AutoGineer
Generated banner for Elasticsearch Search Relevance
Generated logo for Elasticsearch Search Relevance

Elasticsearch Search Relevance

ElasticOfficialCreated by elastic

  • Skill
  • Tool integration
  • Software development
  • Apache-2.0
  • No issues found
Download ZIP

From the author

Improve Elasticsearch search relevance for content and catalog indices: pin or promote results with query rules (correct rule type, criteria, and rule-query wiring) and tune organic ranking with multi_match, field boosts, and analysis grounded in the index mapping. Use when search results rank poorly, a specific document must appear first for a query, or the user asks to tune full-text matching — not for ES|QL analytics, index ingest, or cluster health.
— the description by elastic

AutoGineer summary

Written by AutoGineer from the skill’s own files, not by the author

This skill provides a structured process for improving full-text search relevance in Elasticsearch indices. It distinguishes between deterministic merchandising (pinning specific documents) and organic ranking improvements. Users are guided to diagnose index mappings, select the appropriate relevance lever—either query rules for pinning or multi-match queries with field boosts for organic ranking—and verify results through side-by-side testing. The skill emphasizes using the elastic CLI for all operations and strictly avoiding anti-patterns like sorting by price or keyword fields to manipulate search relevance.

What it does

  • Diagnoses search relevance issues by inspecting index mappings and current query structures.
  • Implements deterministic document pinning using Elasticsearch query rules.
  • Tunes organic search ranking via multi-match queries with strategic field boosting.
  • Provides a testing framework to compare baseline and candidate search results.
  • Maps HTTP-shorthand operations to the required elastic CLI commands.
  • Validates rule criteria and search query wiring to ensure relevance levers function as expected.

When to use it

  • When specific documents must appear at the top of search results for a given query.
  • When search results for content or catalog indices are generally weak or irrelevant.
  • When you need to improve recall for multi-word user queries.
  • When you need to transition from fragile sorting hacks to robust relevance tuning.

Advantages

  • Ensures predictable search behavior for merchandising through query rules.
  • Promotes evidence-based tuning by requiring side-by-side top-hit comparisons.
  • Prevents common anti-patterns like using keyword fields for full-text search.
  • Standardizes the use of the elastic CLI for consistent environment interaction.

Usage examples

“I need to make sure the product with ID SKU123 always appears first when users search for 'sale'.”
The agent will create a pinned query rule for the 'sale' query string and provide the search body using the rule query to ensure SKU123 is promoted.
“My search results for 'running shoes' are poor; they only match the description field.”
The agent will analyze the index mapping, propose a multi_match query that boosts the title field, and guide you through testing the new ranking.

Install

  1. Download the skill as a ZIP:

    https://autogineer.com/agent-skills/elastic/elasticsearch-search-relevance/download

  2. Unzip it into the cross-client skills folder of your project:

    mkdir -p .agents/skills && unzip elasticsearch-search-relevance.zip -d .agents/skills

Documentation by elastic

Elasticsearch Search Relevance

Improve full-text search results on content and catalog indices. Diagnose the mapping and current query, choose the right relevance lever (query rules for deterministic pinning vs multi_match and field boosts for organic ranking), apply the change, and verify top hits before reporting success.

<!-- begin-partial: preamble -->

Environment Configuration

This skill executes Elasticsearch operations through the elastic CLI. If the elastic CLI is not installed, tell the user what it is needed for. Do not guess credentials, call the HTTP API directly, or attempt other workarounds.

This skill references operations in HTTP-shorthand form (e.g., GET /, GET /_cat/indices, GET /{index}/_mapping, GET /{index}/_settings/index.mode, POST /_query). The Operations table at the end of this document maps each shorthand to the equivalent elastic CLI command — always use the CLI rather than calling the HTTP API directly.

<!-- end-partial: preamble -->

Scope

This skill covers Query DSL relevance on indices with text (and optional keyword) fields — product catalogs, documentation, knowledge bases. It uses POST /{index}/_search for evaluation and query-rules APIs for pinned or excluded documents.

Out of scope:

  • ES|QL search (POST /_query) — use the elasticsearch-esql skill.

  • Semantic / vector / hybrid retrieval — different field types and retrievers.

