opensearch-function-scoring-algorithms

opensearch-function-scoring-algorithms is a skill for Claude Code, Codex from pproenca/dot-skills. It costs 224 tokens per session (3,611 once invoked), scanned A, original, MIT.

A guide to improving search-result ranking in OpenSearch or Elasticsearch for two-sided marketplaces, where a search engine chooses and orders listings such as homes, products, or services.

In plain words
What is it for?
It is for building or tuning marketplace search, combining keyword and vector search, choosing ranking algorithms, adding personalisation, and evaluating search quality.
Why use it?
It helps search teams balance text relevance, meaning-based matches, quality signals, and personalisation while reducing ranking bias.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one. Also seen: mentions AGENTS.md.

Good fit It is for building or tuning marketplace search, combining keyword and vector search, choosing ranking algorithms, adding personalisation, and evaluating search quality.

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Install with agentmods
npx agentmods add skills/pproenca/dot-skills/opensearch-function-scoring-algorithms
Install

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.

Any agent
npx skills add pproenca/dot-skills --skill opensearch-function-scoring-algorithms
Clone the repo
git clone --depth 1 https://github.com/pproenca/dot-skills

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for opensearch-function-scoring-algorithms

README.md
[![agentmods](https://agentmods.dev/badge/skills/pproenca/dot-skills/opensearch-function-scoring-algorithms/github.svg)](https://agentmods.dev/skills/pproenca/dot-skills/opensearch-function-scoring-algorithms)
Your own site
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Your own site · 80×15
<a href="https://agentmods.dev/skills/pproenca/dot-skills/opensearch-function-scoring-algorithms"><img src="https://agentmods.dev/badge/skills/pproenca/dot-skills/opensearch-function-scoring-algorithms.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 224 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 3,611 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00224 $0.03611
Opus 5 $0.00112 $0.01806
Sonnet 5 $0.00045 $0.00722
Haiku 4.5 $0.00022 $0.00361

Measured 6d ago against content hash f3e421f956a4, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-09, from the pricing page.

Security

Grade A, and why

opensearch-function-scoring-algorithms 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 6d 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.

skills/.experimental/opensearch-function-scoring-algorithms/SKILL.md · 167 lines

How it starts

The opening of the file, as written. The whole thing — 167 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Marketplace-Research OpenSearch Function Scoring Best Practices

A reference distillation of research-backed algorithms for ranking in two-sided marketplaces (Airbnb, Uber Eats, DoorDash, Etsy, eBay, Booking.com) implemented on OpenSearch or Elasticsearch. Contains 56 rules across 9 categories, prioritised by cascade effect in the search ranking pipeline. Each rule explains the WHY (the cascade or the bias it corrects), shows incorrect-vs-correct code (OpenSearch JSON queries, Painless scripts, Python pre-processing, evaluation methodology), and links to the canonical source — KDD/SIGIR/WSDM papers, the OpenSearch documentation, and the engineering blogs of the marketplaces that proved these patterns at scale.

When to Apply

Reach for this skill when:

  • Designing a new marketplace search system on OpenSearch or Elasticsearch from scratch
  • Tuning function_score / rank_feature / script_score queries that aren't moving the needle
  • Setting up hybrid retrieval (BM25 + dense vectors) with Reciprocal Rank Fusion
  • Choosing between HNSW and IVF for billion-scale ANN indexes
  • Adding personalization via listing/user embeddings or two-tower architectures
  • Correcting position bias in click logs before retraining an LTR model
  • Designing exposure-fairness or new-listing cold-start exposure allocation
  • Composing decay functions (gauss / exp / linear) over geo + date + freshness
  • Diversifying the top window with MMR, DPP, or per-host caps
  • Debugging "why does my top-10 show 8 listings from one host?" or "why does ranking favor popular incumbents?"
  • Building offline evaluation infrastructure — graded judgment sets, NDCG@k pipelines, ablation studies, regression query suites
  • Designing A/B tests for ranking changes — MDE / power / sample-size pre-computation, CUPED variance reduction, online-offline correlation calibration
  • Attributing lift to specific scoring components — "did my new bias-correction help, or was it the embeddings, or both?"

The rules apply to any OpenSearch/Elasticsearch-backed marketplace search regardless of vertical — accommodation, food delivery, restaurants, services, jobs, secondhand goods, real estate. Triggers include "marketplace ranking", "search relevance", "function_score", "rank_feature", "script_score", "kNN", "hybrid search", "RRF", "learning to rank", "embedding-based retrieval", "two-tower", "position bias", "MMR", "supply fairness", "Pareto multi-objective", "NDCG", "judgment set", "ablation study", "CUPED", "A/B sample size", "ranking eval", and "why are my search results bad".

Read the full file on GitHub · 167 lines

Files

What ships with it

60 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.

Changes

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.

  1. 6d ago First seen · 167 lines · 224 tokens per session scan A f3e421f956a4

Subscribe to this mod's changes

opensearch-function-scoring-algorithms is a skill published in the GitHub repository pproenca/dot-skills (205 stars, last pushed 24d ago), licensed MIT. It adds 224 tokens to every session and 3,611 once invoked, about $0.0011 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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