nested for sub-document analysis, and pipeline aggs like cumulative_sum
- Apply Index Lifecycle Management (ILM) policies with hot/warm/cold/delete phases to automate rollover and data retention
- Reindex with POST _reindex using source/dest, applying scripts for field transformations during migration
- Check cluster allocation with GET _cluster/allocation/explain to diagnose why shards remain unassigned
- Tune search performance with the search profiler API, request caching, and pre-warming for frequently used queries
Common Patterns
- Search-as-you-type: Use the search_as_you_type field type or edge_ngram tokenizer with a match_phrase_prefix query for autocomplete experiences
- Parent-Child Relationships: Use join field types for one-to-many relationships where child documents update independently, avoiding costly nested reindexing
- Cross-cluster Search: Configure remote clusters and use cluster:index syntax to query across multiple Elasticsearch deployments transparently
- Snapshot and Restore: Register a snapshot repository (S3, GCS, or filesystem) and schedule regular snapshots for disaster recovery with SLM policies
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.
- 1mo ago First seen · 43 lines · 20 tokens per session scan A d964b5135fbd
elasticsearch is a skill published in the GitHub repository librefang/librefang-registry (11 stars, last pushed 1mo ago), licensed MIT. It adds 20 tokens to every session and 647 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 100% identical to elasticsearch, differing in 3 lines, and is treated as a copy.
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