  • Sorting by price, date, or popularity instead of fixing text relevance unless the user explicitly wants non-relevance ordering.

Relevance levers

User intentLeverAPIs
Always show document X first for query QQuery rules — pinned rule + rule query in searchPUT /_query_rules/{ruleset_id}, POST /{index}/_search
Hide specific documents for query QQuery rules — exclude rule + rule querySame
Better ranking for open-ended text queriesmulti_match across mapped text fields with field boostsPOST /{index}/_search
Tokens not matching user languageOperator, minimum_should_match, or synonym analyzersPOST /{index}/_search, optionally POST /{index}/_analyze

Decision rule: If the user names a document that must rank first for a specific query, use query rules. If results are generally weak for a phrase, tune the organic query from the mapping. Do not simulate pinning with extreme boosts, function_score, or sort clauses.

Process

  1. Inspect the mapping and current query. Call GET / to confirm connectivity. When the index is unknown, narrow candidates with GET /_cat/indices, then call GET /{index}/_mapping.

    From the mapping, list every text field (e.g., title, description) and every keyword field used for filters (brand, category). Note which fields are short (precision) vs long (recall). Read the user's current search body if provided — identify which fields it queries and whether it already uses rule, multi_match, or single-field match.

    Decision: Is the problem deterministic promotion (one doc must win for one query) or organic ranking (several docs should score better)? Data needed: index name, mapping properties, current query JSON, example query strings, and target document ID(s) when pinning.

  2. Choose the relevance lever. Apply the decision from step 1:

    • Pinning / promotion → Create a query-rules ruleset with a rule of type pinned (never exclude for promotion). Set criteria so the rule fires only for the intended query text — e.g., contains or exact on a metadata key such as query_string with value "sale". Set actions to pin the correct document via ids (e.g., ["SKU123"]) or docs (e.g., [{"_index":"catalog","_id":"SKU123"}]). Use docs when _id may not be unique across indices. Read Query Rules Reference for full structure.

    • Organic ranking → Replace single-field match on a long field with multi_match across the mapped text fields. Boost short fields (typically title^2 with description unboosted). Consider operator, minimum_should_match, or synonym-aware analyzers when multi-word recall is still poor — but do not sort by price, date, or keyword fields to fake better text relevance, and do not query .keyword sub-fields with term for analyzed user phrases. Read Multi-Match Tuning.

    Decision: Pick exactly one primary lever per request. Data needed: chosen fields and boosts, ruleset ID and rule ID names, criteria metadata keys, and pinned document identifiers.

  3. Apply the change. Execute the APIs for the chosen lever:

    Query rules path

    • Create or replace the ruleset with PUT /_query_rules/{ruleset_id} (or add one rule with PUT /_query_rules/{ruleset_id}/_rule/{rule_id}).

    • Confirm structure with GET /_query_rules/{ruleset_id}.

    • Validate criteria with POST /_query_rules/{ruleset_id}/_test using the same match_criteria you will pass at search time.

    • Wire the search: POST /{index}/_search must use a rule query whose ruleset_id references the ruleset and whose match_criteria supplies values for every criteria metadata key (e.g., "query_string": "sale"). Place the normal relevance clause inside organic. Creating the ruleset alone does not pin anything — the pin applies only when search includes the rule query.

    Organic tuning path

    • Build a candidate multi_match (or equivalent bool/should) query from the mapping.

    • Optionally inspect analysis with POST /{index}/_analyze on sample query text when tokenization explains misses.

    Decision: Stop after one coherent change set; avoid stacking unrelated edits before testing.

  4. Test and compare top hits. Before and after each candidate, call POST /{index}/_search with the same size (≥ 10), the user's query string, and "track_scores": true. For pinning, the search body must include the rule query from step 3.

    Compare for each run:

    • Top _id values and order

    • _score where relevant

    • Key _source fields (title, description, product id)

    For pinning, confirm the target document (e.g., SKU123) is first when match_criteria matches the query and that organic matches still appear below. For organic tuning, confirm titles and intent-aligned documents rise without relying on sort or keyword exact-match hacks.

    Decision: Ship the candidate that wins on evidence; if none improve results, report what was tried and propose the next lever (e.g., synonyms or additional fields). Data needed: side-by-side top-hit lists from baseline and candidate queries.

