parse-extract-input

parse-extract-input is a skill for Claude Code, Codex from jimmc414/claude-code-plugin-marketplace. It costs 26 tokens per session (722 once invoked), scanned A, original, MIT.

A small set of Python patterns for pulling numbers, words, identifiers, or other simple values from messy text. It uses regular expressions and helper functions to turn text into structured data.

In plain words
What is it for?
Extracting numbers or words, parsing simple input files, cleaning text, and reading programming-puzzle input.
Why use it?
It avoids repeatedly writing and debugging text-matching code when input is not neatly formatted. It also clarifies when to use a JSON or CSV parser, a proper grammar parser, or a simple split instead.

Skill for Claude CodeCodex

Part of the norvig-patterns plugin — 54 skills shipped together

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.

agentmods
npx agentmods add skills/jimmc414/claude-code-plugin-marketplace/parse-extract-input
Any agent
npx skills add jimmc414/claude-code-plugin-marketplace --skill parse-extract-input
Clone the repo
git clone --depth 1 https://github.com/jimmc414/claude-code-plugin-marketplace

Made for: Claude Code, Codex.

Or install norvig-patterns, the plugin that ships this one along with the rest of its 54 skills.

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 parse-extract-input

README.md
[![agentmods](https://agentmods.dev/badge/skills/jimmc414/claude-code-plugin-marketplace/parse-extract-input.svg)](https://agentmods.dev/skills/jimmc414/claude-code-plugin-marketplace/parse-extract-input)
Your own site
<a href="https://agentmods.dev/skills/jimmc414/claude-code-plugin-marketplace/parse-extract-input"><img src="https://agentmods.dev/badge/skills/jimmc414/claude-code-plugin-marketplace/parse-extract-input.svg" alt="Measured on agentmods" height="20"></a>
Per session 26 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 722 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
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 $0.00026 $0.00722
Opus 5 $0.00013 $0.00361
Sonnet 5 $0.00005 $0.00144
Haiku 4.5 $0.00003 $0.00072

Measured 5d ago against content hash 7a365a3d3bdf, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

parse-extract-input 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 5d 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.

plugins/norvig-patterns/skills/parse-extract-input/SKILL.md · 95 lines

How it starts

The opening of the file, as written. The whole thing — 95 lines — stays where its author put it; the contents beside it link to each section on GitHub.

parse-extract-input

When to Use

  • Extracting numbers from text
  • Finding words or identifiers
  • Parsing structured input files
  • Data cleaning
  • Advent of Code input parsing

When NOT to Use

  • Structured format (use JSON/CSV parsers)
  • Complex grammar (use proper parser)
  • Simple split is enough

The Pattern

Use regex or helper functions to extract structured data from text.

import re

def ints(text):
    """Extract all integers from text."""
    return tuple(map(int, re.findall(r'-?[0-9]+', text)))

def words(text):
    """Extract all words from text."""
    return tuple(re.findall(r'[a-zA-Z]+', text))

def atoms(text):
    """Extract all atoms (numbers or identifiers)."""
    return tuple(atom(s) for s in re.findall(r'[+-]?\d+\.?\d*|\w+', text))

def atom(s):
    """Parse string as number or keep as string."""
    try:
        return int(s)
    except ValueError:
        try:
            return float(s)
        except ValueError:
            return s

Example (from pytudes AdventUtils.ipynb)

import re

def ints(text: str) -> Tuple[int, ...]:
    """A tuple of all the integers in text."""
    return tuple(map(int, re.findall(r'-?[0-9]+', text)))

def positive_ints(text: str) -> Tuple[int, ...]:
    """A tuple of all positive integers in text."""
    return tuple(map(int, re.findall(r'[0-9]+', text)))

def digits(text: str) -> Tuple[int, ...]:
    """A tuple of all single digits in text."""
    return tuple(map(int, re.findall(r'[0-9]', text)))

def words(text: str) -> Tuple[str, ...]:
    """A tuple of all alphabetic words in text."""
    return tuple(re.findall(r'[a-zA-Z]+', text))

def atoms(text: str) -> Tuple:
    """A tuple of all atoms (numbers or identifiers)."""
    return tuple(map(atom, re.findall(r'[+-]?\d+\.?\d*|\w+', text)))

def atom(text: str):
    """Parse text into a single float or int or str."""
    try:
        x = float(text)
        return round(x) if x.is_integer() else x
    except ValueError:
        return text.strip()

# Usage examples
ints("Robot at (3, -5) with speed 10")  # (3, -5, 10)
words("Hello, World! 123")  # ('Hello', 'World')
atoms("x=42, y=3.14, name=foo")  # ('x', 42, 'y', 3.14, 'name', 'foo')

Read the full file on GitHub · 95 lines

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. 5d ago First seen · 95 lines · 26 tokens per session scan A 7a365a3d3bdf

Subscribe to this mod's changes

parse-extract-input is a skill published in the GitHub repository jimmc414/claude-code-plugin-marketplace (4 stars, last pushed yesterday), licensed MIT. It adds 26 tokens to every session and 722 once invoked, about $0.0001 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 2026-08-31.

Related

Other skills, from other repositories

sensitive-logging-audit

Audit and fix sensitive-data exposure through Python runtime logging in openai-agents-python. Use when reviewing logging, print, warnings, stderr, traceback, MCP names, model or tool exceptions, redaction flags, or any diagnostic path that may retain user data.

openai/openai-agents-python · 59 tokens

azure-mgmt-fabric-py

Azure Fabric Management SDK for Python. Use for managing Microsoft Fabric capacities and resources. Triggers: "azure-mgmt-fabric", "FabricMgmtClient", "Fabric capacity", "Microsoft Fabric", "Power BI capacity".

microsoft/skills · 51 tokens

ax-python-llm

Use when writing Python code with axllm for using the generated Ax package, factory functions, package docs, examples, and API reference.

ax-llm/ax · 36 tokens

ai-ml-development

AI and machine learning development with PyTorch, TensorFlow, and LLM integration. Use when building ML models, training pipelines, fine-tuning LLMs, or implementing AI features.

travisjneuman/.claude · 43 tokens

generate

Build a source-backed AI industry briefing from official vendor publications, configured RSS feeds, GitHub releases, reputable secondary reporting, and user-supplied URLs. Use when: 'ai briefing', 'ai news', 'what's new in AI', 'catch me up on AI', 'prep for AI meeting', 'AI roundup', or 'generate AI slides'.

melodic-software/claude-code-plugins · 72 tokens

ax-python-gepa

Use when writing Python code with axllm for GEPA, Pareto tradeoffs, reflection clients, metric budgets, optimizer state, and artifacts.

ax-llm/ax · 37 tokens