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/warroom-ceo/core/data-context-extractornpx skills add WARROOM-CEO/CORE --skill data-context-extractorgit clone --depth 1 https://github.com/WARROOM-CEO/COREWhat 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 | $0.00186 | $0.01734 |
| Opus 5 | $0.00093 | $0.00867 |
| Sonnet 5 | $0.00037 | $0.00347 |
| Haiku 4.5 | $0.00019 | $0.00173 |
Grade A, and why
data-context-extractor-th 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 2d 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.
This is a copy
89% identical to data-context-extractor — 13 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 229 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Language: All user-facing output — responses, summaries, and any text the user will read — must be written in Thai (ภาษาไทย). Internal logic, file paths, code snippets, and technical values remain in English.
Data Context Extractor
A meta-skill that extracts company-specific data knowledge from analysts and generates tailored data analysis skills.
How It Works
This skill has two modes:
- Bootstrap Mode: Create a new data analysis skill from scratch
- Iteration Mode: Improve an existing skill by adding domain-specific reference files
Bootstrap Mode
Use when: User wants to create a new data context skill for their warehouse.
Phase 1: Database Connection & Discovery
Step 1: Identify the database type
Ask: "What data warehouse are you using?"
Common options:
- BigQuery
- Snowflake
- PostgreSQL/Redshift
- Databricks
Use ~~data warehouse tools (query and schema) to connect. If unclear, check available MCP tools in the current session.
Step 2: Explore the schema
Use ~~data warehouse schema tools to:
- List available datasets/schemas
- Identify the most important tables (ask user: "Which 3-5 tables do analysts query most often?")
- Pull schema details for those key tables
Sample exploration queries by dialect:
-- BigQuery: List datasets
SELECT schema_name FROM INFORMATION_SCHEMA.SCHEMATA
-- BigQuery: List tables in a dataset
SELECT table_name FROM `project.dataset.INFORMATION_SCHEMA.TABLES`
-- Snowflake: List schemas
SHOW SCHEMAS IN DATABASE my_database
-- Snowflake: List tables
SHOW TABLES IN SCHEMA my_schema
Phase 2: Core Questions (Ask These)
After schema discovery, ask these questions conversationally (not all at once):
Entity Disambiguation (Critical)
"When people here say 'user' or 'customer', what exactly do they mean? Are there different types?"
Listen for:
- Multiple entity types (user vs account vs organization)
- Relationships between them (1:1, 1:many, many:many)
- Which ID fields link them together
What ships with it
6 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 2d ago First seen · 229 lines · 186 tokens per session scan A e4ecfbceece4
data-context-extractor-th is a skill published in the GitHub repository WARROOM-CEO/CORE (30 stars, last pushed 5mo ago), licensed MIT. It adds 186 tokens to every session and 1,734 once invoked, about $0.0009 per session on Opus 5. A static security scan graded it A with 0 findings. It is 89% identical to data-context-extractor, differing in 13 lines, and is treated as a copy.
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