Examples

Pin SKU123 for query "sale" on catalog

Wrong: Boost SKU123, sort by _id, or create a ruleset without a rule search query.

Right:

  1. PUT /_query_rules/catalog-sale-pin with a pinned rule, criteria matching query text "sale", actions pinning SKU123.

  2. POST /catalog/_search with:

{
  "query": {
    "rule": {
      "ruleset_id": "catalog-sale-pin",
      "match_criteria": { "query_string": "sale" },
      "organic": {
        "multi_match": {
          "query": "sale",
          "fields": ["title^2", "description"]
        }
      }
    }
  },
  "size": 10
}

Verify SKU123 is hit #1 and remaining hits are organic matches below the pin.

Improve "running shoes" when only description is searched

Mapping provides title and description as text, plus brand and category as keyword.

Wrong: Keep match on description only; sort by price; term query on title.keyword.

Right:

  1. Baseline: POST /catalog/_search with the user's current match on description; record top hits.

  2. Candidate: POST /catalog/_search with:

{
  "query": {
    "multi_match": {
      "query": "running shoes",
      "fields": ["title^2", "description"],
      "type": "best_fields",
      "operator": "or",
      "minimum_should_match": "75%"
    }
  },
  "size": 10
}

  1. Compare top hits — documents with "running shoes" in title should rank above description-only matches. If recall is still thin, consider synonym expansion in a follow-up iteration (not sort-by-price).

Guidelines

  • Ground every field name in the mapping — never invent name, content, or body without checking GET /{index}/_mapping.

  • Query rules for pins, boosts for ranking — merchandising belongs in query rules; field boosts belong in organic queries.

  • Match criteria wiring is mandatory — metadata keys in rule criteria must appear in the search rule.match_criteria object with the runtime values (typically the user's query string).

  • Test before claiming success — run baseline and candidate searches; cite top-hit changes.

  • Keyword fields filter; text fields search — use keyword fields in filter context, not as the primary full-text target for natural language.

  • Always deliver the concrete artifact — even when you cannot connect to a cluster to verify, produce the full ruleset JSON (for pinning) or the candidate query body (for organic tuning), then explain how to verify once the connection is available. Never stop at a high-level outline.

References

Operations

HTTP API (shorthand)elastic CLI command
GET /elastic es info
GET /_cat/indiceselastic es cat indices --index '<pattern>'
GET /{index}/_mappingelastic es indices get-mapping --index '<index>'
PUT /_query_rules/{ruleset_id}elastic es query-rules put-ruleset --ruleset-id '<id>' --rules '<json>'
PUT /_query_rules/{ruleset_id}/_rule/{rule_id}elastic es query-rules put-rule --ruleset-id '<id>' --rule-id '<id>' --type pinned --criteria '<json>' --actions '<json>'
GET /_query_rules/{ruleset_id}elastic es query-rules get-ruleset --ruleset-id '<id>'
POST /_query_rules/{ruleset_id}/_testelastic es query-rules test --ruleset-id '<id>' --match-criteria '<json>'
POST /{index}/_searchelastic es search --index '<index>' --query '<json>'
POST /{index}/_analyzeelastic es indices analyze --index '<index>' --field '<field>' --text '<text>'

Files

LICENSE

                                 Apache License
                           Version 2.0, January 2004
                        http://www.apache.org/licenses/

   TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION

   1. Definitions.

      "License" shall mean the terms and conditions for use, reproduction,
      and distribution as defined by Sections 1 through 9 of this document.

      "Licensor" shall mean the copyright owner or entity authorized by
      the copyright owner that is granting the License.

      "Legal Entity" shall mean the union of the acting entity and all
      other entities that control, are controlled by, or are under common
      control with that entity. For the purposes of this definition,
      "control" means (i) the power, direct or indirect, to cause the
      direction or management of such entity, whether by contract or
      otherwise, or (ii) ownership of fifty percent (50%) or more of the
      outstanding shares, or (iii) beneficial ownership of such entity.

      "You" (or "Your") shall mean an individual or Legal Entity
      exercising permissions granted by this License.

      "Source" form shall mean the preferred form for making modifications,
      including but not limited to software source code, documentation
      source, and configuration files.

