Agent skill

Fp Data Transforms

by sickn33 in sickn33/agentic-awesome-skills

Everyday data transformations using functional patterns - arrays, objects, grouping, aggregation, and null-safe access

MITAuto-check passedData & Analytics

Install Fp Data Transforms

skills CLI
$ npx skills add sickn33/agentic-awesome-skills --skill fp-data-transforms -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install sickn33/agentic-awesome-skills fp-data-transforms --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/sickn33/agentic-awesome-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/fp-data-transforms .claude/skills/fp-data-transforms && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
fp-data-transforms
GitHub stars
47k
Used in
2 other repos
Token cost
~2.4k tokens
SKILL.md length
354 words
Files
2 (incl. references)
Skills in repo
1,354
Repo updated
First seen
Licence
MIT

At a glance

Everyday data transformations using functional patterns - arrays, objects, grouping, aggregation, and null-safe access

  • Works in 2 steps: Real-World Examples → When to Use What
  • Tasks that involve Data cleaning
  • SKILL.md covers Detailed Guide, When to Use, 6. Real-World Examples and 7. When to Use What, plus 1 more section
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Fp Data Transforms is an agent skill from sickn33/agentic-awesome-skills. Everyday data transformations using functional patterns - arrays, objects, grouping, aggregation, and null-safe access

Its SKILL.md is about 2.4k tokens, which your agent loads only when the skill is triggered. The skill folder holds 2 other files, including reference files (for example `references/detailed-guide.md`).

It sits in Data & Analytics, covering Data cleaning. The repository describes itself as: AAS Core is the local, agent-first control plane for complete catalog discovery, agent-owned selection, stack validation, and planning, backed by 2,400+ agentic skills. Includes… The licence is MIT.

When your agent uses it

  • Tasks that involve Data cleaning

Example prompts

  • “/fp-data-transforms”

Workflow steps

2 steps, taken from the step headings in SKILL.md.

  1. Real-World Examples
  2. When to Use What

What it can do on your machine

Read from SKILL.md and the folder at commit ec02547. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md (its code samples are typescript).

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Fp Data Transforms loads about 2.4k tokens when it runs, and up to ~9.4k if it reads all its reference files. Until then it costs about 34 tokens; SKILL.md has 354 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~34
When it runs · the whole SKILL.md, loaded when a task matches
~2.4k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~9.4k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from sickn33/agentic-awesome-skills at commit ec02547, republished under its MIT licence (© sickn33). 354 words, ~2,398 tokens.

Download SKILL.mdSave it as .claude/skills/fp-data-transforms/SKILL.md (or your agent's skills folder). This skill also uses 1 other file; get the full folder from GitHub.
name
fp-data-transforms
description
Everyday data transformations using functional patterns - arrays, objects, grouping, aggregation, and null-safe access
risk
critical
source
community
date_added
2026-09-04
version
1.0.0
author
Claude
tags
functional-programming, typescript, data-transformation, fp-ts, arrays, objects, grouping, aggregation, null-safety

Practical Data Transformations

This skill covers the data transformations you do every day: working with arrays, reshaping objects, normalizing API responses, grouping data, and safely accessing nested values. Each section shows the imperative approach first, then the functional equivalent, with honest assessments of when each approach shines.

Detailed Guide

Read the detailed guide before executing this skill. It retains the complete procedure and reference material. Treat its safety, prerequisites, and validation requirements as mandatory. For focused work, load the relevant sections; for end-to-end work, read the guide completely.

When to Use

  • You need to transform arrays, objects, grouped data, or nested values in TypeScript.
  • The task involves reshaping API responses, null-safe access, aggregation, or normalization.
  • You want practical functional patterns for everyday data work instead of low-level loops.

6. Real-World Examples

Example 1: Transform API Response to UI-Ready Data
typescript
// API response
interface ApiOrder {
  order_id: string;
  customer: {
    id: string;
    full_name: string;
  };
  line_items: Array<{
    product_id: string;
    product_name: string;
    qty: number;
    unit_price: number;
  }>;
  order_date: string;
  status: 'pending' | 'processing' | 'shipped' | 'delivered';
}

// What the UI needs
interface OrderSummary {
  id: string;
  customerName: string;
  itemCount: number;
  total: number;
  formattedTotal: string;
  date: string;
  statusLabel: string;
  statusColor: string;
}

// Transformation
const STATUS_CONFIG: Record<string, { label: string; color: string }> = {
  pending: { label: 'Pending', color: 'yellow' },
  processing: { label: 'Processing', color: 'blue' },
  shipped: { label: 'Shipped', color: 'purple' },
  delivered: { label: 'Delivered', color: 'green' },
};

const formatCurrency = (cents: number): string =>
  `$${(cents / 100).toFixed(2)}`;

const formatDate = (iso: string): string =>
  new Date(iso).toLocaleDateString('en-US', {
    month: 'short',
    day: 'numeric',
    year: 'numeric',
  });

const toOrderSummary = (order: ApiOrder): OrderSummary => {
  const total = order.line_items.reduce(
    (sum, item) => sum + item.qty * item.unit_price,
    0
  );

  const status = STATUS_CONFIG[order.status] ?? STATUS_CONFIG.pending;

  return {
    id: order.order_id,
    customerName: order.customer.full_name,
    itemCount: order.line_items.reduce((sum, item) => sum + item.qty, 0),
    total,
    formattedTotal: formatCurrency(total),
    date: formatDate(order.order_date),
    statusLabel: status.label,
    statusColor: status.color,
  };
};

