enterprise-data-retrieval

enterprise-data-retrieval is a skill for Claude Code, Codex from cxcscmu/SkillLearnBench. It costs 15 tokens per session (1,074 once invoked), scanned A, original, MIT.

A method for finding and combining information from several company data sources, such as employee records, customer files, product data, and team information.

In plain words
What is it for?
It helps find project team members, authors, reviewers, URLs, and related records, then connect them using employee IDs or other shared information.
Why use it?
Relevant details are often spread across separate files, making it difficult to identify people, documents, projects, or shared resources in one search.

Skill for Claude CodeCodex

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

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/cxcscmu/skilllearnbench/enterprise-data-retrieval
Any agent
npx skills add cxcscmu/SkillLearnBench --skill enterprise-data-retrieval
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 enterprise-data-retrieval

README.md
[![agentmods](https://agentmods.dev/badge/skills/cxcscmu/skilllearnbench/enterprise-data-retrieval.svg)](https://agentmods.dev/skills/cxcscmu/skilllearnbench/enterprise-data-retrieval)
Your own site
<a href="https://agentmods.dev/skills/cxcscmu/skilllearnbench/enterprise-data-retrieval"><img src="https://agentmods.dev/badge/skills/cxcscmu/skilllearnbench/enterprise-data-retrieval.svg" alt="Measured on agentmods" height="20"></a>
Per session 15 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,074 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 5.1 $0.00015 $0.01074
Opus 5 $0.00008 $0.00537
Sonnet 5 $0.00003 $0.00215
Haiku 4.5 $0.00002 $0.00107

Measured 6d ago against content hash bfa6a6a7d777, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-06, from the pricing page.

Security

Grade A, and why

enterprise-data-retrieval 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 6d 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/enterprise-data-retrieval/SKILL.md · 174 lines

How it starts

The opening of the file, as written. The whole thing — 174 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Enterprise Data Retrieval

Overview

Techniques for finding specific information across multiple enterprise data files (employee records, product files, team information) and aggregating results.

Use Cases

  • Finding team members involved in specific projects/products
  • Identifying authors and reviewers of documents
  • Extracting URLs and resources shared in team communications
  • Cross-referencing employee IDs with their information
  • Building answer sets from multiple data sources

Directory Structure

/root/DATA/
├── metadata/
│   ├── employee.json          # Employee records with IDs and names
│   ├── customers_data.json    # Customer information
│   └── salesforce_team.json   # Sales team information
└── products/
    ├── ContentForce.json      # Product-specific data (Slack, docs, etc.)
    ├── SecurityForce.json     # Other products...
    └── ...

Code Examples

Load All Metadata

import json
import os

def load_metadata():
    """Load all metadata files"""
    metadata_path = '/root/DATA/metadata'
    metadata = {}

    for file in os.listdir(metadata_path):
        if file.endswith('.json') and not file.endswith(':Zone.Identifier'):
            with open(os.path.join(metadata_path, file), 'r') as f:
                metadata[file.replace('.json', '')] = json.load(f)

    return metadata

# Usage
metadata = load_metadata()
employees = metadata['employee']

Find Product Data

import json

def load_product_data(product_name):
    """Load product JSON data"""
    path = f'/root/DATA/products/{product_name}.json'
    with open(path, 'r') as f:
        return json.load(f)

# Usage
contentforce_data = load_product_data('ContentForce')

Identify Competitors and Resources

import json
import re

def find_competitor_mentions(product_data):
    """Find all mentions of competitor products"""
    competitors = {}

    messages = product_data.get('slack', [])
    for msg in messages:
        text = msg.get('Message', {}).get('text', '')

        # Look for competitor product mentions (heuristic: Force/Genie products)
        if 'demo' in text.lower() or 'url' in text.lower():
            # Extract URLs
            urls = re.findall(r'https?://[^\s\)]+', text)
            if urls:
                user_id = msg.get('Message', {}).get('User', {}).get('userId')
                competitors[user_id] = urls

    return competitors

Read the full file on GitHub · 174 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. 6d ago First seen · 174 lines · 15 tokens per session scan A bfa6a6a7d777

Subscribe to this mod's changes

enterprise-data-retrieval is a skill published in the GitHub repository cxcscmu/SkillLearnBench (83 stars, last pushed 1mo ago), licensed MIT. It adds 15 tokens to every session and 1,074 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 2026-08-30.

Related

Other skills, from other repositories

agentsop-crewai

SOP for building multi-agent systems with CrewAI — role-based collaboration, sequential/hierarchical processes, Flows, memory, delegation. Use when modeling agent teams with clear roles and task pipelines.

agentsope/SkillAlchemy · 45 tokens

agentsop-dify

SOP for building LLM applications on Dify — visual workflow + chatflow + agent + RAG knowledge base + plugin marketplace + observability, self-hostable. Use when shipping LLM apps fast with a "no-code to pro-code" gradient, especially when non-engineers need to co-author the flow.

agentsope/SkillAlchemy · 69 tokens

agentsop-dspy

Operating SOP for DSPy (Stanford NLP) — the declarative framework for "programming, not prompting" language models. Activate when the user says any of: "use DSPy", "compile a prompt", "optimize prompts/programs", "MIPRO/MIPROv2", "BootstrapFewShot", "GEPA", "Signatures + Modules", "teleprompter", "auto-tune prompts…

agentsope/SkillAlchemy · 157 tokens

agentsop-framework-selection

Neutral, framework-agnostic decision tree for project kickoff: "which agent / RAG / LLM framework should I reach for?" Synthesizes the ecosystem sections of 7 landmark-project SOPs (LangGraph, LlamaIndex, DSPy, CrewAI, vLLM, Aider, Dify) into one layered rubric. Core stance: frameworks are LAYERS, not competitors — a…

agentsope/SkillAlchemy · 189 tokens

agentsop-hybrid-retrieval

Enhancement-overlay SOP for adding sparse (BM25 / keyword) retrieval alongside dense (embedding) retrieval. Activate when a calling agent is building, reviewing, or debugging a retrieval pipeline whose corpus contains exact-match tokens — identifiers, error codes, SKUs, API/function names, proper nouns, citations…

agentsope/SkillAlchemy · 173 tokens

agentsop-idempotent-ingestion

Re-ingest-correctness SOP for production RAG. Activate when a calling agent builds, reviews, or debugs an ingestion pipeline that runs more than once over a changing corpus — scheduled re-index, incremental updates, CI re-ingest, or a "retrieval has duplicates / shows deleted docs" bug. Encodes the rule — ingestion…

agentsope/SkillAlchemy · 205 tokens