Fundamental Library for Angular is an SAP-maintained Angular component library implementing SAP's design system and wrapping UI5 Web Components. Angular developers use its typed UI components and higher-level composites to build SAP-style web interfaces. Catalogue entries provide skills, agents, instructions, and a rule for working with the library.
Borrowing it
Nothing to install: this file belongs to SAP/fundamental-ngx. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/SAP/fundamental-ngx/main/.claude/skills/build-table/SKILL.mdgit clone --depth 1 https://github.com/SAP/fundamental-ngxWrote 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/sap/fundamental-ngx/build-table)<a href="https://agentmods.dev/skills/sap/fundamental-ngx/build-table"><img src="https://agentmods.dev/badge/skills/sap/fundamental-ngx/build-table/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/sap/fundamental-ngx/build-table"><img src="https://agentmods.dev/badge/skills/sap/fundamental-ngx/build-table.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Prompt Injection · line 159 Large whitespace padding was detected (a block of blank lines or a long run of spaces). This can push injected instructions below or to the right of the visible area so a human reviewer never sees them while the agent still reads them. Manual review of the hidden content is recommended.Fix: Remove the large whitespace padding (blank-line blocks or long space runs) and review any content hidden below or to the right of it. Keep skill files compact and reviewable so no instructions can be
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 | $0.00029 | $0.02570 |
| Opus 5 | $0.00015 | $0.01285 |
| Sonnet 5 | $0.00006 | $0.00514 |
| Haiku 4.5 | $0.00003 | $0.00257 |
Grade A, and why
build-table scanned grade A with 1 finding 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
| HTTP (lazy, server-side sort/filter) | Implement `TableDataSource<T>` interface — override `fetch(tableState)` | How it starts
The opening of the file, as written. The whole thing — 227 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Build Table: $ARGUMENTS
If $ARGUMENTS is empty, ask: (1) what data the table displays, (2) which features are needed.
Phase 1: Determine Scope
Parse from $ARGUMENTS or ask:
- Table name (PascalCase, e.g.,
Users,Orders) - Data model: field names + TypeScript types (used for column definitions and the data interface). If not provided in
$ARGUMENTS, ask explicitly: "What columns should the table have? Please list field name and type, e.g.:id:number, customerName:string, status:string" - Base component:
fdp-table(platform, feature-rich — default) |fd-table(core, markup-level, use only for purely presentational tables) - Features (check all that apply):
sort— sortable column headersfilter— per-column or global search filterpaginate— page size picker + page navigationselect— row selection (single|multiple)toolbar— title bar with action buttons
- Data source: static array | observable | HTTP service (lazy)
Default to fdp-table with sort + paginate unless the user specifies otherwise.
Phase 2: Gather Component Context
Call the @fundamental-ngx/mcp MCP server:
get_usage_guide('table')— DataSource patterns, variant decision tree, pitfallsget_component_api('fdp-table')— all inputs/outputs, selection mode options, page size inputs- If
filterrequested:get_component_api('fdp-table-toolbar')for filter toolbar wiring - If custom cell rendering needed: look for
fdpTableCelltemplate directive API
If MCP is unavailable, read libs/mcp-server/src/data/usage-guides.ts.
Phase 3: Present Plan
Output this summary before writing any code:
## Table Plan: [TableName]
**Component:** fdp-table
**DataSource type:** FdpTableDataSource (wraps observable)
**Features:** sort ✓ | filter ✓ | paginate ✓ | select(multiple) ✓
| Column key | Label | Type | Sortable | Filterable | Width |
|------------|-------|------|----------|------------|-------|
| name | Name | string | yes | yes | auto |
| status | Status | string | yes | yes | 120px |
| createdAt | Created | date | yes | no | 160px |
**Toolbar actions:** Add, Delete selected
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 · 227 lines · 29 tokens per session scan A c12d3a122a77
build-table is a skill published in the GitHub repository SAP/fundamental-ngx (294 stars, last pushed 4d ago), licensed Apache-2.0. It adds 29 tokens to every session and 2,570 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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