// Transform all orders
const toOrderSummaries = (orders: ApiOrder[]): OrderSummary[] =>
  orders.map(toOrderSummary);
Example 2: Merge User Settings with Defaults
typescript
interface AppSettings {
  theme: {
    mode: 'light' | 'dark' | 'system';
    primaryColor: string;
    fontSize: 'small' | 'medium' | 'large';
  };
  notifications: {
    email: boolean;
    push: boolean;
    sms: boolean;
    frequency: 'immediate' | 'daily' | 'weekly';
  };
  privacy: {
    showProfile: boolean;
    showActivity: boolean;
    allowAnalytics: boolean;
  };
}

type DeepPartial<T> = {
  [P in keyof T]?: T[P] extends object ? DeepPartial<T[P]> : T[P];
};

const DEFAULT_SETTINGS: AppSettings = {
  theme: {
    mode: 'system',
    primaryColor: '#007bff',
    fontSize: 'medium',
  },
  notifications: {
    email: true,
    push: true,
    sms: false,
    frequency: 'immediate',
  },
  privacy: {
    showProfile: true,
    showActivity: true,
    allowAnalytics: true,
  },
};

const deepMergeSettings = (
  defaults: AppSettings,
  user: DeepPartial<AppSettings>
): AppSettings => ({
  theme: { ...defaults.theme, ...user.theme },
  notifications: { ...defaults.notifications, ...user.notifications },
  privacy: { ...defaults.privacy, ...user.privacy },
});

// Usage
const userPreferences: DeepPartial<AppSettings> = {
  theme: { mode: 'dark' },
  notifications: { sms: true, frequency: 'daily' },
};

const finalSettings = deepMergeSettings(DEFAULT_SETTINGS, userPreferences);
Example 3: Group Orders by Customer with Totals
typescript
interface Order {
  id: string;
  customerId: string;
  customerName: string;
  items: Array<{ name: string; price: number; quantity: number }>;
  date: string;
}

interface CustomerOrderSummary {
  customerId: string;
  customerName: string;
  orderCount: number;
  totalSpent: number;
  orders: Order[];
}

const calculateOrderTotal = (order: Order): number =>
  order.items.reduce((sum, item) => sum + item.price * item.quantity, 0);

const groupOrdersByCustomer = (orders: Order[]): CustomerOrderSummary[] => {
  const grouped = groupBy((order: Order) => order.customerId)(orders);

  return Object.entries(grouped).map(([customerId, customerOrders]) => ({
    customerId,
    customerName: customerOrders[0].customerName,
    orderCount: customerOrders.length,
    totalSpent: customerOrders.reduce(
      (sum, order) => sum + calculateOrderTotal(order),
      0
    ),
    orders: customerOrders,
  }));
};
Example 4: Safely Access Deeply Nested Config
typescript
interface AppConfig {
  services?: {
    api?: {
      endpoints?: {
        users?: string;
        orders?: string;
        products?: string;
      };
      auth?: {
        type?: 'bearer' | 'basic' | 'oauth';
        token?: string;
      };
    };
    database?: {
      primary?: {
        host?: string;
        port?: number;
        name?: string;
      };
    };
  };
}

import * as O from 'fp-ts/Option';
import { pipe } from 'fp-ts/function';

// Create a type-safe config accessor
const getConfigValue = <T>(
  config: AppConfig,
  path: (config: AppConfig) => T | undefined,
  defaultValue: T
): T => path(config) ?? defaultValue;

// Usage with optional chaining (simplest)
const apiUsersEndpoint = getConfigValue(
  config,
  c => c.services?.api?.endpoints?.users,
  '/api/users'
);

// For more complex scenarios, use Option
const getEndpoint = (config: AppConfig, name: 'users' | 'orders' | 'products'): string =>
  pipe(
    O.fromNullable(config.services),
    O.flatMap(s => O.fromNullable(s.api)),
    O.flatMap(a => O.fromNullable(a.endpoints)),
    O.flatMap(e => O.fromNullable(e[name])),
    O.getOrElse(() => `/api/${name}`)
  );

// Reusable pattern for multiple values
const getDbConfig = (config: AppConfig) => ({
  host: config.services?.database?.primary?.host ?? 'localhost',
  port: config.services?.database?.primary?.port ?? 5432,
  name: config.services?.database?.primary?.name ?? 'app',
});

