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 agentmods add skills/jimmc414/claude-code-plugin-marketplace/parse-extract-inputnpx skills add jimmc414/claude-code-plugin-marketplace --skill parse-extract-inputgit clone --depth 1 https://github.com/jimmc414/claude-code-plugin-marketplaceWrote 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/jimmc414/claude-code-plugin-marketplace/parse-extract-input)<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>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 | $0.00026 | $0.00722 |
| Opus 5 | $0.00013 | $0.00361 |
| Sonnet 5 | $0.00005 | $0.00144 |
| Haiku 4.5 | $0.00003 | $0.00072 |
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.
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')
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.
- 5d ago First seen · 95 lines · 26 tokens per session scan A 7a365a3d3bdf
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.
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.
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".
ax-python-llm
Use when writing Python code with axllm for using the generated Ax package, factory functions, package docs, examples, and API reference.
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.
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'.
ax-python-gepa
Use when writing Python code with axllm for GEPA, Pareto tradeoffs, reflection clients, metric budgets, optimizer state, and artifacts.