json-data-extraction

json-data-extraction is a skill for Claude Code, Codex from cxcscmu/SkillLearnBench. It costs 18 tokens per session (881 once invoked), scanned A, original, MIT.

A guide to reading, filtering, searching, and combining information stored in JSON files, a common text format for structured data.

In plain words
What is it for?
It is for extracting employee records, searching message histories, finding document links, and aggregating data from multiple JSON sources.
Why use it?
It removes the need to manually inspect large enterprise files when you need particular records or relationships.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit It is for extracting employee records, searching message histories, finding document links, and aggregating data from multiple JSON sources.

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Install with agentmods
npx agentmods add skills/cxcscmu/skilllearnbench/json-data-extraction
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.

Any agent
npx skills add cxcscmu/SkillLearnBench --skill json-data-extraction
Clone the repo
git clone --depth 1 https://github.com/cxcscmu/SkillLearnBench

Made for: Claude Code, Codex.

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 json-data-extraction

README.md
[![agentmods](https://agentmods.dev/badge/skills/cxcscmu/skilllearnbench/json-data-extraction/github.svg)](https://agentmods.dev/skills/cxcscmu/skilllearnbench/json-data-extraction)
Your own site
<a href="https://agentmods.dev/skills/cxcscmu/skilllearnbench/json-data-extraction"><img src="https://agentmods.dev/badge/skills/cxcscmu/skilllearnbench/json-data-extraction/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.

agentmods 80×15 button for json-data-extraction

Your own site · 80×15
<a href="https://agentmods.dev/skills/cxcscmu/skilllearnbench/json-data-extraction"><img src="https://agentmods.dev/badge/skills/cxcscmu/skilllearnbench/json-data-extraction.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 18 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 881 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
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 5.1 $0.00018 $0.00881
Opus 5.5 $0.00007 $0.00352
Sonnet 5.5 $0.00004 $0.00176
Haiku 4.5 $0.00002 $0.00088

Measured 1mo ago against content hash 3cf75bdfb7ba, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

json-data-extraction 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 1mo 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.

skills/b1-one-shot-claude-haiku-4-5/enterprise-information-search/json-data-extraction/SKILL.md · 121 lines

How it starts

The opening of the file, as written. The whole thing — 121 lines — stays where its author put it; the contents beside it link to each section on GitHub.

JSON Data Extraction from Enterprise Data

Overview

This skill covers parsing and extracting information from large JSON files containing enterprise data like employee records, Slack messages, and product metadata.

Use Cases

  • Extracting specific fields from employee databases
  • Searching through Slack message histories for mentions or keywords
  • Finding relationships between employees and documents/reports
  • Aggregating data across multiple JSON sources

Installation & Setup

# Python has built-in json module, no installation needed
python3 -c "import json; print('JSON module available')"

Code Examples

Basic JSON Loading

import json

with open('/root/DATA/metadata/employee.json', 'r') as f:
    employee_data = json.load(f)

# Access specific employee
employee = employee_data.get('eid_1e9356f5', {})

Extracting Data with Filters

import json

with open('/root/DATA/products/ContentForce.json', 'r') as f:
    product_data = json.load(f)

# Extract Slack messages mentioning specific employee
messages = product_data.get('slack', [])
for msg in messages:
    if 'Market Research Report' in msg.get('Message', {}).get('text', ''):
        print(msg)

Extracting IDs from Text

import re

def extract_employee_ids(text):
    """Extract employee IDs (format: eid_xxxxxxxx) from text"""
    pattern = r'eid_[a-f0-9]{8}'
    return re.findall(pattern, text)

# Usage
text = "@eid_1e9356f5 created this channel. @eid_06cddbb3 joined."
ids = extract_employee_ids(text)  # Returns ['eid_1e9356f5', 'eid_06cddbb3']

Finding Report Authors and Reviewers

import json
import re

def find_report_authors_and_reviewers(product_json_path, report_name):
    """Find employees who authored/reviewed a report"""
    with open(product_json_path, 'r') as f:
        data = json.load(f)

    authors = set()
    reviewers = set()

    messages = data.get('slack', [])
    for msg in messages:
        text = msg.get('Message', {}).get('text', '')
        if report_name.lower() in text.lower():
            # Author: person sharing the report
            author_id = msg.get('Message', {}).get('User', {}).get('userId')
            if author_id and author_id.startswith('eid_'):
                authors.add(author_id)

            # Reviewers: people responding in thread
            for reply in msg.get('ThreadReplies', []):
                reviewer_id = reply.get('User', {}).get('userId')
                if reviewer_id and reviewer_id.startswith('eid_'):
                    reviewers.add(reviewer_id)

    return list(authors), list(reviewers)

Read the full file on GitHub · 121 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. 1mo ago First seen · 121 lines · 18 tokens per session scan A 3cf75bdfb7ba

Subscribe to this mod's changes

json-data-extraction is a skill published in the GitHub repository cxcscmu/SkillLearnBench (88 stars, last pushed 17d ago), licensed MIT. It adds 18 tokens to every session and 881 once invoked, about $0.0001 per session on Opus 5.5. 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.

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