      "Object" form shall mean any form resulting from mechanical
      transformation or translation of a Source form, including but
      not limited to compiled object code, generated documentation,
      and conversions to other media types.

      "Work" shall mean the work of authorship, whether in Source or
      Object form, made available under the License, as indicated by a
      copyright notice that is included in or attached to the work
      (an example is provided in the Appendix below).

      "Derivative Works" shall mean any work, whether in Source or Object
      form, that is based on (or derived from) the Work and for which the
      editorial revisions, annotations, elaborations, or other modifications
      represent, as a whole, an original work of authorship. For the purposes
      of this License, Derivative Works shall not include works that remain
      separable from, or merely link (or bind by name) to the interfaces of,
      the Work and Derivative Works thereof.

      "Contribution" shall mean any work of authorship, including
      the original version of the Work and any modifications or additions
      to that Work or Derivative Works thereof, that is intentionally
      submitted to Licensor for inclusion in the Work by the copyright owner
      or by an individual or Legal Entity authorized to submit on behalf of
      the copyright owner. For the purposes of this definition, "submitted"
      means any form of electronic, verbal, or written communication sent
      to the Licensor or its representatives, including but not limited to
      communication on electronic mailing lists, source code control systems,
      and issue tracking systems that are managed by, or on behalf of, the
      Licensor for the purpose of discussing and improving the Work, but
      excluding communication that is conspicuously marked or otherwise
      designated in writing by the copyright owner as "Not a Contribution."

      "Contributor" shall mean Licensor and any individual or Legal Entity
      on behalf of whom a Contribution has been received by Licensor and
      subsequently incorporated within the Work.

   2. Grant of Copyright License. Subject to the terms and conditions of
      this License, each Contributor hereby grants to You a perpetual,
      worldwide, non-exclusive, no-charge, royalty-free, irrevocable
      copyright license to reproduce, prepare Derivative Works of,
      publicly display, publicly perform, sublicense, and distribute the
      Work and such Derivative Works in Source or Object form.

   3. Grant of Patent License. Subject to the terms and conditions of
      this License, each Contributor hereby grants to You a perpetual,
      worldwide, non-exclusive, no-charge, royalty-free, irrevocable
      (except as stated in this section) patent license to make, have made,
      use, offer to sell, sell, import, and otherwise transfer the Work,
      where such license applies only to those patent claims licensable
      by such Contributor that are necessarily infringed by their
      Contribution(s) alone or by combination of their Contribution(s)
      with the Work to which such Contribution(s) was submitted. If You
      institute patent litigation against any entity (including a
      cross-claim or counterclaim in a lawsuit) alleging that the Work
      or a Contribution incorporated within the Work constitutes direct
      or contributory patent infringement, then any patent licenses
      granted to You under this License for that Work shall terminate
      as of the date such litigation is filed.

   4. Redistribution. You may reproduce and distribute copies of the
      Work or Derivative Works thereof in any medium, with or without
      modifications, and in Source or Object form, provided that You
      meet the following conditions:

      (a) You must give any other recipients of the Work or
          Derivative Works a copy of this License; and

      (b) You must cause any modified files to carry prominent notices
          stating that You changed the files; and

      (c) You must retain, in the Source form of any Derivative Works
          that You distribute, all copyright, patent, trademark, and
          attribution notices from the Source form of the Work,
          excluding those notices that do not pertain to any part of
          the Derivative Works; and

      (d) If the Work includes a "NOTICE" text file as part of its
          distribution, then any Derivative Works that You distribute must
          include a readable copy of the attribution notices contained
          within such NOTICE file, excluding those notices that do not
          pertain to any part of the Derivative Works, in at least one
          of the following places: within a NOTICE text file distributed
          as part of the Derivative Works; within the Source form or
          documentation, if provided along with the Derivative Works; or,
          within a display generated by the Derivative Works, if and
          wherever such third-party notices normally appear. The contents
          of the NOTICE file are for informational purposes only and
          do not modify the License. You may add Your own attribution
          notices within Derivative Works that You distribute, alongside
          or as an addendum to the NOTICE text from the Work, provided
          that such additional attribution notices cannot be construed
          as modifying the License.

