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 skills add cxcscmu/SkillLearnBench --skill json-data-extractiongit clone --depth 1 https://github.com/cxcscmu/SkillLearnBenchWrote 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/cxcscmu/skilllearnbench/json-data-extraction)<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.
<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>- NVIDIA SkillSpector pass
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 | $0.00018 | $0.00881 |
| Opus 5.5 | $0.00007 | $0.00352 |
| Sonnet 5.5 | $0.00004 | $0.00176 |
| Haiku 4.5 | $0.00002 | $0.00088 |
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.
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)
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.
- 1mo ago First seen · 121 lines · 18 tokens per session scan A 3cf75bdfb7ba
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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