7. When to Use What

Use Native Methods When:
  • Simple transformations: .map(), .filter(), .reduce() are perfectly good
  • No composition needed: You're doing a one-off transformation
  • Team familiarity: Everyone knows native methods
  • Optional chaining suffices: obj?.prop?.value ?? default handles your null-safety needs
typescript
// Native is fine here
const activeUserNames = users
  .filter(u => u.isActive)
  .map(u => u.name);
Show full SKILL.md (153 more words)Show less
Use fp-ts When:
  • Chaining operations that might fail: Multiple steps where each can return nothing
  • Composing transformations: Building reusable transformation pipelines
  • Type-safe error handling: You want the compiler to track potential failures
  • Complex data pipelines: Many steps that benefit from explicit composition
typescript
// fp-ts shines here
const result = pipe(
  users,
  A.findFirst(u => u.id === userId),
  O.flatMap(u => O.fromNullable(u.profile)),
  O.flatMap(p => O.fromNullable(p.settings)),
  O.map(s => s.theme),
  O.getOrElse(() => 'default')
);
Use Custom Utilities When:
  • Domain-specific operations: groupBy, countBy, sumBy for your data
  • Repeated patterns: You find yourself writing the same transformation many times
  • Team conventions: Establishing consistent patterns across the codebase
typescript
// Custom utility pays off when used repeatedly
const revenueByRegion = sumBy(
  (sale: Sale) => sale.region,
  (sale: Sale) => sale.amount
)(sales);
Performance Considerations
  • Chaining creates intermediate arrays: arr.filter().map() creates one array, then another
  • For hot paths, consider reduce: One pass through the data
  • Measure before optimizing: The readability cost of optimization is often not worth it
typescript
// If performance matters (and you've measured!)
const result = items.reduce((acc, item) => {
  if (item.isActive) {
    acc.push(item.name.toUpperCase());
  }
  return acc;
}, [] as string[]);

// vs the more readable (but 2-pass) version
const result = items
  .filter(item => item.isActive)
  .map(item => item.name.toUpperCase());

Limitations

  • Use this skill only when the task clearly matches the scope described above.
  • Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
  • Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.

© sickn33, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 1 other file (references) in skills/fp-data-transforms of sickn33/agentic-awesome-skills.

  • SKILL.md
  • references/detailed-guide.md

Open the folder on GitHubat commit ec02547

Used in 2 other repositories

We found 11 copies of this SKILL.md (exact, near-identical or edited) in other folders, from 2 other GitHub owners. This page covers the copy in sickn33/agentic-awesome-skills, which our catalogue first saw on October 7, 2026.

Compare with similar skills

Fp Data Transforms next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Fp Data Transforms compared with similar skills
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Fp Data Transforms this skillsickn33/agentic-awesome-skills47k2 repos~2.4kAutomated safety check: PassMIT
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Dingo VerifyMigoXLab/dingo757—~741Automated safety check: NotesApache-2.0
Data Validationplatonai/Browser41.2k—~896Automated safety check: PassApache-2.0
Issues DeduplicationJetBrains/ideavim10k—~1.3kAutomated safety check: PassMIT
Pandas ProJeffallan/claude-skills12k1 repos~1.5kAutomated safety check: PassMIT

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Questions about Fp Data Transforms

What does Fp Data Transforms do?

Everyday data transformations using functional patterns - arrays, objects, grouping, aggregation, and null-safe access. Fp Data Transforms is an agent skill from sickn33/agentic-awesome-skills.

When should I use Fp Data Transforms?

Fp Data Transforms fits situations like: tasks that involve Data cleaning.

How do I install Fp Data Transforms in Claude Code?

Run `npx skills add sickn33/agentic-awesome-skills --skill fp-data-transforms -a claude-code`. Or copy the skill folder (skills/fp-data-transforms in sickn33/agentic-awesome-skills) into .claude/skills/fp-data-transforms in your project. Claude Code loads it when a task matches its description.

How do I install Fp Data Transforms in Codex?

Run `npx skills add sickn33/agentic-awesome-skills --skill fp-data-transforms -a codex`. Or copy the skill folder (skills/fp-data-transforms in sickn33/agentic-awesome-skills) into .agents/skills/fp-data-transforms in your project. Codex loads it when a task matches its description.

Can I use Fp Data Transforms in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add sickn33/agentic-awesome-skills --skill fp-data-transforms -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/fp-data-transforms, .gemini/skills/fp-data-transforms, .github/skills/fp-data-transforms and .opencode/skills/fp-data-transforms in your project.

What does Fp Data Transforms need to run?

SKILL.md names no scripts, command-line tools or credentials: Fp Data Transforms is instructions for the agent only.

Does Fp Data Transforms access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Fp Data Transforms safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Fp Data Transforms use?

Fp Data Transforms is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Fp Data Transforms use?

About 2.4k tokens (SKILL.md is roughly 9.6k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 7k tokens, read only when the agent opens those files.

What are the alternatives to Fp Data Transforms?

Skills that share tags, products or a category with Fp Data Transforms: Question2report (refraction-ray/xalpha, 2.7k stars), Dingo Verify (MigoXLab/dingo, 757 stars), Data Validation (platonai/Browser4, 1.2k stars) and Issues Deduplication (JetBrains/ideavim, 10k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Fp Data Transforms?

sickn33 (a GitHub user) maintains it in sickn33/agentic-awesome-skills, which has 47,343 GitHub stars. The repository holds 1,354 skills in this directory. The repository was last updated on October 7, 2026.

Source: sickn33/agentic-awesome-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.