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 MarioDeFelipe/sap-datasphere-plugin-for-claude-cowork --skill datasphere-intelligent-lookupgit clone --depth 1 https://github.com/MarioDeFelipe/sap-datasphere-plugin-for-claude-coworkWrote 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/mariodefelipe/sap-datasphere-plugin-for-claude-cowork/datasphere-intelligent-lookup)<a href="https://agentmods.dev/skills/mariodefelipe/sap-datasphere-plugin-for-claude-cowork/datasphere-intelligent-lookup"><img src="https://agentmods.dev/badge/skills/mariodefelipe/sap-datasphere-plugin-for-claude-cowork/datasphere-intelligent-lookup/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/mariodefelipe/sap-datasphere-plugin-for-claude-cowork/datasphere-intelligent-lookup"><img src="https://agentmods.dev/badge/skills/mariodefelipe/sap-datasphere-plugin-for-claude-cowork/datasphere-intelligent-lookup.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 | $0.00068 | $0.06112 |
| Opus 5 | $0.00034 | $0.03056 |
| Sonnet 5 | $0.00014 | $0.01222 |
| Haiku 4.5 | $0.00007 | $0.00611 |
Grade A, and why
Intelligent Lookup Wizard 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 12d 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 — 783 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Intelligent Lookup Wizard Skill
Overview
The Intelligent Lookup Wizard guides you through creating intelligent lookups in SAP Datasphere. Intelligent lookups use fuzzy matching algorithms to find similar records across datasets, enabling data harmonization, deduplication, and master data management without requiring exact matches.
What Are Intelligent Lookups?
Definition
An intelligent lookup is a data matching tool that:
- Compares text values using fuzzy matching algorithms
- Finds similar records across two datasets (input and lookup entities)
- Assigns match scores indicating confidence level
- Enables data enrichment, harmonization, and deduplication
- Includes review workflow for manual confirmation of matches
When to Use Intelligent Lookups
Use intelligent lookups for:
- Company name matching — "ACME Corp", "Acme Corporation", "ACME Inc" → Match
- Address reconciliation — Handling abbreviations, spelling variations, format differences
- Product description matching — "Widget Pro" vs "Professional Widget" → Same product
- Customer deduplication — Finding duplicate customer records within dataset
- Master data harmonization — Matching vendor IDs across multiple procurement systems
- Data quality improvement — Identifying and merging incomplete/duplicate records
- Cross-system reconciliation — Matching accounts between ERP and CRM systems
DO NOT use intelligent lookups for:
- Exact match lookups (use standard SQL join)
- Numeric lookups (use hash or checksum matching)
- High-speed real-time lookups (use reference tables/caches)
- Data that must match perfectly (use data quality tools instead)
When Intelligent Lookups Add Value
Scenario 1: Company Name Matching
Input data has: "ACME Corporations Inc"
Lookup table has: "Acme Corp"
Standard lookup: NO MATCH ✗
Intelligent lookup: MATCH ✓ (score: 0.92)
Scenario 2: Product Matching
Input: "Professional Grade Widget with Advanced Features"
Lookup: "Widget Pro - Advanced"
Standard lookup: NO MATCH ✗
Intelligent lookup: MATCH ✓ (score: 0.85)
Scenario 3: Address Matching
Input: "123 Main St, New York, NY 10001"
Lookup: "123 Main Street, New York, New York 10001"
Standard lookup: NO MATCH ✗
Intelligent lookup: MATCH ✓ (score: 0.95)
What ships with it
1 file 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.
- 12d ago First seen · 783 lines · 68 tokens per session scan A 1cfc815d953d
Intelligent Lookup Wizard is a skill published in the GitHub repository MarioDeFelipe/sap-datasphere-plugin-for-claude-cowork (26 stars, last pushed 4mo ago), licensed MIT. It adds 68 tokens to every session and 6,112 once invoked, about $0.0003 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.
Other skills, from other repositories
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
local-ai-agents
Build local-first AI agents that run entirely on a developer workstation with Microsoft Foundry Local and Qwen function-calling models. Covers Small Language Models (SLMs), the OpenAI-compatible local endpoint, sandboxed local tools, local RAG with Chroma, local MCP servers, hybrid cloud/local routing, and the…
next-cache-components-adoption
Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…
chat-pet-sprite-creation
Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.
cpu-profile-analysis
Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…
insight-error-page
Write or audit an insight-kind error page for the Next.js dev overlay. Use when creating a new errors/ .mdx page, auditing an existing one, or checking that a page matches the framework fix cards. Covers page structure, title alignment, FixCard cards with Copy prompt button, code snippets, terminology verification…