      You may add Your own copyright statement to Your modifications and
      may provide additional or different license terms and conditions
      for use, reproduction, or distribution of Your modifications, or
      for any such Derivative Works as a whole, provided Your use,
      reproduction, and distribution of the Work otherwise complies with
      the conditions stated in this License.

   5. Submission of Contributions. Unless You explicitly state otherwise,
      any Contribution intentionally submitted for inclusion in the Work
      by You to the Licensor shall be under the terms and conditions of
      this License, without any additional terms or conditions.
      Notwithstanding the above, nothing herein shall supersede or modify
      the terms of any separate license agreement you may have executed
      with Licensor regarding such Contributions.

   6. Trademarks. This License does not grant permission to use the trade
      names, trademarks, service marks, or product names of the Licensor,
      except as required for reasonable and customary use in describing the
      origin of the Work and reproducing the content of the NOTICE file.

   7. Disclaimer of Warranty. Unless required by applicable law or
      agreed to in writing, Licensor provides the Work (and each
      Contributor provides its Contributions) on an "AS IS" BASIS,
      WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or
      implied, including, without limitation, any warranties or conditions
      of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A
      PARTICULAR PURPOSE. You are solely responsible for determining the
      appropriateness of using or redistributing the Work and assume any
      risks associated with Your exercise of permissions under this License.

   8. Limitation of Liability. In no event and under no legal theory,
      whether in tort (including negligence), contract, or otherwise,
      unless required by applicable law (such as deliberate and grossly
      negligent acts) or agreed to in writing, shall any Contributor be
      liable to You for damages, including any direct, indirect, special,
      incidental, or consequential damages of any character arising as a
      result of this License or out of the use or inability to use the
      Work (including but not limited to damages for loss of goodwill,
      work stoppage, computer failure or malfunction, or any and all
      other commercial damages or losses), even if such Contributor
      has been advised of the possibility of such damages.

   9. Accepting Warranty or Additional Liability. While redistributing
      the Work or Derivative Works thereof, You may choose to offer,
      and charge a fee for, acceptance of support, warranty, indemnity,
      or other liability obligations and/or rights consistent with this
      License. However, in accepting such obligations, You may act only
      on Your own behalf and on Your sole responsibility, not on behalf
      of any other Contributor, and only if You agree to indemnify,
      defend, and hold each Contributor harmless for any liability
      incurred by, or claims asserted against, such Contributor by reason
      of your accepting any such warranty or additional liability.

   END OF TERMS AND CONDITIONS

   APPENDIX: How to apply the Apache License to your work.

      To apply the Apache License to your work, attach the following
      boilerplate notice, with the fields enclosed by brackets "[]"
      replaced with your own identifying information. (Don't include
      the brackets!)  The text should be enclosed in the appropriate
      comment syntax for the file format. We also recommend that a
      file or class name and description of purpose be included on the
      same "printed page" as the copyright notice for easier
      identification within third-party archives.

   Copyright 2026 Elasticsearch BV

   Licensed under the Apache License, Version 2.0 (the "License");
   you may not use this file except in compliance with the License.
   You may obtain a copy of the License at

       http://www.apache.org/licenses/LICENSE-2.0

   Unless required by applicable law or agreed to in writing, software
   distributed under the License is distributed on an "AS IS" BASIS,
   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   See the License for the specific language governing permissions and
   limitations under the License.

Requirements and compatibility

Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta

“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.

The author says: Elasticsearch 8.10 or later (query rules), self-managed, Elastic Cloud Hosted, or Elastic Cloud Serverless. Requires the `elastic` CLI ≥ 0.2 with `stack es` support.

Security

No issues found by our static scan· No issues found

A static scan is not a guarantee. Skills are third-party content: review a skill before you let an agent use it.

Attribution and licence

Created by
elastic
Publisher
Elastic (Official)
Source
elastic/agent-skills · skills/elasticsearch/elasticsearch-search-relevance · commit 09d879d
Licence
Apache License 2.0 (redistribution allowed)
  • LICENSE (added by AutoGineer from LICENSE)
  • ATTRIBUTION.md (written by AutoGineer)
Synced
Also distributed in
elastic/agent-skills

AutoGineer is not affiliated with or endorsed by elastic or Elastic.

Download ZIP

The ZIP holds the skill’s files, its licence files and an ATTRIBUTION.md. Download elasticsearch-search-relevance.zip